- [tools as body extension]
-
Many of our physical tools, and even some of our institutional ones—like
the military—may be analogized, in some way or another,
as approximations or extensions,
of our body: the Hand, the Foot, the Fist, and so on. As we get closer
to recent times, some of them get closer to the head. Radio (and before it,
telephony, telegraphy, and such) is like the Ear. Television (and before
it cinematography, photography, microscopy, telescopy, painting, and so
on) is like the Eye. The computer, though, is like the Brain, and with
that all possibility of foretelling ends.
“During the mechanical ages we had extended our bodies in space. Today,
after more than a century of electric technology, we have extended our
central nervous system itself in a global embrace, abolishing both space
and time as far as our planet is concerned. Rapidly, we approach the
final phase of the extensions of man — the technological simulation
of consciousness, when the creative process will be collectively and
corporately extended to the whole of human society, much as we have
already extended our senses and our nerves by the various media.”
Understanding Media:
The Extensions of Man,
Marshall McLuhan,
McGraw-Hill, 1966, page 19.
See also:
“Technology and the Extension of Human Capabilities,”
C. Lawson,
Journal for the Theory of Social Behaviour,
40(2):207-223, 2010.
“Theories of Technology as Extension of the Human Body,”
P. Brey,
in:
Research in Philosophy and Technology,
Volume 19:
Metaphysics, Epistemology and Technology,
Carl Mitcham (editor),
JAI Press, 2000, pages 59-78.
- [brain emulators, scanners, implants to 2007]
-
For example, we’ve trained a ball of 25,000 rat neurons to fly a
(simulated) fighter plane. We’ve wired up monkey brains to control robot
bodies halfway around the planet. We’ve put pleasure-center implants
into rats to control their movements. We’ve learned how to remotely
control implanted sharks. Ditto for beetles. We’ve mated a silicon
neuron to 14 lobster neurons, and the lobster neurons can’t seem to
tell the difference. We’ve tapped into the brains of cats, seeing the
world much as they see it. We’ve begun to do the same for our own brains,
including partially predicting the content of dreams—but with scanners,
not implants—just as we’ve already put brain implants into patients who
suffer from Parkinson’s disease, chronic pain, quadriplegia, and other
problems. We’ve also put in brain implants for the deaf, and are beginning
to do the same for the blind. We’ve read live rat thoughts as they move
through a maze for simple yes or no decisions, and put rat brains into
robots.
Rat brain flying a jet:
It learned to fly an F-22 Raptor flight simulator.
“Adaptive flight control with living neuronal networks on microelectrode
arrays,”
T. B. DeMarse, K. P. Dockendorf,
Proceedings of the 2005 IEEE International Journal of Computation
and Neural Networks,
pages 1548-1551, 2005.
Monkeys control robots:
“Brain controlled robots,
M. Kawato,
HFSP Journal,
2(3):136-142, 2008.
“Bipedal locomotion with a humanoid robot controlled by cortical
ensemble activity,”
G. Cheng, N. A. Fitzsimmons, J. Morimoto, M. A. Lebedev, M. Kawato,
M. A. Nicolelis,
37th Annual Meeting of the Society for Neuroscience,
San Diego, California, 2007.
“Learning to Control a Brain-Machine Interface for Reaching and Grasping
by Primates.”
J. M. Carmena, M. A. Lebedev, R. E. Crist, J. E. O’Doherty,
D. M. Santucci, D. F. Dimitrov, P. G. Patil, C. S. Henriquez,
M. A. L. Nicolelis,
Public Library of Science, Biology,
1(2):193-208, 2003.
Robotic rats, sharks, and beetles:
“Radio-controlled cyborg beetles:
a radio-frequency systems for insect neural flight control,”
H. Sato, Y. Peeri, E. Baghoomian, C. W. Berry, M. M. Maharbiz,
IEEE Micro Electro Mechanical Systems, (MEMS 2009),
Sorrento, Italy, January 25-29, 2009.
“Autonomous Shark Tag with Neural Reading and Stimulation Capability for
Open-ocean Experiments,”
W. J. Gomes, III, D. Perez, Jr., J. A. Catipovic,
plus
“Steering sharks with odor plume information,”
J. M. Gardiner, D. V. Dale, S. Patell, J. Atema,
both posters were presented in:
Eos, Transactions, American Geophysical Union
87(36),
Ocean Sciences Meeting Supplement, Abstract OS24I-06, 2006.
“A multi-channel telemetry system for brain microstimulation in freely
roaming animals,”
S. Xu, S. K. Talwar, E. S. Hawley, L. Li, J. K. Chapin,
Journal of Neuroscience methods,
133(1-2):57-63, 2004.
See also:
Physical Control of the Mind:
Toward a Psychocivilized Society,
Joé M. R. Delgado,
Harper and Row, 1969.
Seeing what cats see:
“Encoding of natural scene movies by tonic and burst spikes in the
lateral geniculate nucleus,”
N. A. Lesica, G. B. Stanley,
Journal of Neuroscience,
24(47):10731-10740, 2004
“Reconstruction of natural scenes from ensemble responses in the lateral
geniculate nucleus,”
G. B. Stanley, F. F. Li, Y. Dan,
Journal of Neuroscience,
19(18):8036-8042, 1999.
Reading rat brains and simulating rats:
“Feature Detection in Motor Cortical Spikes by Principal Component Analysis,”
J. Hu, J. Si, B. P. Olson, J. He,
IEEE Transactions on Neural Systems and Rehabilitation Engineering,
13(3):256-262, 2005.
“The Neurally Controlled Animat:
Biological Brains Acting with Simulated Bodies,”
T. B. DeMarse, D. A. Wagenaar, A. W. Blau, S. M. Potter,
Autonomous Robots,
11(3):305-310, 2001.
Reading human brains:
The research relates to reading what image the human visual attention
mechanism is focused on, not yet the thoughts of human subjects about
those images.
“Identifying natural images from human brain activity,”
K. N. Kay, T. Naselaris, R. J. Prenger, J. L. Gallant,
Nature,
452(7185):352-355, 2008.
More recently, another team was able to decipher images solely by doing
computations on fMRI images of human brains.
“Visual Image Reconstruction from Human Brain Activity using a Combination
of Multiscale Local Image Decoders,”
Y. Miyawaki, H. Uchida, O. Yamashita, M. Sato,
Y. Morito, H. C. Tanabe, N. Sadato, Y. Kamitani,
Neuron,
60(5):915-929, 2008.
Such results are surprising only at first glance. We already know the
primary visual cortex in primates performs a conformal map of retinal
input (it uses the complex logarithm map and so is conformal because it
preserves angles locally but distorts shapes at larger scales). Thus,
to a first approximation, there is a simple mapping from visual input
to visual representation, at least as far into the brain as the primary
visual cortex.
“What Geometric Visual Hallucinations Tell Us about the Visual Cortex,”
P. C. Bressloff, J. D. Cowan, M. Golubitsky, P. J. Thomas, M. C. Wiener,
Neural Computation,
14(3):473-491, 2002.
What happens beyond V1 is at present unknown.
Reading human dream images:
The researchers used fMRI scans to predict the content
of hypnaogic hallucinations preceding sleep of three subjects
60 percent of the time.
“Visual imagery during sleep has long been a topic of persistent
speculation, but its private nature has hampered objective analysis. Here
we present a neural decoding approach in which machine-learning models
predict the contents of visual imagery during the sleep-onset period, given
measured brain activity, by discovering links between human functional
magnetic resonance imaging patterns and verbal reports with the assistance
of lexical and image databases. Decoding models trained on stimulus-induced
brain activity in visual cortical areas showed accurate classification,
detection, and identification of contents. Our findings demonstrate that
specific visual experience during sleep is represented by brain activity
patterns shared by stimulus perception, providing a means to uncover
subjective contents of dreaming using objective neural measurement.”
From:
“Neural decoding of visual imagery during sleep,”
T. Horikawa, M. Tamaki, Y. Miyawaki, Y. Kamitani,
Science,
340(6132):639-642, 2013.
Human brain-machine interfaces:
“Toward a whole-body neuroprosthetic,”
M. A. Lebedev, M. A. Nicolelis,
Progress in Brain Research,
194:47-60, 2011.
“Cortical activity during motor execution, motor imagery, and imagery-based
online feedback,”
K. J. Miller, G. Schalk, E. E. Fetz, M. den Nijs, J. G. Ojemann, R. P. N. Rao,
Proceedings of the National Academy of Sciences,
107(9):4430-4435, 2010.
“When mind meets machine:
A new wave of brain-machine interfaces helps disabled people connect
with the outside world,”
V. Brower,
EMBO Reports,
6(2):108-110, 2005.
“Brain-Computer Interfaces:
Prospects for Neuro-Rehabilitation and Implications for Neurocritical
Care,”
L. R. Hochberg, J. A. Mukand, S. Williams, G. Polykoff, G. M. Friehs,
J. P. Donoghue,
Third Annual Meeting of the
Neurocritical Care Society, Scottsdale, February 2005.
“Control of a two-dimensional movement signal by a noninvasive
brain-computer interface in humans,”
J. R. Wolpaw, D. J. McFarland,
Proceedings of the National Academy of Sciences,
101(51):17849-17854, 2004.
“Connecting cortex to machines:
recent advances in brain interfaces,”
J. P. Donoghue,
Nature Neuroscience,
5(Supplement):1085-1088, 2002.
Neural prostheses for restoration of sensory and motor function,
John K. Chapin and Karen A. Moxon (editors),
CRC Press, 2001.
Quadriplegic brain-to-computer implant:
“The Cone Electrode:
Ultrastructural Studies
Following Long-Term Recording in Rat and Monkey Cortex,”
P. R. Kennedy, S. S. Mirra, R. A. E. Bakay,
Neuroscience Letters,
142(1):89-94, 1992.
“Behavioral Correlates of Action Potentials
Recorded Chronically Inside the Cone Electrode,”
P. R. Kennedy, R. A. E. Bakay, S. M. Sharpe,
NeuroReport,
3(7):605-608, 1992.
“The Cone Electrode:
a Long-Term Electrode that Records from
Neurites Grown onto its Recording Surface,”
P. R. Kennedy,
Journal of Neuroscience Methods,
29(3):181-193, 1989.
Paraplegic implants and exoskeletons:
Reports of tests with multi-electrode implants (in monkeys) are here:
“Instant Neural Control of a Movement Signal,”
M. D. Serruya, N. G. Hatsopoulos, L. Paninski, M. R. Fellows, J. P.
Donoghue,
Nature,
416(6877):141-2, 2002.
Brain pacemakers and electromyographic amplifiers for paraplegics,
quadriplegics, and amputees are growing. Many are called Functional
Electro-Stimulation (FES) systems. One company producing such devices
is Cyberkinetics. The ReWalk from Argo Medical Technologies, is a
computerized exoskeleton (or orthotic device) that lets paraplegics walk
again, and without surgery. Another exoskeleton from Kanagawa Institute
of Technology lets non-paraplegics lift heavy weights. There are now
several new assistive technologies.
Implants for Parkinson’s disease and others:
“Deep Brain Stimulation of the Ventral Capsule/Ventral Striatum for
Treatment-Resistant Depression,”
D. A. Malone Jr., D. D. Dougherty, A. R. Rezai, L. L. Carpenter,
G. M. Friehs, E. N. Eskandar, S. L. Rauch, S. A. Rasmussen,
A. G. Machado, C. S. Kubu, A. R. Tyrka,
L. H. Price, P. H. Stypulkowski, J. E. Giftakis, M. T. Rise,
P. F. Malloy, S. P. Salloway, B. D. Greenberg,
Biological Psychiatry,
65(4):267-275, 2009.
“Deep brain stimulation for parkinsonian gait disorders,”
A. M. Lozano, B. J. Snyder,
Journal of Neurology,
255(Supplement 4):30-1, 2008.
“Subcallosal cingulate gyrus deep brain stimulation for treatment-resistant
depression,”
A. M. Lozano, H. S. Mayberg, P. Giacobbe, C. Hamani, R. C. Craddock,
S. H. Kennedy,
Biological psychiatry,
64(6):461-467, 2008.
“Evolution of neuromodulation,”
P. J. Gildenberg,
Stereotactic and Functional Neurosurgery,
83(2-3):71-79, 2005.
Artificial ears and eyes:
Artificial cochleas have existed for decades. Artificial eyes have also
existed for decades, but have been much harder to bring up to near-human
performance.
“Retinal prosthetic strategy with the capacity to restore normal vision,”
S. Nirenberg, C. Pandarinath,
Proceedings of the National Academy of Sciences,
109(37):15012-15017, 2012.
“Artificial vision:
needs, functioning, and testing of a retinal electronic prosthesis,”
G. J. Chader, J. Weiland, M. S. Humayun,
Progress in Brain Research,
175:317-332, 2009.
“Current and future prospects for optoelectronic retinal prostheses,”
J. Dowling,
Eye,
23(10):1999-2005, 2009.
“A silicon retina that reproduces signals in the optic nerve,”
K. A. Zaghloul, K. Boahen,
Journal of Neural Engineering,
3(4):257-267, 2006.
“Pseudo-Voltage-Domain Implementation of a Design of a 2-Dimensional
Silicon Cochlea,”
A. van Schaik, E. Fragnière,
Proceedings of the 2001 IEEE International Symposium on Circuits
and Systems,
pages 185-188, 2001.
“Improved implementation of the silicon cochlea,”
IEEE Journal of Solid-State Circuits,
L. Watts, D. A. Kerns, R. F. Lyon, C. A. Mead,
27(5):692-700, 1992.
artificial lobster neurons:
“Interacting biological and electronic neurons generate realistic
oscillatory rhythms,”
A. Szücs, P. Varona, A. R. Volkovskii, H. D. I. Abarbanel,
M. I. Rabinovich, A. I. Selverston,
Neuroreport,
11(3):563-569, 2000.
“Lobster Robots,”
J. Ayers, J. Witting, N. McGruer, C. Olcott, D. Massa,
Proceedings of the International Symposium on Aqua Biomechanisms,
T. Wu and N. Kato (editors),
Tokai University, 2000.
More generally, see:
“Understanding circuit dynamics using the stomatogastric nervous system of
lobsters and crabs,”
E. Marder, D. Bucher,
Annual Review of Physiology,
69:291-316, 2007.
- [synthetic mouse cortex at seven seconds per second]
-
In 2007 we simulated part of a mouse’s brain in a supercomputer.
The modeling was very crude. It assumes point neurons (thus missing
99.999 percent of the complexity of the real mouse brain), and those
neurons have no branches, and no detailed ion channels. Essentially it
assumes a fairly uniform field of ion channels and synaptic connections,
something we know isn’t the case in real brains. It’s more like a really
big and flat neural network than a true brain model.
“Toward Real-Time, Mouse-Scale, Cortical Simulations”
J. Frye, R. Ananthanarayanan, D. S. Modha,
IBM Research Report RJ10404 (A0702), February 2007.
Computational and Systems Neuroscience,
Salt Lake City, February 22-25, 2007.
“Scaling, Stability, and Synchronization in Mouse-sized (and Larger)
Cortical Simulations,”
R. Ananthanarayanan, D. S. Modha,
BMC Neuroscience,
8(Supplement 2):P187, 2007.
The same researchers then did a rat-scale brain (55 million neurons).
It took nine seconds to do one second of rat simulation.
“Anatomy of a Cortical Simulator,”
R. Ananthanarayanan, D. S. Modha,
International Conference on High Performance Computing, Networking,
Storage, and Analysis,
Reno, November 10-16, 2007.
Then they did a billion neurons, which is about twice as much as in
a cat’s brain.
“The Cat is Out of the Bag:
Cortical Simulations with 109, 1013 synapses,”
R. Ananthanarayanan, S. K. Esser, H. D. Simon, D. S. Modha,
Proceedings of the Conference on High Performance Computing Networking,
Storage and Analysis,
Portland, Oregon, pages 1-12, 2009.
But to call this a cat’s brain would be misleading. The neurons are
still highly simplified—no more than a soma containing a nucleus and
a simplified spiking model. It also runs 100 to 1,000 times slower than
a cat’s brain.
Another supercomputer simulation of rat brains is in a different class
altogether. It’s aiming for near-total biological realism. It runs in
real time but only simulates about 10,000 baby rat neurons. However,
each neuron is simulated to be made up of up to 1,000 compartments.
Those neurons form a cortical column, the building block of mammalian
brains. Our cortical columns have about 50,000 neurons and our synapse
count per neuron is larger but is otherwise about the same. And
now the simulation is being extended to multiple columns, and so on up.
“Out of the Blue,”
J. Lehrer,
Seed Magazine,
March 3rd, 2008.
“The Blue Brain Project,”
H. Markram,
Nature Neuroscience Review,
7(2):153-160, 2006.
Yet another group has pushed its supercomputer brain simulation out to
22 million neurons. This simulation began to display characteristics
of real brains but it also ran over a thousand times slower than real
brains.
“Brain-scale simulation of the neocortex on the IBM Blue Gene/L
supercomputer,”
M. Djurfeldt, M. Lundqvist, C. Johansson, M. Rehn,
Ö. Ekeberg, A. Lansner,
IBM Journal of Research and Development,
52(1/2):31-41, 2008.
None of these simulations connect the brain to anything, so there’s as yet
no sensory input nor motor output. It’s difficult then to say what, if
anything, such an isolated brain-in-a-box can be capable of.
To put all these simulations in context, see:
Connectome:
How the Brain’s Wiring Makes Us Who We Are,
Sebastian Seung,
Houghton Mifflin Harcourt, 2012, pages 263-268.
“The tops in flops,”
P. Kogge,
IEEE Spectrum,
48(2):48-54, 2011.
Yet another group is building brain-like hardware, rather than attempting
to simulate, either at a crude level or a low level, in software on
general-purpose supercomputers. Again the neural model is crude; in this
case it assumes that it’s a good approximation to divide each neuron
into only two compartments. This work in particular promises to replace
a one-megawatt, 500-teraflop supercomputer with one that consumes less
than one watt and costs about a thousand times less.
“Neurogrid:
A Mixed-Analog-Digital Multichip System for Large-Scale Neural
Simulations,”
B. V. Benjamin, P. Gao, E. Mcquinn, S. Choudhary, A. R. Chandrasekaran,
J.-M. Bussat, R. Alvarez-Icaza, J. V. Arthur, P. A. Merolla, K. Boahen,
Proceedings of the IEEE,
102(5):699-716, 2014.
“Neurotech for Neuroscience:
Unifying Concepts, Organizing Principles, and Emerging Tools,”
R. Silver, K. Boahen, S. Grillner, N. Kopell, K. L. Olsen,
The Journal of Neuroscience,
27(44):11807-11819, 2007.
“Neurogrid:
Emulating a Million Neurons in the Cortex,”
K. Boahen,
28th Annual International Conference of the IEEE on Engineering in
Medicine and Biology Society,
pages 6702-6702, 2006.
“Neuronal Ion-Channel Dynamics in Silicon,”
K. M. Hynna, K. Boahen,
Proceedings of the 2006 IEEE International Symposium on Circuits
and Systems,
2006.
A similar project has built a mixed analog/digital VLSI 8-inch
silicon wafer modelling 200 thousand neurons and 50 million synapses.
The project ended in 2010 and has since been folded into the Human Brain
Project. The goal is to build and connect 20 wafers, corresponding to
4 million neurons and 1 billion synapses operating at an acceleration
factor of 10,000 (compared to biological real time).
“The Human Brain Project and neuromorphic computing,”
A. Calimera, E. Macii, M. Poncino,
Functional Neurology,
28(3):191-196, 2013.
And another project, the DARPA SyNAPSE, has built a scalable 1 million
neuron, 256 million synapse chip.
“A million spiking-neuron integrated circuit with a scalable communication
network and interface,”
P. A. Merolla, J. V. Arthur, R. Alvarez-Icaza, A. S. Cassidy, J. Sawada,
F. Akopyan, B. L. Jackson, N. Imam, C. Guo, Y. Nakamura, B. Brezzo, I. Vo,
S. K. Esser, R. Appuswamy, B. Taba, A. Amir, M. D. Flickner, W. P. Risk,
R. Manohar, D. S. Modha,
Science,
345(6197):668-673, 2014.
- [the Blue Brain project 2015 result]
-
The project needed a
supercomputer running a billion computations every 25 microseconds.
“Reconstruction and Simulation of Neocortical Microcircuitry,”
H. Markram, E. Muller, S. Ramaswamy, M. W. Reimann, M. Abdellah,
C. A. Sanchez, A. Ailamaki, L. Alonso-Nanclares, N. Antille, S. Arsever,
G. A. A. Kahou, T. K. Berger, A. Bilgili, N. Buncic, A. Chalimourda,
G. Chindemi, J.-D. Courcol, F. Delalondre, V. Delattre, S. Druckmann,
R. Dumusc, J. Dynes, S. Eilemann, E. Gal, Michael E. Gevaert, J.-P. Ghobril,
A. Gidon, J. W. Graham, A. Gupta, V. Haenel, E. Hay, T. Heinis,
J. B. Hernando, M. Hines, L. Kanari, D. Keller, J. Kenyon, G. Khazen, Y. Kim,
J. G. King, Z. Kisvarday, P. Kumbhar, S. Lasserre, J.-V. Le Bé
B. R. C. Magalhães, A. Merchán-Pérez, J. Meystre,
B. R. Morrice, J. Muller, A. Muñoz-Céspedes, S. Muralidhar,
K. Muthurasa, D. Nachbaur, T. H. Newton, M. Nolte, A. Ovcharenko, J. Palacios,
L. Pastor, R. Perin, R. Ranjan, I. Riachi, J.-R. Rodríguez,
J. L. Riquelme, C. Rössert, K. Sfyrakis, Y. Shi, J. C. Shillcock,
G. Silberberg, R. Silva, F. Tauheed, M. Telefont, M. Toledo-Rodriguez,
T. Tränkler, W. Van Geit, J. V. Díaz, R. Walker, Y. Wang,
S. M. Zaninetta, J. DeFelipe, S. L. Hill, I. Segev, F. Schürmann,
Cell,
163(2):456-492, 2015.
- [brain scanners]
-
Just scanning brains itself costs serious money, and takes serious time.
A chunk of brain tissue about the size of a coarse grain of sand (a cubic
millimeter), has about 50,000 brain cells,
with perhaps half a billion junctions between them, with hundreds of cell
types, spread over many different layers of brain tissue, each with its
own job. Scanning that grain means making 33,333 slices of brain tissue
at a thickness of a thousandth the width of a human hair (29 nanometers),
and creating enough data to fill 2,000 terabytes. It would take 6 months.
But one grain is only about one thousandth of a mouse brain, and about
a millionth of a human brain.
So scanning one mouse brain this way might take over 500 years,
and one human brain might take over half a million years.
In 2021 there are several brain scan projects at present. In the United States,
R. Clay Reid, at the Allen Institute for Brain Science, is trying to
reverse-engineer one cubic millimeter of the mouse brain by recording
the activity and connectivity of 100,000 neurons of the animal that had
completed visual perception and learning tasks. Reid is part of a team
of researchers, including H. Sebastian Seung at Princeton University
and others from the Allen Institute and Baylor College of Medicine.
IARPA (the Intelligence Advanced Research Projects Activity) has that team
and two other teams in its MICrONS (Machine Intelligence from Cortical
Networks) project as well. Jeff Lichtman at Harvard University will
participate with a Harvard University team led by David Cox. Another
team includes researchers from Carnegie Mellon University and the Wyss
Institute for Biologically Inspired Engineering at Harvard University.
“The value of an integrated approach for understanding the neocortex
by combining functional characterization of single neuron activity with
the underlying circuit architecture has been understood since the dawn
of modern neuroscience. However, in practice, anatomical connectivity and
physiology have been studied mostly separately. Following in the footsteps
of previous studies that have combined physiology and anatomy in the same
tissue, here we present a unique functional connectomics dataset that
contains calcium imaging of an estimated 75,000 neurons from primary visual
cortex (VISp) and three higher visual areas (VISrl, VISal and VISlm), that
were recorded while a mouse viewed natural movies and parametric stimuli.
The functional data were co-registered with electron microscopy (EM) data
of the same volume which were automatically segmented, reconstructing more
than 200,000 cells (neuronal and non-neuronal) and 524 million synapses.
Subsequent proofreading of some neurons in this volume yielded
reconstructions that include complete dendritic trees as well the local and
inter-areal axonal projections. The largest proofread excitatory axon
reached a length of 19 mm and formed 1893 synapses, while the largest
inhibitory axon formed 10081 synapses. Here we release this dataset as an
open access resource to the scientific community including a set of
analysis tools that allows easy data access, both programmatically and
through a web user interface.”
From:
“Functional connectomics spanning multiple areas of mouse visual cortex,”
MICrONS Consortium, J. A. Bae, M. Baptiste,
A. L. Bodor, D. Brittain, J. Buchanan, D. J. Bumbarger, M. A. Castro,
B. Celii, E. Cobos, F. Collman, N. Maçarico da Costa,
S. Dorkenwald, L. Elabbady, P. G. Fahey, T. Fliss, E. Froudarakis,
J. Gager, C. Gamlin, A. Halageri, J. Hebditch, Z. Jia, C. Jordan,
D. Kapner, N. Kemnitz, S. Kinn, S. Koolman, K. Kuehner, K. Lee, K. Li,
R. Lu, T. Macrina, G. Mahalingam, S. McReynolds, E. Miranda, E. Mitchell,
S. S. Mondal, M. Moore, S. Mu, T. Muhammad, B. Nehoran, O. Ogedengbe,
C. Papadopoulos, S. Papadopoulos, S. Patel, X. Pitkow, S. Popovych,
A. Ramos, R. C. Reid, J. Reimer, C. M. Schneider-Mizell, H. S. Seung,
B. Silverman, W. Silversmith, A. Sterling, F. H. Sinz, C. L. Smith,
S. Suckow, M. Takeno, Z. H. Tan, A. S. Tolias, R. Torres, N. L. Turner,
E. Y. Walker, T. Wang, G. Williams, S. Williams, K. Willie, R. Willie,
W. Wong, J. Wu, C. Xu, R. Yang, D. Yatsenko, F. Ye, W. Yin, S.-C. Yu,
bioRxiv preprint,
doi: https://doi.org/10.1101/2021.07.28.454025
2021.
See also:
“Generative models and abstractions for large-scale neuroanatomy
datasets,”
D. Rolnick, E. L. Dyer,
Current Opinion in Neurobiology,
55:112-120, 2019.
“Serial-section Electron Microscopy Using Automated Tape-Collecting
Ultramicrotome (ATUM),”
V. Baena, R. L. Schalek, J. W. Lichtman, M. Terasaki,
in:
Methods in Cell Biology:
Three-Dimensional Electron Microscopy,
Volume 152,
Thomas Müller-Reichert and Gaia Pigino (editors),
Academic Press, pages 41-67, 2019
“Learning cellular morphology with neural networks,”
P. J. Schubert, S. Dorkenwald, M. Januszewski, V. Jain, J. Kornfeld,
Nature Communications,
10(1):2736, 2019.
“High-Precision Automated Reconstruction of Neurons with Flood-filling
Networks,”
M. Januszewski, J. Kornfeld, P. H. Li, A. Pope, T. Blakely, L. Lindsey,
J. Maitin-Shepard, M. Tyka, W. Denk, V. Jain,
Nature Methods,
15(8):605-610, 2018.
“Progress and remaining challenges in high-throughput volume electron
microscopy,”
J. Kornfeld, W. Denk,
Current Opinion in Neurobiology,
50:261-267, 2018.
“Large Volume Electron Microscopy and Neural Microcircuit Analysis,”
Y. Kubota, J. Sohn, Y. Kawaguchi,
Frontiers in Neural Circuits,
12:98, 2018.
“Saturated Reconstruction of a Volume of Neocortex,”
N. Kasthuri, K. J. Hayworth, D. R. Berger, R. L. Schalek, J. A. Conchello,
S. Knowles-Barley, D. Lee, A. Vázquez-Reina, V. Kaynig, T. R. Jones,
M. Roberts, J. L. Morgan, J. C. Tapia, H. S. Seung, W. G. Roncal,
J. T. Vogelstein, R. Burns, D. L. Sussman, C. E. Priebe, H. Pfister,
J. W. Lichtman,
Cell,
162(3):648-661, 2015.
- [fruit fly brain size]
-
This was done using a new technique (different from the ultramicrotomy of
the mouse brain scanning).
The fruit fly brain,
roughly the size of a poppy seed,
has a three-dimensional volume of 8 x 107 cubic micrometers.
The fly brain tissue was stained completely with uranium and lead and
sectioned into much thicker slices (7,062 20-micrometer thick slabs).
Each slab was then imaged at 8x8x8 cubic nanometer voxel-resolution
using automated focused ion beam scanning electron microscopes.
Then those images were reconstructed and colored into full 3D
using machine learning. Then they were proofread (for 2 years) by experts
to verify the flood-filling algorithm accurately reconstructed the cells.
“A complete electron microscopy volume of the brain of adult Drosophila
melanogaster,”
Z. Zheng, J. S. Lauritzen, E. Perlman, C. G. Robinson,
M. Nichols, D. Milkie, O. Torrens, J. Price, C. B. Fisher,
N. Sharifi, S. A. Calle-Schuler, L. Kmecova, I. J. Ali,
W. Karsh, E. T. Trautman, J. Bogovic, P. Hanslovsky, G. S. X. E. Jefferis,
M. Kazhdan, K. Khairy, S. Saalfeld, R. D. Fetter, D. D. Bock,
Cell,
174(3):730-743, 2018.
“An Image Recognition Algorithm for Automatic Counting of Brain Cells of
Fruit Fly,”
T. Shimada, K. Kato, K. Ito,
in:
Computer Simulation Studies in Condensed-Matter Physics XVII,
D. P. Landau, S. P. Lewis, and H.-B. Shüttler (editors),
Springer-Verlag, 2006.
- [size of some cortices]
-
Fruit Fly: 135,000.
Mouse: 71 million.
Hamster: 90 million.
Rat: 200 million.
Guinea Pig: 240 million.
Marmoset: 634 million.
Rhesus Macaque: 6.376 billion.
Human: 86 billion.
“The Brain Activity Map Project
and the Challenge of Functional Connectomics,”
A. P. Alivisatos, M. Chun, G. M. Church, R. J. Greenspan,
M. L. Roukes, R. Yuste,
Neuron,
74(6):970-974, 2012.
“The human brain in numbers:
a linearly scaled-up primate brain,”
S. Herculano-Houzel,
Frontiers in Human Neuroscience,
3:31, 2009.
See also:
“The remarkable, yet not extraordinary, human brain as a scaled-up
primate brain and its associated cost,”
S. Herculano-Houzel,
Proceedings of the National Academy of Sciences,
109(Supplement_1):10661-10668, 2012.
“Not all brains are made the same:
new views on brain scaling in evolution,”
S. Herculano-Houzel,
Brain, behavior and evolution,
78(1):22-36, 2011.
“Equal numbers of neuronal and nonneuronal cells make the human brain an
isometrically scaled-up primate brain,”
F. A. Azevedo, L. R. Carvalho, L. T. Grinberg, J. M. Farfel,
R. E. Ferretti, R. E. Leite, W. J. Filho, R. Lent, S. Herculano-Houzel,
The Journal of Comparative Neurology,
513(5):532-541, 2009.
As of 2009, microprocessors packed about 10 thousand million transistors
into a square centimeter. The human cortex packs in about 100 thousand
million synapses into the same space. And that doesn’t count all the
space consumed in today’s computers by air and wire.
- [size of worm and insect cortices and lamprey spine]
-
Assuming we do the C. elegans worm (302 neurons),
we might try for the leech (around 13,000 neurons),
then various brains: the fruit fly (around 135,000), the cockroach (around
a million), the rat (around 200 million), the macaque (over six billion),
the human (86 billion). However if do we get that far, why not press on?
We now know a lot about the neurons that control walking in stick
insects, swimming in leeches, flying in locusts, feeding in mollusks,
digesting in crabs and lobsters.... We’ve picked apart the neurons
that process the visual changes prompting flies to turn, the courtship
sounds attracting crickets and grasshoppers, the smells enticing bees to
extend their proboscis.... They’re all just complex call-and-response
machines. There’s no clear reason to believe that our brain isn’t similar,
except even more complex.
However, making a brain of complexity at least equal to one of ours
isn’t like making a plane or submarine—or heart or liver. It’s more
like making a baby. But we don’t make our babies any more than we make
our food—we run the bodies that make our babies. And those bodies
result from millions of years of intricate engineering.
How we make our babies will long remain a mystery. Ditto for our brains.
The leech has about 13,000 neurons. We know a lot about it as
well. But only now are we finding out just how subtly its neurons control
its body (and the reverse). A fruit fly, which has 135,000 neurons,
is still more complex. A termite, which has around 200,000 neurons,
is yet more complex. A termite colony of about a million termites is
even more complex. Understanding how it behaves illustrates the problem
well. We can trace all the tunnels in a termite nest, but we’d still have
little idea how termite traffic in those tunnels would respond to all
events. It’s the same for our brain. Even after we map all its roads,
we won’t necessarily know how its traffic flows.
Note that none of that means that we can’t simulate large networks
for special purposes; it merely means that we don’t know all the
parameters—either those that are internal to it or those imposed
on it from its surroundings. Thus, after decades of study, we can now
mimic the around 50,000 neurons in the lamprey’s spine with around 3,000
simulated excitatory interneurons. With that model we’ve built a robot
that swims like a lamprey. However, it can’t smell, mate, lay eggs,
or do anything else that lampreys can do.
“Modeling a vertebrate motor system:
pattern generation, steering and control of body orientation,”
S. Grillner, A. Kozlov, P. Dario, C. Stefanini, A. Menciassi, A. Lansner,
J. Hellgren Kotaleski,
Progress in Brain Research,
165:221-234, 2007.
“Tectal Control of Locomotion, Steering, and Eye Movements in Lamprey,”
K. Saitoh, A. Ménard, S. Grillner,
Journal of Neurophysiology,
97(4):3093-3108, 2007.
“Neurotech for Neuroscience:
Unifying Concepts, Organizing Principles, and Emerging Tools,”
R. Silver, K. Boahen, S. Grillner, N. Kopell, K. L. Olsen,
Journal of Neuroscience,
27(44):11807-11819, 2007.
- [the leech nervous system]
-
“Neuronal Decision-Making Circuits,”
W. B. Kristan, Jr.,
Current Biology,
18(19):R928-R932, 2008.
“Multifunctional pattern generating circuits,”
K. L. Briggman, W. B. Kristan, Jr.,
Annual Review of Neuroscience,
31:271-294, 2008.
“Leech locomotion:
swimming, crawling, and decisions,”
W. O. Friesen, W. B. Kristan, Jr.,
Current Opinion in Neurobiology,
17(6):704-711, 2007.
“Optical imaging of neuronal populations during decision-making,”
K. L. Briggman, H. D. I. Abarbanel, W. B. Kristan, Jr.,
Science,
307(5711):896-901, 2005.
- [the crab nervous system]
-
“When does neuromodulation of a single neuron influence the output
of the entire network? We constructed a five-cell circuit in which a
neuron is at the center of the circuit and the remaining neurons form two
distinct oscillatory subnetworks. All neurons were modeled as modified
Morris−Lecar models with a hyperpolarization-activated conductance
(ḡh) in addition to calcium (ḡCa),
potassium (ḡK), and leak conductances. We determined
the effects of varying ḡCa, ḡK,
and ḡh on the frequency, amplitude, and duty cycle
of a single neuron oscillator. The frequency of the single neuron
was highest when the ḡK and ḡh
conductances were high and ḡCa was moderate whereas,
in the traditional Morris−Lecar model, the highest frequencies occur
when both ḡK and ḡCa are high.
We randomly sampled parameter space to find 143 hub oscillators with
nearly identical frequencies but with disparate maximal conductance,
duty cycles, and burst amplitudes, and then embedded each of these hub
neurons into networks with different sets of synaptic parameters. For
one set of network parameters, circuit behavior was virtually identical
regardless of the underlying conductances of the hub neuron. For a
different set of network parameters, circuit behavior varied with
the maximal conductances of the hub neuron. This demonstrates that
neuromodulation of a single target neuron may dramatically alter the
performance of an entire network when the network is in one state,
but have almost no effect when the circuit is in a
different state.”
From:
“Modulation of a Single Neuron Has State-Dependent Actions on Circuit
Dynamics,”
G. J. Gutierrez, E. Marder,
eNeuro,
1(1) ENEURO.0009-14, 2014.
See also:
“Multiple mechanisms switch an electrically coupled,
synaptically inhibited neuron between competing rhythmic oscillators,”
G. J. Gutierrez, T. O’Leary, E. Marder,
Neuron,
77(5):845-858, 2013.
- [the brain as computer metaphor]
-
The text isn’t intended to imply that; the problem is the lack of an
abstract word for what neurons do, so ’computation’ is used.
Throughout the centuries, the brain has been analogized to whatever we best
understood at the time: hydraulics in the time of Descartes, the telegraph
in the time of Helmholtz, the computer in the time of von Neumann.
- [computer prices fell about a trillionfold from 1940 to 2012]
-
“The costs of a standard computation have declined at an average annual
rate of 53% per year over the period 1940-2012. There may have been a
slowing in the speed of chip computations over the last decade, but the
growth in parallel, cloud, and high-performance clusters as well as
improvements in software appear to have offset that for many applications.”
From:
“Are We Approaching an Economic Singularity?
Information Technology and the Future of Economic Growth,”
W. D. Nordhaus,
Discussion Paper No. 2021,
Cowles Foundation for Research in Economics,
Yale University, 2015.
See also:
“Implications in recent trends in performance, costs, and energy use
for servers,”
J. G. Koomey, C. Belady, M. Patterson, A. Santos, K.-D. Lange,
in:
The Green Computing Book:
Tackling Energy Efficiency at Large Scale,
Wu-chun Feng (editor),
CRC Press, 2014, pages 297-320.
“Assessing trends in the electrical efficiency of computation over time,”
J. G. Koomey, S. Berard, M. Sanchez, H. Wong,
IEEE Annals of the History of Computing,
33(3):46-54, 2011.
“Depending upon the standard used, computer performance has improved since
manual computing by a factor between 1.7 trillion and 76 trillion.
Second, there was a major break in the trend around World War II.”
From:
“Two centuries of productivity growth in computing,”
W. D. Nordhaus,
The Journal of Economic History,
67(1):128-159, 2007.
“Since 1900 there has been a trillionfold increase in the amount of
computation a dollar will buy.”
Mind Children:
The Future of Robot and Human Intelligence,
Hans Moravec,
Harvard University Press, 1988, pages 64-65.
In 1997, a $2 million U.S. supercomputer beat the world chess champion. A
decade later, the parts for roughly the same machine cost just $1,300. And
it fit into a large suitcase. At this pace, we might have a brain-scale
supercomputer in about a decade. A couple decades later its price and size
might be quite reasonable. Plus, you don’t have to stuff a supercomputer
into your head before you might use one as a brain aid—all you need is
an implant or helmet or headband that can send and receive signals. So
one day, perhaps in three or four decades, we may have brain aids that
compensate for dyslexia or poor memory. If so, enhancements may then
become common.
“Microwulf:
a beowulf cluster for every desk,”
J. C. Adams, T. H. Brom,
Proceedings of the 39th SIGCSE technical symposium on Computer
science education,
2008, pages 121-125.
Behind Deep Blue:
Building the Computer that Defeated the World Chess Champion,
Feng-Hsiung Hsu,
Princeton University Press, 2002.
- [getting the joke]
-
Detecting a joke localizes to the left inferior frontal cortex and the
posterior temporal cortex. Finding a joke funny, though, localizes to
bilateral regions of the insular cortex and the amygdala.
“Neural correlates of humor detection and appreciation,”
J. M. Moran, G. S. Wig, R. B. Adams, Jr., P. Janata, W. M. Kelley,
NeuroImage,
21(3):1055-60, 2004.
See also:
voluntary and involuntary laughter:
The Psychology of Humor:
An Integrative Approach,
Rod A. Martin,
Academic Press, 2007, page 171.
“Contralateral smile and laughter, but no mirth, induced by electrical
stimulation of the cingulate cortex,”
F. Sperli, L. Spinelli, C. Pollo, M. Seeck,
Epilepsia,
47(2):440-443, 2006.
- [brain ‘modules’ and cortical specializations]
-
The idea of ‘modules’ is just a way of speaking. We have no real idea
how the brain is organized, except in a very crude sense. For example,
we have several different kinds of memory systems. We use one, called
semantic memory, when we recall what the word ‘breakfast’ means. We
use another, called procedural memory, when we recall how to eat
breakfast. And we use a third, episodic memory, when we recall whether
we’ve had breakfast today.
Our scanners are not yet high-resolution enough to answer the question
given the complexly dimensioned tasks that the brain undertakes.
Most neuroscientists today think that most if not all neural tasks are
widely distributed across the brain, although of course some are often
localized to only a few regions. Even when a brain region can be said
to specialize in some task, its amount of participation, and the brain
regions that it partners with to accomplish that task, can change from
task to task.
“Shifts of Effective Connectivity Within a Language Network during
Rhyming and Spelling,”
T. Bitan, J. R. Booth, J. Choy, D. D. Burman, D. R. Gitelman, M. M. Mesulam,
Journal of Neuroscience,
25(22):5397-403, 2005.
The problem is further complicated in that loss of connectivity between
regions can also impair task performance, even when the functional regions
themselves remain intact.
“Altered Effective Connectivity within the Language Network in Primary
Progressive Aphasia,”
S. P. Sonty, M. M. Mesulam, S. Weintraub, N. A. Johnson, T. B. Parrish,
D. R. Gitelman,
Journal of Neuroscience,
27(6):1334-1345, 2007.
First, here’s one recent paper challenging one of the task-specialization
attempts:
“Activity in Right Temporo-Parietal Junction is Not Selective for
Theory-of-Mind,”
J. P. Mitchell,
Cerebral Cortex,
18(2):262-271, 2008.
And here are several papers defending it for various tasks, particular
face recognition:
“Cortical Specialization for Face Perception in Humans,”
N. Kanwisher, G. Yovel,
in:
Handbook of Neuroscience for the Behavioral Sciences,
John T. Cacioppo and Gary G. Berntson (editors),
John Wiley & Sons, 2009.
“Patches with Links:
A Unified System for Processing Faces in the Macaque Temporal Lobe,”
S. Moeller, W. A. Freiwald, D. Y. Tsao,
Science,
320(5881):1355-1359, 2008.
“Domain Specificity in Visual Cortex,”
P. E. Downing, A. W. Chan, M. V. Peelen, C. M. Dodds, N. Kanwisher,
Cerebral Cortex,
16(10):1453-1461, 2006.
“Separate Face and Body Selectivity on the Fusiform Gyrus,”
R. F. Schwarzlose, C. I. Baker, N. Kanwisher,
Journal of Neuroscience,
25(47):11055-11059, 2005.
“Synaesthesia:
A window into perception, thought and language,”
V. S. Ramachandran, E. M. Hubbard,
Journal of Consciousness Studies,
8(12):3-34, 2001.
Phantoms in the Brain:
Probing the Mysteries of the Human Mind,
V. S. Ramachandran and Sandra Blakeslee,
Harper Perennial, 1999.
- [non-verbal cues used in conversation]
-
Our brain starts building that network in infancy. Soon after birth we
start paying attention to faces longer than any other object. And soon
after that, we start imitating them.
Our brain starts learning how to do all that even before we can speak.
As infants, we pay attention to a speaker’s head position and eyes by
two months of age, and by six months of age we also pay attention to
the direction of gaze.
“How does the topic of conversation affect verbal exchange and eye gaze?
A comparison between typical development and high-functioning autism,”
A. Nadig, I. Lee, L. Singh, K. Bosshart, S. Ozonoff,
Neuropsychologia,
48(9):2730-2739, 2010.
“Speakers’ eye gaze disambiguates referring expressions early during
face-to-face conversation,”
J. E. Hanna, S. E. Brennan,
Journal of Memory and Language,
57(4):596-615, 2007.
“Non-Verbal Cues for Discourse Structure,”
J. Cassell, Y. I. Nakano, T. W. Bickmore, C. L. Sidner, C. Rich,
Proceedings of the 41st Annual Meeting of the Association of Computational
Linguistics,
2001, pages 106-115.
Hand and Mind:
What Gestures Reveal about Thought,
David McNeill,
University of Chicago Press, 1992.
“Conspec and conlern—a 2-process theory of infant face recognition,”
J. Morton, M. H. Johnson,
Psychological Review,
98(2):164-181, 1991.
“Imitation of facial and manual gestures by human neonates,”
A. N. Meltzoff, M. K. Moore,
Science,
198(4312):74-78, 1977.
- [illusion from auditory and visual conflict during speech]
-
“The noisy encoding of disparity model of the McGurk effect,”
J. F. Magnotti, M. S. Beauchamp,
Psychonomic Bulletin & Review,
22(3):701–709, 2015.
“A neural basis for interindividual differences in the McGurk effect,
a multisensory speech illusion,”
A. R. Nath, M. S. Beauchamp,
NeuroImage,
59(1):781-787, 2012.
“fMRI-Guided transcranial magnetic stimulation reveals that the
superior temporal sulcus is a cortical locus of the McGurk effect,”
M. S. Beauchamp, A. R. Nath, S. Pasalar,
The Journal of Neuroscience,
30(7):2414-2417, 2010.
“Hearing lips and seeing voices,”
H. McGurk, J. MacDonald,
Nature,
264(5588):746-748, 1976.
- [auditory processing during speech]
-
The rough figures, from measurements of activation of Broca’s area,
are as follows:
200 milliseconds after cue to figure out what a word means.
320 milliseconds after cue to figure out what the appropriate grammar is.
450 millseconds after cue to process the phonetic form of the word.
“Sequential Processing of Lexical, Grammatical, and Phonological Information
Within Broca’s Area,”
N. T. Sahin, S. Pinker, S. S. Cash, D. Schomer, E. Halgren,
Science,
326(5951):445-449, 2009.
“The Speaking Brain,”
P. Hagoort, W. J. M. Levelt,
Science,
326(5951):372-373, 2009.
- [guessing emotions from faces]
-
Motion and emotion seem to be closely related.
“More than mere mimicry?
The influence of emotion on rapid facial reactions to faces,”
E. J. Moody, D. N. McIntosh, L. J. Mann, K. R. Weisser,
Emotion,
7(2):447-457, 2007.
“What’s in a smile?
Neural correlates of facial embodiment during social interaction,”
L. Schilbach, S. B. Eickhoff, A. Mojzisch, K. Vogeley,
Social Neuroscience,
3(1):37-50, 2007.
“Face to face:
blocking facial mimicry can selectively impair recognition of
emotional expressions,”
L. M. Oberman, W. Winkielman, V. S. Ramachandran,
Social Neuroscience,
2(3-4):167-178, 2007.
“Unconscious Facial Reactions to Emotional Facial Expressions,”
U. Dimberg, M. Thunberg, K. Elmehed,
Psychological Science,
11(1):86-89, 2000.
“Rapid facial reactions to emotional facial expressions,”
U. Dimberg, M. Thunberg,
Scandinavian Journal of Psychology,
39(1):39-45, 1998.
This is an example of the ‘facial feedback hypothesis,’ which is a branch
of research called ‘embodied cognition.’ It argues that we understand
emotions largely because we have physical bodies that we can configure
and then reflect on their configuration.
(Related hypotheses are the ‘mirror neuron hypothesis’ and the ‘facial mimicry
hypothesis.’)
For example, doctors can make pre-surgery patients with electrodes on
their heads laugh (or cry) depending on which part of their brain are
stimulated. Then, once their body starts laughing (or crying), their
brain starts feeling emotions that fit their body’s action. Or again,
when your brain sees someone’s fearful face, your amygdala reacts, and
your body then responds—you thus become a little afraid, too. Yet again,
if your brain sees, or even hears, someone laughing, your zygomaticus major
facial muscles (which contract when you laugh) will respond. Similarly,
if your brain sees or hears someone crying, your corrugator supercilii
facial muscles (which contract when you cry) respond.
The Myth of Mirror Neurons:
The Real Neuroscience of Communication and Cognition,
Gregory Hickok,
W. W. Norton, 2014.
“Dynamic Facial Expressions Evoke Distinct Activation in the Face Perception
Network:
A Connectivity Analysis Study,”
E. Foley, G. Rippon, N. J. Thai, O. Longe, C. Senior,
Journal of Cognitive Neuroscience,
24(2):507-520, 2011.
“A cortical network for face perception,”
A. Ishai,
in:
New Frontiers in Social Cognitive Neuroscience,
R. Kawashima, M. Sugiura, T. Tsukiura (editors),
Tohoku University Press, 2011, pages 73-81.
“You smile—I smile:
Emotion expression in social interaction,”
U. Hess, P. Bourgeois,
Biological Psychology,
84(3):514-520, 2010.
“Cosmetic Use of Botulinum Toxin-A Affects Processing of Emotional
Language,”
D. A. Havas, A. M. Glenberg, K. A. Gutowski, M. J. Lucarelli, R. J. Davidson,
Psychological Science,
21(7):895-900, 2010.
“Emotional conception:
How embodied emotion concepts guide perception and facial action,”
J. Halberstadt, P. Winkielman, P. Niedenthal, N. Dalle,
Psychological Science,
20(10):1254-1261, 2009.
“Embodied Emotion Modulates Neural Signature of Performance Monitoring,”
D. Wiswede, T. F. Münte, U. M. Krämer, J. Rüsseler,
PLoS ONE,
4(6):e5754, 2009.
“Cultural specificity in amygdala response to fear faces,”
J. Y. Chiao, T. Iidaka, H. L. Gordon, J. Nogawa, M. Bar, E. Aminoff,
N. Sadato, N. Ambady,
Journal of Cognitive Neuroscience,
20(12):2167-2174, 2008.
“Intentional Attunement:
Mirror Neurons and the Neural Underpinnings of Interpersonal Relations,”
V. Gallese, M. N. Eagle, P. Migone,
Journal of the American Psychoanalytic Association,
55(1):131-176, 2007.
“Embodied Emotions,”
J. Prinz,
in:
Thinking about Feeling:
Contemporary Philosophers on Emotions,
Robert C. Solomon (editor),
Oxford University Press, 2004, pages 44-58.
Looking for Spinoza:
Joy, Sorrow, and the Feeling Brain,
Antonio R. Damasio,
Houghton Mifflin Harcourt, 2003, especially pages 65-79.
“Duchenne smile, emotional experience, and autonomic reactivity:
A test of the facial feedback hypothesis,”
R. Soussignan,
Emotion,
2(1):52-74, 2002.
“Facial mimicry and emotional contagion to dynamic emotional
facial expressions and their influence on decoding accuracy,”
U. Hess, S. Blairy,
International Journal of Psychophysiology,
40(2):129-141, 2001.
“Mirror neurons, the insula, and empathy,”
M. Iacoboni, G. L. Lenzi,
Behavioral and Brain Sciences,
25(1):39-40, 2001.
“Amygdala response to facial expressions in children and adults,”
K. M. Thomas, W. C. Drevets, P. J. Whalen, C. H. Eccard, R. E. Dahl,
N. D. Ryan, B. J. Casey,
Biological Psychiatry,
49(4):309-316, 2001.
“Facial reactions to happy and angry facial expressions:
Evidence for right hemisphere dominance,”
U. Dimberg, M. Petterson,
Psychophysiology,
37(5):693-696, 2000.
The Feeling of What Happens:
Body and Emotion in the Making of Consciousness,
Antonio Damasio,
Harvest, 1999.
“Facial expressions are contagious,”
L. O. Lundqvist, U. Dimberg,
Journal of Psychophysiology,
9:203-211, 1995.
“Inhibiting and Facilitating Conditions of the Human Smile:
A Nonobtrusive Test of the Facial Feedback Hypothesis,”
F. Strack, L. L. Martin, S. Stepper,
Journal of Personality and Social Psychology,
54(5):768-777, 1988.
“Perception of the speech code,”
A. M. Liberman, F. S. Cooper, D. P. Shankweiler, M. Studdert-Kennedy,
Psychological Review,
74(6):431-461, 1967.
Maybe that mimicry even extends to your whole body. (No one’s done
the experiments yet, so we don’t know.) Perhaps when you see someone
hunched in on themselves in fear or sadness, maybe you hunch over a little,
too. If so, then perhaps when actors display an emotion well they aren’t
simply pretending—even if they think they are. Maybe the mere act
of configuring your body to convey an emotion generates an echo of that
emotion in your brain.
Note: The 1988 Strack result (on feeling better just by biting a pencil)
has been rejected as it isn’t replicable.
“According to the facial feedback hypothesis, people’s
affective responses can be influenced by their own facial expression
(e.g., smiling, pouting), even when their expression did not result from
their emotional experiences. For example, Strack, Martin, and Stepper
(1988) instructed participants to rate the funniness of cartoons using
a pen that they held in their mouth. In line with the facial feedback
hypothesis, when participants held the pen with their teeth (inducing a
‘smile’), they rated the cartoons as funnier than when they held the pen
with their lips (inducing a ‘pout’). This seminal study of the facial
feedback hypothesis has not been replicated directly. This Registered
Replication Report describes the results of 17 independent direct
replications of Study 1 from Strack et al. (1988), all of which followed
the same vetted protocol. A meta-analysis of these studies examined the
difference in funniness ratings between the “smile” and “pout”
conditions. The original Strack et al. (1988) study reported a
rating difference of 0.82 units on a 10-point Likert scale. Our
meta-analysis revealed a rating difference of 0.03 units with a 95%
confidence interval ranging from −0.11 to 0.16.”
From:
“Registered Replication Report:
Strack, Martin, & Stepper (1988),”
A. Acosta, R. B. Adams Jr., D. N. Albohn, E. S. Allard, T. Beek,
S. D. Benning, E.-M. Blouin-Hudon, L. C. Bulnes, T. L. Caldwell,
R. J. Calin-Jageman, C. A. Capaldi, N. S. Carfagno, K. T. Chasten,
A. Cleeremans, L. Connell, J. M. DeCicco, L. Dijkhoff, K. Dijkstra,
A. H. Fischer, F. Foroni, Q. F. Gronau, U. Hess, K. J. Holmes,
J. L. H. Jones, O. Klein, C. Koch, S. Korb, P. Lewinski, J. D. Liao,
S. Lund, J. Lupiáñez D. Lynott, C. N. Nance, S. Oosterwijk,
A. A. Özdoğru, A. P. Pacheco-Unguetti, B. Pearson, C. Powis,
S. Riding, T.-A. Roberts, R. I. Rumiati, M. Senden, N. B. Shea-Shumsky,
K. Sobocko, J. A. Soto, T. G. Steiner, J. M. Talarico, Z. M. vanAllen,
E.-J. Wagenmakers, M. Vandekerckhove, B. Wainwright, J. F. Wayand,
R. Zeelenberg, E. E. Zetzer, R. A. Zwaan,
Perspectives on Psychological Science,
11(6):917-928, 2016.
- [guessing ahead for reading and conversation ease]
-
Many experiments are still missing but it does appear as if you
don’t so much hear as reconstruct. You also don’t so much
read as recognize. Yuor brian has no prolbmes redanig thsee scarmbeld
wrods. Simly, yr brn cn stl rd ths sntc evn tho mny of its ltrs r msng.
And long before you finish this sentence your brain has already guessed
how it might... end.
Note, though, that there is a cost to decipher the words, that spelling is
linked to sounding, and that it doesn’t work uniformly in every language.
Hebrew, for example, is less forgiving than English.
“Neural responses to grammatically and lexically degraded speech,”
A. Bautista, S. M. Wilson,
Language, Cognition and Neuroscience,
31(4):1-8, 2016.
“Combined eye tracking and fMRI reveals neural basis of linguistic
predictions during sentence comprehension,”
C. E. Bonhage, J. L. Mueller, A. D. Friederici, C. J. Fiebach,
Cortex,
68:33-47, 2015.
“Assessing the influence of letter position in reading normal and transposed
texts using a letter detection task,”
K. Guérard, J. Saint-Aubin, M. Poirier, C. Demetriou,
Canadian Journal of Experimental Psychology,
66(4):227-238, 2012.
“Dissociating neural subsystems for grammar by contrasting word order and
inflection,”
A. J. Newman, T. Supalla, P. Hauser, E. L. Newport, D. Bavelier,
Proceedings of the National Academy of Sciences,
107(16):7539-7544, 2010.
“Letter-transposition effects are not universal:
The impact of transposing letters in Hebrew,”
H. Velan, R. Frost,
Journal of Memory and Language,
61(3):285-302, 2009.
“Transposed-letter priming of prelexical orthographic representations,”
S. Kinoshita, D. Norris,
Journal of Experimental Psychology. Learning, memory, and cognition,
35(1):1-18, 2009.
“Do transposed-letter similarity effects occur at a morpheme level?
Evidence for morpho-orthographic decomposition,”
J. A. Duñabeitia, M. Perea, M. Carreiras,
Cognition,
105(3):691-703, 2007.
“Raeding wrods with jubmled lettres:
there is a cost,”
K. Rayner, S. J. White, R. L. Johnson, S. P. Liversedge,
Psychological Science,
17(3):192-193, 2006.
“The remarkable inefficiency of word recognition,”
D. G. Pelli, B. Farell, D. C. Moore,
Nature,
423(6941):752-756, 2003.
“Does jugde activate COURT?
Transposed-letter confusability effects in masked associative priming,”
M. Perea, S. J. Lupker,
Memory and Cognition,
31(6):829-841, 2003.
“The brain circuitry of syntactic comprehension,”
E. Kaan, T. Y. Swaab,
Trends in Cognitive Sciences,
6(8):350-356, 2002.
“Eye-fixation behaviour, lexical storage and visual word recognition
in a split processing model,”
R. Shillcock, T. M. Ellison, P. Monaghan,
Psychological Review,
107(4):824-851, 2000.
“Lexical retrieval and selection processes:
Effects of transposed-letter confusability,”
S. Andrews,
Journal of Memory and Language,
35(6):775-800, 1996.
“Inference During Reading,”
G. McKoon, R. Ratcliff,
Psychological Review,
99(3):440-466, 1992.
“A ROWS is a ROSE:
Spelling, sound, and reading,”
G. C. Van-Orden,
Memory and Cognition,
15(3):181-198, 1987.
The significance of letter position in word recognition,
G. E. Rawlinson,
doctoral thesis,
University of Nottingham, 1976.
See also his amusing:
“Reibadailty,”
Graham Rawlinson,
New Scientist,
162(2188):55, 29 May 1999.
Here’s an extract:
“In a puiltacibon of New Scnieitst you could ramdinose all the letetrs,
keipeng the first two and last two the same, and reibadailty would hadrly
be aftcfeed. My ansaylis did not come to much beucase the thoery at the
time was for shape and senqeuce retigcionon. Saberi’s work sugsegts we may
have some pofrweul palrlael prsooscers at work.”
- [a half billion years of neural engineering...]
-
Likely, the first neurons go back at least around 540 million years to the
Edicaran (that is, Precambrian).
“The transition from simple, microscopic forms to the abundance of
complex animal life that exists today is recorded within soft-bodied
fossils of the Ediacara Biota (571 to 539 Ma). Perhaps most critically
is the first appearance of bilaterians—animals with two openings
and a through-gut—during this interval. Current understanding of
the fossil record limits definitive evidence for Ediacaran bilaterians
to trace fossils and enigmatic body fossils. Here, we describe the
fossil Ikaria wariootia, one of the oldest bilaterians
identified from South Australia. This organism is consistent with
predictions based on modern animal phylogenetics that the last
ancestor of all bilaterians was simple and small and represents a
rare link between the Ediacaran and the subsequent record of animal
life.”
From:
“Discovery of the oldest bilaterian from the Edicaran of South
Australia,”
S. D. Evans, I. V. Hughes, J. G. Gehling, M. L. Droser,
Proceedings of the National Academy of Sciences,
117(14):7845-7850, 2020.
- [is there a captain Kirk in the brain?]
-
This is known as the ‘no homunculus’ idea within executive function in
neuroscience. For example, variant response time in the Stroop task,
where you have to decide a color given a color word (red, blue, green,
and so on) that is itself in one of various colors, suggests that there
is no single area of the brain specialized to inhibiting all other areas.
“Conscious and Unconscious:
Toward an Integrative Understanding of Human Mental Life and Action,”
R. F. Baumeister, J. A. Bargh,
in:
Dual-Process Theories of the Social Mind,
Jeffrey W. Sherman, Bertram Gawronski, and Yaacov Trope (editors),
Guilford Press, 2014, pages 35-49.
“The Three Pillars of Volition:
Phenomenal states, ideomotor processing, and the skeletal muscle system,”
E. Morsella, T. Molapuor, M. Lynn,
in:
Agency and Joint Attention,
Janet Metcalfe and Herbert S. Terrace (editors),
Oxford University Press, 2013, pages 284-303.
“The Function of Consciousness in Controlling Behavior,”
S. Steele, H. Lau,
in:
Agency and Joint Attention,
Janet Metcalfe and Herbert S. Terrace (editors),
Oxford University Press, 2013, pages 304-320.
“Do Conscious Thoughts Cause Behavior?”
R. F. Baumeister, E. J. Masicampo, K. D. Vohs
Annual Review of Psychology,
62(1):331-361, 2011.
“Conscious Thought Is for Facilitating Social and Cultural Interactions:
How Mental Simulations Serve the Animal-Culture Interface,”
R. F. Baumeister, E. J. Masicampo,
Psychological Review,
117(3):945-971, 2010.
“The inhibition of unwanted actions,”
C. E. Curtis, M. D’Esposito,
in:
Oxford Handbook of Human Action,
Ezequiel Morsella, John A. Bargh, and Peter M. Gollwitzer (editors),
Oxford University Press, 2009, pages 72-97.
“Limits on introspection:
Distorted subjective time during the dual-task bottleneck,”
G. Corallo, J. Sackur, S. Dehaene, M. Sigman,
Psychological Science,
19(11):1110-1117, 2008.
The Genius Engine:
Where Memory, Reason, Passion, Violence,
and Creativity Intersect in the Human Brain,
Kathleen Stein,
Wiley, 2007.
“Banishing the homunculus:
Making working memory work,”
T. E. Hazy, M. J. Frank, R. C. O’Reilly,
Neuroscience,
139(1):105-118, 2006.
- [the brain may be impossible to understand]
-
This can happen even with seemingly trivial analog circuits,
once they have evolved rather than been designed.
“Notes on design through artificial evolution:
Opportunities and algorithms,”
A. Thompson,
in:
Adaptive Computing in Design and Manufacture V,
I. C. Parmee (editor),
Springer-Verlag, 2002, pages 17-26.
“Evolution of robustness in an electronics design,”
A. Thompson, P. Layzell,
in:
Proceedings of the Third International Conference
on Evolvable Systems (ICES2000):
From Biology to Hardware,
Julian Miller, Adrian Thompson, Peter Thomson, and Terence C. Fogarty (editors),
Springer-Verlag, 2000, pages 218-228.
- [Vicki P. and other split-brain patients]
-
That division of labor inside the brain isn’t just true for sense-making,
it may be true for everything about us. We suspect that because nearly all
of us have two half-brains. The left half controls the right side of the
body while the right half controls the left side of the body. Normally,
only one half-brain (usually the left one) controls the voice box. That
usually doesn’t matter, because the halves are linked—so both know
what the other is up to. However, in rare cases doctors sometimes used
to split those brains (in last-ditch treatments for epilepsy), and in
the split-brained, neither brain has much idea what the other is up
to—or even that the other brain exists.
In 1979, doctors sliced Vicki P.’s brain in two and on recovery, she
noticed herself behaving oddly. Her left hand would grab things she didn’t
intend to grab. When she’d get dressed, she’d find herself putting on
two pairs of shorts, one on top of the other. She’s even more unusual in
that about a year after surgery her normally mute right brain learned
how to speak. Both her brains say that they’re Vicki. So when she says
that her left arm is doing something that ‘she’ doesn’t intend, we know
that her left brain is talking. It’s presently controlling her voice box
but it has no idea why the body it’s in is moving its left arm. Before
her brain was split she was one person. Now she may be becoming two.
“...[T]he two most obvious sources of employment are closed to her: she
can’t type and she can’t be a waitress because her manual dexterity is
limited through her operation. Vicki knows, too, and knew before she
started her psychological tests, that there was a strangeness in her
behaviour.
‘My left hand is under control, but yet it grabs things that it
shouldn’t grab, or it grabs things I don’t want it to grab. It just
sort of just reaches out, like that. I don’t like the idea of that,
because I don’t know what is happening. Sometimes I just take my right
hand and grab hold of my left hand or arm and pull it back. Other times,
it may sound silly, but I slap it because I get mad at it, I really do,
I get really mad at it, and I find that doesn’t do any good, except it
hurts after it’s slapped.’
Typical of this behaviour are the repeated frustrations she experiences
when trying to select her clothes in the morning. ‘I knew what I wanted
to wear and I would open my closet, get ready to take out what I wanted,
and my other hand would just take control. It would just reach in.
I told the lady at medical college that I was really fighting with
it and she said to talk to it, talk to your hand. But it didn’t do
any good. It would reach in and get something I didn’t want at all.
And a couple of times I had a pair of shorts on, and I would find myself
putting on another pair of shorts on top of the pair I already had on.
I knew that was wrong. I wouldn’t go out of the house that way, I knew
that was totally wrong, but my hand sort of took control, got that other
pair of shorts and put them on.’ ”
The Human Brain,
Dick Gilling and Robin Brightwell,
Orbis Publishing, 1982, page 171.
Note that while Vicki’s mute right brain eventually developed speech,
so did Paul S. and J. W., two other split-brain patients, and none of the
three had full sectioning but instead two-stage callosotomy (sectioning
of the corpus callosum only and not the anterior commissure, the massa
intermedia, and the right fornix) so that may be part of why. But a
more important reason is how old they were before their first epileptic
attack (Paul S. was only 20 months old before his, and Vicki P. just
six years old before hers, whereas J. W. was 19 years old before his first
attack). A strong possibility is that the younger the age, the more likely
it is that the right brain would begin to take over some language tasks
sooner. J. W. took 11 years before his right brain showed emerging signs
of increased language capability, and nothing as sophisticated as Vicki
P.’s, which emerged 12 months after surgery, and Paul S.’s, which emerged
18 months after surgery. All three are right-handed.
“The control of speech in the adult brain:
The disconnected right hemispheres of PS, VP, and JW,”
C. Code, Y. Joanette,
in:
Classic Cases in Neuropsychology,
Volume II,
Chris Code, Claus-W. Wallesch, Yves Joanette,
and André Roche Lecours (editors),
Psychology Press, 2003, pages 114-121.
See also:
“Binocular rivalry and perceptual ambiguity,”
D. Alais, R. Blake,
in:
Oxford Handbook of Perceptual Organization,
Johan Wagemans (editor),
Oxford University Press, 2015, pages 775-798.
“Binocular rivalry in split-brain observers,”
R. P. O’Shea, P. M. Corballis,
Journal of Vision,
3(10):610-615, 2003.
Nature’s Mind:
The Biological Roots of Thinking, Emotions, Sexuality, Language, and
Intelligence,
Michael S. Gazzaniga,
Basic Books, 1992, pages 123-126.
The Social Brain:
Discovering the Networks of the Mind,
Michael S. Gazzaniga,
Basic Books, 1985, page 91 and 129.
- [language specialization and split-brain patients]
-
“Review:
Hemispheric specialization for language,”
G. Josse, N. Tzourio-Mazoyer,
Brain Research Reviews,
44(1):1-12, 2004.
“Cerebral specialization and interhemispheric communication:
Does the corpus callosum enable the human condition?”
M. S. Gazzaniga,
Brain,
123(7):1293-1326, 2000.
- [half a brain can be enough]
-
Vicki P. isn’t the only such example. One girl, A. H., was born with
half her brain missing. One boy, Nico, had half his brain removed. Both
are growing up almost normally.
A. H. was diagnosed only when she was three, when she started having
seizures. As of 2009, she was ten and seemed almost normal.
“Bilateral visual field maps in a patient with only one hemisphere,”
L. Muckli, M. J. Naumerd, W. Singer,
Proceedings of the National Academy of Sciences,
106(31):13034-13039, 2009.
Nico, an incurably epileptic three-year-old, had half his brain removed
yet he too is growing up almost normally.
Half a Brain is Enough:
The Story of Nico,
Antonio M. Battro,
Cambridge University Press, 2001.
Surgeons have now done at least a hundred hemispherectomies.
- [reality is a construct]
-
Your brain is always active, even while you’re tired or distracted—or
even asleep. Your neurons link in highly recursive ways, with cycles
inside cycles inside cycles. As one gets excited, it excites others,
which may in turn excite it. They’re thus always catalyzing each
other—oscillating, resonating, pulsating. Thus, most of the time,
most of your brain isn’t watching or controlling action in the world;
it’s talking to itself. So just as our cells use catalysis to bring their
parts together, and our swarm uses synergy to bring its parts together,
perhaps your brain uses synchrony to bring its parts together. Thus,
perhaps, you’re mostly guessing your way along, with your memories coloring
what you then think you detect.
You might only discover what parts of your brain really want or are
good at if you hit your head, get drunk, fall in love, do some heroin,
or otherwise disturb how those parts link. Your moods, views, talents,
all could change depending on what happens to you.
Most of your brain’s modules do their stuff mostly below, beside, or beyond
your awareness. For example, Paul S., who, like Vicki P., is a split-brain
patient, was once asked what he’d like to be when he grew up. He was 15
at the time. His left-brain, his speaking brain, said ‘draftsman.’ That’s
also what his parents wanted him to become. However, his mute right-brain,
when asked the same question, chose ‘race-car driver.’ In 1994, Tony C., a
42-year-old surgeon, was hit by lightning. Three days later an insatiable
hunger for music beset him. Three months later he was playing the piano
night and day. Then he started composing. In 2006, he remarked that
“[the music] comes from heaven, as Mozart said.” Similarly, in 1987,
Kenneth P., a 23-year-old, fell asleep in front of the TV. He then rose,
got in his car, and drove 14 miles from his home to the house of his
parents-in-law. He then killed his mother-in-law and tried to kill his
father-in-law. He then drove to the nearest police station, covered in
blood. Police saw it as an open-and-shut case. However, after trial,
he was set free. The court judged that when he committed the murder he
was asleep.
“What we call reality consists of a few iron posts of observation between
which we fill in by an elaborate papier-mâché construction
of imagination and theory.”
From:
“Delayed choice experiments and the Bohr-Einstein dialog,”
John Archibald Wheeler,
in:
Quantum Theory and Measurement,
John Archibald Wheeler and Wojciech Hubert Zurek (editors),
Princeton University Press, 1983, page 194.
Paul S. reference:
The Integrated Mind,
Michael S. Gazzaniga and Joseph E. LeDoux,
Springer, 1978, page 143.
To be precise, the occupation his right-brain chose was ‘automobile race.’
Tony C. references:
Musicophilia:
Tales of Music and the Brain,
Oliver Sacks,
Random House, Inc., 2007.
Notes From an Accidental Pianist and Composer,
Tony Cicoria,
self-published audio CD, 2008.
Kenneth P. references:
“Sleepwalking violence:
a sleep disorder, a legal dilemma, and a psychological challenge,”
R. Cartwright,
American Journal of Psychiatry,
161(7):1149-58, 2004.
“Homicidal Somnambulism:
A Case Report,”
R. Broughton, R. Billings, R. Cartwright, D. Doucette, J. Edmeads,
M. Edwardh, F. Ervin, B. Orchard, R. Hill, G. Turrell,
Sleep,
17(3):253-264, 1994.
R. v. Parks, [1992] 2 S.C.R. 871,
File Number 22073, August 27th, 1992,
Judgments of the Supreme Court of Canada.
- [the guessing brain?]
-
“On How Network Architecture Determines the Dominant Patterns of Spontaneous
Neural Activity,”
R. F. Galán,
PLoS ONE,
3(5):e2148, 2008.
“Nonperiodic Synchronization in Heterogeneous Networks of Spiking Neurons,”
J.-P. Thivierge, P. Cisek,
Journal of Neuroscience,
28(32):7968-7978, 2008.
“Deep, Narrow Sigmoid Belief Networks Are Universal Approximators,”
I. Sutskever, G. E. Hinton,
Neural Computation,
20(11):2629-2636, 2008.
Learning and Memory:
From Brain to Behavior,
Mark A. Gluck, Eduardo Mercado, Catherine E. Myers,
Worth Publishers, 2007.
Rhythms of the Brain,
György Buzsáki,
Oxford University Press, 2006.
“Reducing the Dimensionality of Data with Neural Networks,”
G. E. Hinton, R. R. Salakhutdinov,
Science,
313(5786):504-507, 2006.
“A Fast Learning Algorithm For Deep Belief Networks,”
G. E. Hinton, S. Osindero, Y. W. Teh,
Neural Computation,
18(7):1527-1554, 2006.
“Cortico-hippocampal interaction and adaptive stimulus representation:
a neurocomputational theory of associative learning and memory,”
M. A. Gluck, C. E. Myers, M. Meeter,
Neural Networks,
18(9):1265-1279, 2005.
I of the Vortex:
From Neurons to Self,
Rodolfo R. Llinás,
The MIT Press, 2002.
Fluid Concepts and Creative Analogies:
Computer Models of the Fundamental Mechanisms of Thought,
Douglas Hofstadter and the Fluid Analogies Research Group,
Basic Books, 1995.
“A Learning Algorithm for Boltzmann Machines,”
D. H. Ackley, G. E. Hinton, T. J. Sejnowski,
Cognitive Science,
9(1):147-169, 1985.
“The Copycat Project:
An Experiment in Nondeterminism and Creative Analogies,”
D. R. Hofstadter,
Memo Number 755,
MIT Artificial Intelligence Laboratory,
MIT, 1984.
- [the distributed brain?]
-
“Complex material systems with distributed non-linear feedback, such
as brains and the activities of their neural and behavioral substrates,
cannot be explained by linear causality. They can be said to operate
by circular causality without agency. The nature of self-control is
described by breaking the circle into a forward limb, the intentional
self, and a feedback limb, awareness of the self and its actions. The
two limbs are realized through hierarchically stratified kinds of neural
activity. Actions are governed by the microscopic neural activity of
cortical and subcortical components in the brain that is self-organized
into mesoscopic wave packets. The wave packets form by state transitions
that resemble phase transitions between vapor and liquid. The cloud of
action potentials driven by a stimulus condenses into an ordered state
that gives the category of the stimulus. Awareness supervenes as a
macroscopic ordering state that defers action until the self-organizing
mesoscopic process has reached closure in reflective prediction. Agency,
which is removed from the causal hierarchy by the appeal to circularity,
re-appears as a metaphor by which objects and events in the world are
anthropomorphized and assigned the human property of causation, so that
they can be assimilated as subject to the possibility of observer control.”
From:
“William James on Consciousness, Revisited,”
W. J. Freeman,
in:
New Research on Chaos and Complexity,
Franco F. Orsucci and Nicoletta Sala (editors),
Nova Publishers, 2006, pages 21-46.
However, see:
“The Automaticity Juggernaut:
Or, Are We Automatons After All?”
J. F. Kihlstrom,
in:
Are We Free?
Psychology and Free Will,
John Baer, James C. Kaufman, and Roy F. Baumeister (editors),
Oxford University Press US, 2008, pages 155-181.
For more general discussion of the idea of automaticity, see:
Mind in Life:
Biology, Phenomenology, and the Sciences of Mind,
Evan Thompson,
Harvard University Press, 2007, particularly Chapter 3.
A Mind of Its Own:
How Your Brain Distorts and Deceives,
Cordelia Fine,
W. W. Norton, 2006.
“The Automaticity of Social Life,”
J. A. Bargh, E. L. Williams,
Current Directions in Psychological Science,
15(1):1-4, 2006.
Strangers to Ourselves:
Discovering the Adaptive Unconscious,
Timothy Wilson,
Harvard University Press, 2002.
The Illusion of Conscious Will,
Daniel M. Wegner,
The MIT Press, 2002.
“The Unbearable Automaticity of Being,”
J. A. Bargh, T. L. Chartrand,
American Psychologist,
54(7):462-479, 1999.
Autopoiesis and Cognition:
The Realization of the Living,
Humberto Maturana and Francisco J. Varela,
D. Reidel, 1980.
- [the story-making brain?]
-
“Brain preparation before a voluntary action:
Evidence against unconscious movement initiation,”
J. Trevena, J. Miller,
Consciousness and Cognition,
19(1):447-456, 2009.
“Unconscious determinants of free decisions in the human brain,”
C. S. Soon, M. Brass, H.-J. Heinze, J.-D. Haynes,
Nature Neuroscience,
11(5):543-545, 2008.
Mind Time:
The Temporal Factor in Consciousness,
Benjamin Libet,
Harvard University Press, 2004.
Altered Egos:
How the Brain Creates the Self,
Todd E. Feinberg,
Oxford University Press, 2002.
The Society of Mind,
Marvin Minsky,
Simon & Schuster, 1988.
The Social Brain:
Discovering the Networks of the Mind,
Michael S. Gazzaniga,
Basic Books, 1987.
“Telling more than we can know:
Verbal reports on mental processes,”
R. E. Nisbett, T. D. Wilson,
Psychological Review,
84(3):231-259, 1977.
- [the daydreaming brain?]
-
“The brain’s default network:
anatomy, function, and relevance to disease,”
R. L. Buckner, J. R. Andrews-Hanna, D. L. Schacter,
Annals of the New York Academy of Sciences,
1124(1):1-38, 2008.
“Going AWOL in the Brain:
Mind Wandering Reduces Cortical Analysis of External Events,”
J. Smallwood, E. Beach, J. W. Schooler, T. C. Handy,
Journal of Cognitive Neuroscience,
20(3):458-469, 2008.
“The maturing architecture of the brain’s default network,”
D. A. Fair, A. L. Cohen, N. U. F. Dosenbach, J. A. Church, F. M. Miezin,
D. M. Barch, M. E. Raichle, S. E. Petersen, B. L. Schlaggar,
Proceedings of the National Academy of Science,
105(10):4028-4032, 2008.
“Network structure of cerebral cortex shapes functional connectivity on
multiple time scales,”
C. J. Honey, R. Kötter, M. Breakspear, O. Sporns,
Proceedings of the National Academy of Science,
104(24):10240-10245, 2007.
“Wandering Minds:
The Default Network and Stimulus-Independent Thought,”
M. F. Mason, M. I. Norton, J. D. Van Horn, D. M. Wegner, S. T. Grafton,
C. N. Macrae,
Science,
315(5810):393-395, 2007.
“Neuroscience:
The Brain’s Dark Energy,”
M. E. Raichle,
Science,
314(5803):1249-1250, 2006.
Interestingly, these two main functional modes of the brain seem to
correlate with neural frequencies in the high-gamma band
(about 60 to 200 Hertz).
“A blueprint for real-time functional mapping via human intracranial
recordings,”
J. P. Lachaux, K. Jerbi, O. Bertrand, L. Minotti,
D. Hoffmann, B. Schoendorff, P. Kahane,
PLoS ONE,
2(10):e1094, 2007.
“High Gamma Power Is Phase-Locked to Theta Oscillations in Human
Neocortex,”
R. T. Canolty, E. Edwards, S. S. Dalal, M. Soltani, S. S. Nagarajan,
H. E. Kirsch, M. S. Berger, N. M. Barbaro, R. T. Knight,
Science,
313(5793):1626-1628, 2006.
Both research groups, in Lyon and in San Francisco, are measuring
electrical activity in the brains of awake pre-surgical epilepsy patients.
The brain’s default network changes from childhood to adulthood, with
connections growing from local to global.
“Functional Brain Networks Develop
from a ‘Local to Distributed’ Organization,”
D. A. Fair, A. L. Cohen, J. D. Power, N. U. F. Dosenbach, J. A. Church,
F. M. Miezin, B. L. Schlaggar, S. E. Petersen,
PLoS Computational Biology,
5(5):e1000381, 2009.
- [we act, then notice that we acted]
-
“We Infer Rather Than Perceive the Moment We Decided to Act,”
W. P. Banks, E. A. Isham,
Psychological Science,
20(1):17-21, 2009.
“The timing of the conscious intention to move,”
M. Matsuhashi, M. Hallett,
European Journal of Neuroscience,
28(11):2344-2351, 2008.
“Perceiving the Present and a Systematization of Illusions,”
M. A. Changizi, A. Hsieh, R. Nijhawan, R. Kanai, S. Shimojo,
Cognitive Science:
A Multidisciplinary Journal,
32(3):459-503, 2008.
“A Cinematographic Hypothesis of Cortical Dynamics in Perception,”
W. J. Freeman,
International Journal of Psychophysiology,
60(2):149-161, 2006.
“On Measuring the Perceived Onsets of Spontaneous Actions,”
H. C. Lau, R. D. Rogers, R. E. Passingham,
Journal of Neuroscience,
26(27):7265-71, 2006.
The New Unconscious,
Ran R. Hassin, James S. Uleman, John A. Bargh (editors),
Oxford University Press, 2005.
The Illusion of Conscious Will,
Daniel M. Wegner,
The MIT Press, 2002.
- [surfing an ocean of calculation—running a body]
-
In 1971 Ian Waterman caught a virus. It destroyed his sense of touch
and body position from the neck down. He could feel pain and heat, but
lost all other sense of his body. He could no longer walk. He couldn’t
sit in a chair without falling over. He couldn’t hold an egg without
either crushing it or dropping it. He couldn’t feed himself, or wash
himself. He couldn’t even get out of bed. Then, with enormous effort, he
taught himself to sit up in bed. To do so, he had to plan when to tense
each muscle, then watch them at all times to make sure they didn’t go
off and do something else. It took him four months to learn how to put on
his socks. Learning to stand again took a year. Even today he has to keep
an eye on himself at all times—literally. He has to watch his limbs and
think his way through even the simplest movement. To him, simply walking
is like one of us juggling three balls while riding a unicycle. And even
with all that constant thinking, he’s never regained the fluid grace that
most of us take for granted. For instance, whenever the power goes out,
he collapses—like a puppet with its strings cut. Unable to see himself,
he must lay there until power comes back. Only when he can see himself
again can he control his robot body enough to stand up.
Ian Waterman has a large fiber sensory neuropathy. He’s lost all his
dorsal root ganglion cells. It has led to the loss of the sensations
of movement and position sense and light touch, below the neck.
“IW - ‘the man who lost his body’,”
D. McNeill, L. Quaeghebeur, S. Duncan,
in:
Handbook of Phenomenology and Cognitive Sciences,
Shaun Gallagher and Daniel Schmickin (editors),
Springer, 2010, pages 519-546.
How the Body Shapes the Mind,
Shaun Gallagher,
Oxford University Press, 2005, pages 43-64.
Pride and a Daily Marathon,
Jonathan Cole,
The MIT Press, 1995.
- [involuntary action]
-
Likely then, your brain has many different controllers. Many of them
have simple goals: seek warmth, attempt sex, avoid pain. Others size
up the world, make context, set goals. Others modulate how or when
yet others react or can react. Many, perhaps most, of them may be out
of your direct control. Thus, only a rare few of us can trigger our
goose bumps, engorge our nipples, change our skin temperature, control
our heart rate, alter our brain waves—or possess superhuman memory,
calculation, musicality. Your crafty brain is far faster, smarter,
sexier, and more knowledgeable than you are. It’s also more infantile,
more bigoted, more emotional, and more shortsighted than you are. And,
perhaps, you may be better off when it keeps its secrets to itself.
All that may help explain why your brain may be only around two percent
of your body’s weight yet it consumes up to 20 percent of your body’s
energy. Most of its computation you don’t need to be aware of.
For example, the technical term for goosebumps is piloerection. For almost
all of us, that’s involuntary and so part of the autonomic (involuntary)
nervous system. But a very few of us can control it.
“Physiological correlates and emotional specificity of human
piloerection,”
M. Benedek, C. Kaernbach,
Biological Psychology,
86(3):320-329, 2011.
“Objective and continuous measurement of piloerection,”
M. Benedek, B. Wilfling, R. Lukas-Wolfbauer, B. H. Katzur, C. Kaernbach,
Psychophysiology,
47(5):989-993, 2010.
“Autonomic activity and brain potentials associated with ‘voluntary’ control
of the pilomotors (mm. arrectores pilorum),”
D. B. Lindsley, W. H. Sassaman,
Journal of Neurophysiology,
1(4):342-349, 1938.
“Voluntary contraction of the Arrectores Pilorum,”
A. J. Chalmers,
Journal of Physiology,
Volume 31,
Proceedings of the Physiological Society,
August 19, pages 60-61, 1904.
“A Case of Voluntary Erection of the Human Hair and Production of Cutis
Anserina,”
S. S. Maxwell,
American Journal of Physiology,
7(4):369-379, 1902.
For some other (scientifically documented) rare abilities see:
“Biofeedback,”
R. J. Gatchel,
in:
Cambridge Handbook of Psychology, Health, and Medicine,
Andrew Baum (editor),
Cambridge University Press, 1997, pages 197-199.
In 1963, Neal Miller and associates attempted to disprove Gregory Kimble’s
widely held assertion that voluntary control over the autonomic nervous
system was not possible. Since then, many studies have shown that it is
possible for heart rate, blood pressure, muscle tension, skin temperature,
skin conductance, respiration, and gastrointestinal activity.
- [writing in the surf...]
-
Partly inspired by two stories.
“The Electric Ant,”
in:
The Collected Short Stories of Philip K. Dick,
Volume 5:
We Can Remember It for You Wholesale,
Philip K. Dick,
Orion Publishing Group,
1987, pages 225-240.
“Exhalation,”
in:
Exhalation:
Stories,
Ted Chiang,
Knopf, 2019, pages 37-57.
As a aside, whether a brain is necessary, unless attacked, see
“Swarm,”
Crystal Express,
Bruce Sterling,
Arkham House, 1989.
- [a worm’s eye view—the worm brain]
-
The text refers to the hermaphroditic form of Caenorhabditis
elegans (the other form is the much smaller male; there are no
females), the first animal species for which we know both its genome
and its connectome (its brain since 1986; in fact, more than that, as of 2019
we know its entire nervous system, and for both sexes).
It’s just a millimeter long and
its genome is 100,291,840 base pairs long, which encodes a
little over 20,400 genes. Its connectome consists of 302 neurons (almost
a third of its 959 cells in total, with 95 of them muscle cells, 56 of
them are support cells for its neurons, and 113 of them are motor neurons)
that are linked by over 6,393 chemical synapses, 890 electrical junctions
(gap junctions, or very close neural links, which are very fast junctions
but which lack signal amplification and can only be excitatory, never
inhibitory), and 1,410 neuromuscular junctions (namely, where neurons meet
muscles). As of 2011, around 10 percent of the wiring is still unknown.
Its nervous system divides into roughly four layers: a sensory layer
directly exposed to external stimuli (odor, touch, heat, light);
a layer of interneurons receiving those signals; a layer of command
neurons receiving those interneuron signals as well as sensory signals;
and a layer of motor neurons that control the muscles. Various
neurotransmitters and neuromodulators—such as serotonin, dopamine,
acetylcholine, tyramine, and octopamine—modulate that neural circuitry.
Their diffusion can change which neurons can talk to which other neurons.
Further, many genes combine to make one neuron, and also many neurons
combine to make one type of behavior (example, fleeing or feeding).
Conversely, one gene may influence many neurons (pleiotropy), just as one
neuron many influence many behaviours (particularly if the neuron belongs
to the small ‘rich club’ of neurons—a clique of high-degree network
hubs that are connected to each other topologically with high efficiency
so that there is a short path length between any two rich club nodes).
Further, the worm’s development can affect what it learns—or at least,
how it later behaves.
As things happen in the worm, or in the world around it, the worm’s
neural network has to decide: Feed? Flee? Lay an egg? But the network’s
behavior doesn’t merely come from neuron linkage in space; it also comes
from linkage patterns in time. When a neuron fires, that act might
affect the rest of the network some particular way; but had the same
thing happened a second before (or after), its effect might have been
different.
Who would design such a thing? It’s so plastic that its behavior is
difficult (impossible?) to predict. But consider: If there once were
many highly predictable creatures, to any less predictable creatures,
such creatures could only be walking boxed lunches.
“Whole-animal connectomes of both Caenorhabditis elegans sexes,”
S. J. Cook, T. A. Jarrell, C. A. Brittin, Y. Wang, A. E. Bloniarz,
M. A. Yakovlev, K. C. Q. Nguyen, L. T.-H. Tang, E. A. Bayer, J. S. Duerr,
J. E. Bülow, O. Hobert, D. H. Hall, S. W. Emmons,
Nature,
571(7763):63-71, 2019.
Behaving:
What’s Genetic, What’s Not, and Why Should We Care?
Kenneth F. Schaffner,
Oxford University Press, 2016, chapter 3.
“Distinct Circuits for the Formation and Retrieval of an Imprinted Olfactory
Memory,”
X. Jin, N. Pokala, C. I. Bargmann,
Cell,
164(4):632-643, 2016.
“Genome-wide Functional Analysis of CREB/Long-Term Memory-Dependent
Transcription Reveals Distinct Basal and Memory Gene Expression Programs,”
V. Lakhina, R. N. Arey, R. Kaletsky, A. Kauffman, G. Stein, W. Keyes,
D. Xu, C. T. Murphy,
Neuron,
85(2):330-345 2015.
“C. elegans locomotion:
small circuits, complex functions,”
M. Zhen, A. D. Samuel,
Current Opinion in Neurobiology,
33:117-126, 2015.
“Global brain dynamics embed the motor command sequence of Caenorhabditis
elegans,”
S. Kato, H. S. Kaplan, T. Schrödel, S. Skora, T. H. Lindsay,
E. Yemini, S. Lockery, M. Zimmer,
Cell,
163(3):656-669, 2015.
“Feedback from Network States Generates Variability in a
Probabilistic Olfactory Circuit,”
A. Gordus, N. Pokala, S. Levy, S. W. Flavell, C. I. Bargmann,
Cell,
161(2):215-227, 2015.
“An Integrated Neuromechanical Model of Steering in C. elegans,”
E. J. Izquierdo, R. D. Beer,
Proceedings of the European Conference on Artificial Life,
Paul Andrews, Leo Caves, René Doursat, Simon Hickinbotham, Fiona Polack,
Susan Stepney, Tim Taylor, and Jon Timmis (editors),
The MIT Press,
pages 199-206, 2015.
“Information Flow through a Model of the C. elegans
Klinotaxis Circuit,”
E. J. Izquierdo, P. L. Williams, R. D. Beer,
PLoS ONE,
10(10):e0140397, 2015.
“The OpenWorm Project:
currently available resources and future plans,”
P. Gleeson, M. Cantarelli, M. Currie, J. Hokanson, G. Idili, S. Khayrulin,
A. Palyanov, B. Szigeti, S. Larson,
BMC Neuroscience,
16(Supplement 1):P141, 2015.
“OpenWorm:
an open-science approach to modelling Caenorhabditis elegans,”
B. Szigeti, P. Gleeson, M. Vella, S. Khayrulin, A. Palyanov, J. Hokanson,
M. Currie, M. Cantarelli, G. Idili, S. Larson,
Frontiers in Computational Neuroscience,
8(79): 2014.
“High-throughput optical quantification of mechanosensory
habituation reveals neurons encoding memory in Caenorhabditis
elegans,”
T. Sugi, Y. Ohtani, Y. Kumiya, R. Igarashi, M. Shirakawa,
Proceedings of the National Academy of Science,
111(48):17236-17241, 2014.
“Investigating dynamical properties of the Caenorhabditis
elegans, connectome through full-network simulations,”
J. Kunert, E. Shlizerman, J. N. Kutz,
BMC Neuroscience,
14(Supplement 1):P229, 2013.
“Connecting a Connectome to Behavior:
An Ensemble of Neuroanatomical Models, of C. elegans Klinotaxis,”
E. J. Izquierdo, R. D. Beer,
PLoS Computational Biology,
9(2):e1002890, 2013.
“The Rich Club of the C. elegans Neuronal Connectome,”
E. K. Towlson, P. E. Vértes, S. E. Ahnert, W. R. Schafer, E. T. Bullmore,
The Journal of Neuroscience,
33(15):6380-6387, 2013.
“Neuropeptide secreted from a pacemaker activates neurons to control a
rhythmic behavior,”
H. Wang, K. Girskis, T. Janssen, J. P. Chan, K. Dasgupta, J. A. Knowles,
L. Schoofs, D. Sieburth,
Current Biology,
23(9):746-754, 2013.
“The connectome of a decision-making neural network,”
T. A. Jarrell, Y. Wang, A. E. Bloniarz, C. A. Brittin, M. Xu, J. N. Thomson,
D. G. Albertson, D. H. Hall, S. W. Emmons,
Science,
337(6093):437-444, 2012.
“Structural Properties of the Caenorhabditis elegans
Neuronal Network,”
L. R. Varshney, B. L. Chen, E. Paniagua, D. H. Hall, D. B. Chklovskii,
PLoS Computational Biology,
7(2):e1001066, 2011.
“Lethargus is a Caenorhabditis elegans sleep-like state,”
D. M. Raizen, J. E. Zimmerman, M. H. Maycock, U. D. Ta, Y. J. You,
M. V. Sundaram, A. I. Pack,
Nature,
451(7178):569-572, 2008
“Insulin, cGMP, and TGF-beta signals regulate food intake and quiescence in
C. elegans:
a model for satiety,”
Y. J. You, J. Kim, D. M. Raizen, L. Avery,
Cell Metabolism,
7(3):249-257, 2008.
“Associative learning on a continuum in evolved dynamic neural networks,”
E. J. Izquierdo, I. Harvey, R. D. Beer,
Adaptive Behavior,
16(6):361-384, 2008.
“Neuropeptidergic signaling in the nematode Caenorhabditis elegans,”
S. J. Husson, I. Mertens, T. Janssen, M. Lindemans, L. Schoofs,
Progress in Neurobiology,
82(1):33-55, 2007.
The Neurobiology of C. Elegans,
Eric Aamodt (editor),
Academic Press, 2006.
“Wiring optimization can relate neuronal structure and function,”
B. L. Chen, D. H. Hall, D. B. Chklovskii,
Proceedings of the National Academy of Science,
103(12):4723-4728, 2006.
“Genomics in C. elegans:
So many genes, such a little worm,”
L. W. Hillier, A. Coulson, J. I. Murray, Z. Bao, J. E. Sulston,
R. H. Waterston,
Genome Research,
15(12):1651-1660, 2005.
“Neuronal substrates of complex behaviors in C. elegans,”
M. de Bono, A. V. Maricq,
Annual Review of Neuroscience,
28:451-501, 2005.
“Stochastic formulation for a partial neural circuit of C. elegans,”
Y. Iwasaki, S. Gomi,
Bulletin of Mathematical Biology,
66(4):727-743, 2004.
“Database of Synaptic Connectivity of C. elegans for Computation,”
K. Oshio, Y. Iwasaki, S. Morita, Y. Osana, S. Gomi, E. Akiyama, K. Omata,
K. Oka, K. Kawamura,
Technical Report of CCeP (Cybernetic Caenorhabditis elegans Program),
Keio Future, Number 3, Keio University, 2003.
“Behavioral plasticity in C. elegans:
paradigms, circuits, genes,”
O. Hobert,
Journal of Neurobiology,
54(1):203-223, 2003.
“The Immunoglobulin Superfamily Protein SYG-1 Determines the Location of
Specific Synapses in C. elegans,
K. Shen, C. I. Bargmann
Cell,
112(5):619-630, 2003.
“The Genome Sequence of Caenorhabditis briggsae:
A Platform for Comparative Genomics,”
L. D. Stein, Z. Bao, D. Blasiar, T. Blumenthal, M. R. Brent, N. Chen,
A. Chinwalla, L. Clarke, C. Clee, A. Coghlan, A. Coulson, P. D’Eustachio,
D. H. A Fitch, L. A. Fulton, R. E. Fulton, S. Griffiths-Jones,
T. W. Harris, L. W. Hillier, R. Kamath, P. E. Kuwabara, E. R. Mardis,
M. A. Marra, T. L. Miner, P. Minx, J. C. Mullikin, R. W. Plumb, J. Rogers,
J. E. Schein, M. Sohrmann, J. Spieth, J. E. Stajich, C. Wei, D. Willey,
R. K. Wilson, R. Durbin, R. H. Waterston,
Public Library of Science, Biology,
1(2):166-192, 2003.
“From gene to identified neuron to behaviour in Caenorhabditis
elegans,”
C. H. Rankin,
Nature Reviews Genetics,
3(8):622-630, 2002.
“The fundamental role of pirouettes in Caenorhabditis elegans
chemotaxis,”
J. T. Pierce-Shimomura, T. M. Morse, S. R. Lockery,
Journal of Neuroscience,
19(21):9557-9569, 1999.
“Genome sequence of the nematode C. elegans:
A platform for investigating biology,”
The C. elegans Sequencing Consortium,
Science,
282(5396):2012-2018, 1998.
“Interacting genes required for pharyngeal excitation by motor neuron MC in
Caenorhabditis elegans,”
D. M. Raizen, R. Y. Lee, L. Avery,
Genetics,
141(4):1365-1382, 1995.
“The structure of the nervous system of the nematode C. elegans,”
J. G. White, E. Southgate, J. N. Thomson, S. Brenner,
Philosophical Transactions of the Royal Society, B,
314(1165):1-340, 1986.
- [the worm has rote dynamics for movement]
-
Kato and others did principal component analysis on 109 segmented head
neurons and detected a 3-d smoothly connected manifold for run-and-turn
actions (move forward, move back, turn).
“While isolated motor actions can be correlated with activities of
neuronal networks, an unresolved problem is how the brain assembles
these activities into organized behaviors like action sequences. Using
brain-wide calcium imaging in Caenorhabditis elegans, we show
that a large proportion of neurons across the brain share information by
engaging in coordinated, dynamical network activity. This brain state
evolves on a cycle, each segment of which recruits the activities of
different neuronal sub-populations and can be explicitly mapped, on
a single trial basis, to the animals’ major motor commands. This
organization defines the assembly of motor commands into a string
of run-and-turn action sequence cycles, including decisions between
alternative behaviors. These dynamics serve as a robust scaffold for
action selection in response to sensory input. This study shows that
the coordination of neuronal activity patterns into global brain dynamics
underlies the high-level organization of behavior.”
From:
“Global brain dynamics embed the motor command sequence of Caenorhabditis
elegans,”
S. Kato, H. S. Kaplan, T. Schrödel, S. Skora, T. H. Lindsay,
E. Yemini, S. Lockery, M. Zimmer,
Cell,
163(3):656-669, 2015.
- [why connectomes may be so hard to figure out]
-
The connectome is multiple maps smooshed into one map,
and the system switches between them in a multiplexed way depending
delicately on various circumstances.
“In this Historical Perspective, we ask what information is needed
beyond connectivity diagrams to understand the function of nervous
systems. Informed by invertebrate circuits whose connectivities are
known, we highlight the importance of neuronal dynamics and
neuromodulation, and the existence of parallel circuits. The
vertebrate retina has these features in common with invertebrate
circuits, suggesting that they are general across animals. Comparisons
across these systems suggest approaches to study the functional
organization of large circuits based on existing knowledge of small
circuits.
[...]
For C. elegans, although we know what most of the neurons
do, we do not know what most of the connections do, we do not know
which chemical connections are excitatory or inhibitory, and we
cannot easily predict which connections will be important from the
wiring diagram. The problem is illustrated most simply by the
classical touch-avoidance circuit. The PLM sensory neurons in the
tail are solely responsible for tail touch avoidance. PLM forms 31
synapses with 11 classes of neurons, but only one of those targets
is essential for the behavior—an interneuron called PVC that is
connected to PLM by just two gap junctions and two chemical synapses.
An even greater mismatch between the number of synapses and their
importance in behavior is seen in the avoidance of head touch, where
just two of 58 synapses (again representing gap junctions) are the
key link between the sensory neurons (ALM and AVM) and the essential
interneuron (AVD). This general mismatch between the number of
synapses and apparent functional importance has applied wherever
C. elegans circuits have been defined. As a result, early
guesses about how information might flow through the wiring diagram
were largely incorrect.
[...]
Clearly, the wiring diagram could generate hypotheses to test, but
solving a circuit by anatomical inspection alone was not successful.
We believe that anatomical inspection fails because each wiring
diagram encodes many possible circuit outcomes.
[...]
Notably, many electrical synapses connect neurons with different
functions. Almost invariably, the combination of electrical and
chemical synapses create ‘parallel pathways’, that is to say,
multiple pathways by which neuron 1 can influence neuron 2. For
example, in the [Cancer borealis crab] STG, the PD neuron
inhibits the IC neuron through chemical synapses but also can
influence the IC neuron via the electrical synapse from LP to IC.
Parallel pathways such as those in the STG can be viewed as degenerate,
as they create multiple mechanisms by which the network output can
be switched between states. A simulation study shows a simplified
five-cell network of oscillating neurons coupled with electrical
synapses and chemical inhibitory synapses. The f1 and f2 neurons
are connected reciprocally by chemical inhibitory synapses, as are
the s1 and s2 neurons. This type of wiring configuration, called a
half-center oscillator, often but not universally causes the neurons
to be rhythmically active in alternation. In this example, two
different oscillating rhythms are generated, one fast and one slow.
The hub neuron at the center of the network can be switched between
firing in time with the fast f1 and f2 neurons to firing in time
with the slow s1 and s2 neurons by three entirely different circuit
mechanisms: changing the strength of the electrical synapses,
changing the strength of the synapses between f1 and s1 onto the
hub neuron, and changing the strength of the reciprocal inhibitory
synapses linking f1 to f2 and s1 to s2 in the half-center oscillators.
[...]
To understand information flow, there will be no substitute for recording
activity.
[...]
Superimposed on the fast chemical synapses and electrical synapses in
the wiring diagram are the neuromodulators—biogenic amines (serotonin,
dopamine, norepinephrine and histamine) and neuropeptides (dozens to
hundreds, depending on species). These molecules are often released
together with a fast chemical transmitter near a synapse, but they can
diffuse over a greater distance. Modulators also can be released from
neuroendocrine cells that do not make defined synaptic contacts or can be
delivered as hormones through the circulation. As a result, the targets
of neuromodulation are invisible to the electron microscope. Signaling
primarily through G protein–regulated biochemical processes rather than
through ionotropic receptors, neuromodulators change neuronal functions
over seconds to minutes, or even hours.
[...]
Every synapse and every neuron in the STG is subject to modulation;
the connectivity diagram by itself only establishes potential circuit
configurations, whose availability and properties depend critically on
which of many neuromodulators are present at a given moment.”
From:
“From the connectome to brain function,”
C. I. Bargmann, E. Marder,
Nature Methods,
10(6):483-490, 2013.
See also:
“Should I Stay or Should I Go:
Neuromodulators of Behavioral States,”
A. F. Schier,
Cell,
154(5):955-956, 2013.
“Beyond the connectome:
How neuromodulators shape neural circuits,”
C. I. Bargmann,
BioEssays,
34(6):458-465, 2012.
“Neuromodulation of neuronal circuits:
back to the future,”
E. Marder,
Neuron,
76(1):1-11, 2012.
“Beyond the wiring diagram:
signalling through complex neuromodulator networks,”
Philosophical Transactions of the Royal Society B:
Biological Sciences,
V. Brezina,
365(1551):2363-2374, 2010.
“Cellular, synaptic and network effects of neuromodulation,”
E. Marder, V. Thirumalai,
Neural Networks,
15(4-6):479-493, 2002.
- [worm recognizing itself]
-
This is known for another worm, which predates on C. elegans.
“Self-recognition is observed abundantly throughout the natural
world, regulating diverse biological processes. Although ubiquitous,
often little is known of the associated molecular machinery, and so
far, organismal self-recognition has never been described in nematodes.
We investigated the predatory nematode Pristionchus pacificus and,
through interactions with its prey, revealed a self-recognition mechanism
acting on the nematode surface, capable of distinguishing self-progeny
from closely related strains. We identified the small peptide SELF-1,
which is composed of an invariant domain and a hypervariable C
terminus, as a key component of self-recognition. Modifications to
the hypervariable region, including single-amino acid substitutions,
are sufficient to eliminate self-recognition. Thus, the P. pacificus
self-recognition system enables this nematode to avoid
cannibalism while promoting the killing of competing nematodes.”
From:
“Small peptide-mediated self-recognition prevents cannibalism in
predatory nematodes,”
J. W. Lightfoot, M. Wilecki, C. Rödelsperger,
E. Moreno, V. Susoy, H. Witte, R. J. Sommer,
Science,
364(6435):86-89, 2019.
- [oxytocin and mating in C. elegans]
-
It’s not actually oxytocin, but nematocin, a homologue of oxytocin.
“Many biological functions are conserved, but the extent to which
conservation applies to integrative behaviors is unknown. Vasopressin and
oxytocin neuropeptides are strongly implicated in mammalian reproductive
and social behaviors, yet rodent loss-of-function mutants have relatively
subtle behavioral defects. Here we identify an oxytocin/vasopressin-like
signaling system in Caenorhabditis elegans, consisting of a peptide and two
receptors that are expressed in sexually dimorphic patterns. Males lacking
the peptide or its receptors perform poorly in reproductive behaviors,
including mate search, mate recognition, and mating, but other sensorimotor
behaviors are intact. Quantitative analysis indicates that mating motor
patterns are fragmented and inefficient in mutants, suggesting that
oxytocin/vasopressin peptides increase the coherence of mating behaviors.
These results indicate that conserved molecules coordinate diverse
behavioral motifs in reproductive behavior.”
From:
“Oxytocin/vasopressin-related peptides have an ancient role in reproductive
behavior,”
J. L. Garrison, E. Z. Macosko, S. Bernstein, N. Pokala, D. R. Albrecht,
C. I. Bargmann,
Science,
338(6106):540-543, 2012.
- [learning in C. elegans]
-
Has been found in C. elegans. This may be based on a form of
Spike-timing-dependent plasticity (STDP), where,
if an input spike to a neuron tends, on average, to occur immediately
before that neuron’s output spike, then that particular input is made
somewhat stronger.
“Learning is critical for survival as it provides the capacity to
adapt to a changing environment. At the molecular and cellular level,
learning leads to alterations within neural circuits that include synaptic
rewiring and synaptic plasticity. These changes are mediated by signalling
molecules known as neuromodulators. One such class of neuromodulators
are neuropeptides, a diverse group of short peptides that primarily act
through G protein-coupled receptors. There has been substantial progress
in recent years on dissecting the role of neuropeptides in learning
circuits using compact yet powerful invertebrate model systems. We
will focus on insights gained using the nematode Caenorhabditis
elegans, with its unparalleled genetic tractability, compact nervous
system of ∼300 neurons, high level of conservation with mammalian
systems and amenability to a suite of behavioural analyses. Specifically,
we will summarise recent discoveries in C. elegans on the role
of neuropeptides in non-associative and associative learning.”
From:
“The role of neuropeptides in learning:
Insights from C. elegans,”
N. De Fruyt, A. J. Yu, C. H. Rankin, I. Beets, Y. L. Chew,
The International Journal of Biochemistry & Cell Biology,
125:105801, 2020.
“The nematode, Caenorhabditis elegans (C. elegans), is an
organism useful for the study of learning and memory at the molecular,
cellular, neural circuitry, and behavioral levels. Its genetic
tractability, transparency, connectome, and accessibility for in vivo
cellular and molecular analyses are a few of the characteristics that
make the organism such a powerful system for investigating mechanisms of
learning and memory. It is able to learn and remember across many sensory
modalities, including mechanosensation, chemosensation, thermosensation,
oxygen sensing, and carbon dioxide sensing. C. elegans habituates
to mechanosensory stimuli, and shows short-, intermediate-, and long-term
memory, and context conditioning for mechanosensory habituation. The
organism also displays chemotaxis to various chemicals, such as diacetyl
and sodium chloride. This behavior is associated with several forms of
learning, including state-dependent learning, classical conditioning,
and aversive learning. C. elegans also shows thermotactic
learning in which it learns to associate a particular temperature with
the presence or absence of food. In addition, both oxygen preference
and carbon dioxide avoidance in C. elegans can be altered by
experience, indicating that they have memory for the oxygen or carbon
dioxide environment they were reared in.”
From:
“Caenorhabditis elegans Learning and Memory,”
J. S. H. Wong, C. H. Rankin,
Neuroscience,
doi:10.1093/acrefore/9780190264086.013.282,
2019.
Even a single cell can ‘learn,’ as was shown by the sensor neuron for
temperature, which acted as a gate depending on what temperature the worm
was trained on.
“Neural plasticity, the ability of neurons to change their properties
in response to experiences, underpins the nervous system’s capacity to
form memories and actuate behaviors. How different plasticity mechanisms
act together in vivo and at a cellular level to transform
sensory information into behavior is not well understood. We show that
in Caenorhabditis elegans two plasticity mechanisms—sensory
adaptation and presynaptic plasticity—act within a single cell to
encode thermosensory information and actuate a temperature preference
memory. Sensory adaptation adjusts the temperature range of the sensory
neuron (called AFD) to optimize detection of temperature fluctuations
associated with migration. Presynaptic plasticity in AFD is regulated
by the conserved kinase nPKCε and transforms thermosensory
information into a behavioral preference. Bypassing AFD presynaptic
plasticity predictably changes learned behavioral preferences without
affecting sensory responses. Our findings indicate that two distinct
neuroplasticity mechanisms function together through a single-cell logic
system to enact thermotactic behavior.”
From:
“Integration of Plasticity Mechanisms within a Single Sensory Neuron of
C. elegans Actuates a Memory,”
J. D. Hawk, A. C. Calvo, P. Liu, A. Almoril-Porras, A. Aljobeh,
M. L. Torruella-Suárez, I. Ren, N. Cook, J. Greenwood, L. Luo,
Z.-W. Wang, A. D. T. Samuel, D. A. Colón-Ramos
Neuron,
97(2):356-367.e4, 2018.
See also:
“An elegant mind:
Learning and memory in Caenorhabditis elegans,”
E. L. Ardiel, C. H. Rankin,
Learning & Memory,
17:191-201, 2010.
- [over half a billion years
of makeshift neural engineering—545 million years]
-
Porifera (sponges) may be the earliest form of animal since they lack
neurons (and even a digestive system). (But then it’s also possible that
they, and all other animals, descend from another life-form, now lost,
which had neurons, but then they lost them.) Perhaps then came Ctenophora
(comb jellies), since they have a completely alien nervous system. (But
the order might be the exact opposite.) Then came Cnidaria (jellyfish,
corals, and anemones), Placozoa (very simple multicellular amoeba-like
animals), and eventually Bilateria, which contains everything else,
including us.
Dawn of the Neuron:
The Early Struggles to Trace the Origin of Nervous Systems,
Michel Anctil,
McGill-Queen’s University Press, 2015.
See also:
“Convergent evolution of bilaterian nerve cords,”
J. M. Martin-Duran, K. Pang, A. Børve, H. L. Semmler, A. Furu,
J. T. Cannon, U. Jondelius, A. Hejnol,
Nature,
553(7686):45-50, 2018.
“Evolutionary origin of synapses and neurons — Bridging the gap,”
P. Burkhardt, S. G. Sprecher,
BioEssays,
39(10):1700024, 2017.
“Elements of a ‘nervous system’ in sponges,”
S. P. Leys,
Journal of Experimental Biology,
218(4):581-591, 2015.
“Did the ctenophore nervous system evolve independently?”
J. F. Ryan,
Zoology,
117(4):225-226, 2014.
“Evolution:
Ctenophore Genomes and the Origin of Neurons,”
H. Marlow, D. Arendt,
Current Biology,
24(16):R757-R761, 2014.
“Evolution of the Brain:
From Behavior to Consciousness in 3.4 Billion Years,”
J. J. Oró
Neurosurgery,
54(6):1287-1297, 2004.
- [fly’s brain more sensitive to visual cues when flying...]
-
When a fruit fly begins to fly its visual cells immediately ramp up
their activity. Their responses to visual motion roughly doubled.
“We developed a technique for performing whole-cell patch-clamp
recordings from genetically identified neurons in behaving
Drosophila. We focused on the properties of visual interneurons
during tethered flight, but this technique generalizes to different cell
types and behaviors. We found that the peak-to-peak responses of a class
of visual motion-processing interneurons, the vertical-system visual
neurons (VS cells), doubled when flies were flying compared with when
they were at rest. Thus, the gain of the VS cells is not fixed, but is
instead behaviorally flexible and changes with locomotor state. Using
voltage clamp, we found that the passive membrane resistance of VS cells
was reduced during flight, suggesting that the elevated gain was a result
of increased synaptic drive from upstream motion-sensitive inputs. The
ability to perform patch-clamp recordings in behaving Drosophila
promises to help unify the understanding of behavior at the gene, cell
and circuit levels.”
From:
“Active flight increases the gain of visual motion processing in
Drosophila,”
G. Maimon, A. D. Straw, M. H. Dickinson,,
Nature Neuroscience,
13(3):393-399, 2010.
- [C. elegans has circadian rhythms]
-
“Endogenous circadian rhythms have been demonstrated in several
model systems, including mammals, insects, and fungi, among many
others. Cycles in behavior, physiology and gene expression have also
been reported in the nematode Caenorhabditis elegans, although
limited by experimental conditions. Here we report the application
of a luciferase-based reporter to investigate circadian regulation in
C. elegans. Our study demonstrates entrainable, endogenous,
and temperature-dependent circadian rhythms in gene expression as well
as part of the pathway for synchronization. Our results represent an
innovative approach for the study of long-term gene expression in real
time in this system, opening the way for novel research in neuroscience
and molecular pathways in general, including the precise determination
of its elusive circadian clock.”
From:
“Circadian rhythms identified in Caenorhabditis elegans by
in vivo long-term monitoring of a bioluminescent reporter,”
M. E. Goya, A. Romanowski, C. S. Caldart, C. Y. Bénard,
D. A. Golombek,
Proceedings of the National Academy of Science,
113(48)E7837-E7845, 2016.
- [explaining the worm’s train traffic metaphor]
-
Unlike today’s supercomputers, the dynamics of the worm’s model are hard
to figure out because signal traffic along nerves can change the
network, and thus alter the shape of future traffic. In a sense, nerves
can choose—and their choices can change how much they link, or even if
they link. Each nerve might receive many signals from the nerves that
link to it, but it’s fussy about whether it’s going to send signals to
the nerves it links to. To decide that, it’s computing—based on its
current state and how many signals it gets. So while it might get lots of
signals, it might send none on—or it might even send signals on without
getting any at all. Further, a nerve might start paying more (or less)
attention to any nerves that happen to feed it just before (or perhaps
even after) it decides to feed other nerves. Also, a signal traveling
down a nerve may trigger changes in the nerve, or nearby nerves, or even,
after a lag, distant nerves. And how those nerves change may depend on
how the nerves were acting recently, or perhaps even deep in the past.
So its past can affect its future.
Note: ‘Tracks are choosy’: Neuron’s are calculators and deciders. To decide
whether to fire, they add together
excitatory and inhibitory signals and compare the sum against their thresholds
(a voltage of about -55 to -65 millivolts).
Presynaptic
signals may be excitatory (increase the chance of postsynaptic neural
activation) or inhibitory (decrease that chance).
Note: ‘A track might send trains without getting any at all:’
Some neurons are pacemakers. They emit signals without any excitation.
In C. elegans one such pacemaker is motor neuron MC in the worm’s
throat.
Note: ‘A track might start paying more (or less) attention to any tracks
that feed it just before (or after) it decides to feed other tracks.’:
Neurons obey STDP (Spike Timing Dependent Plasticity). If a presynaptic
signal arrives just before a linked neuron’s postsynaptic activation,
the connection is strengthened, else weakened.
“Spike timing-dependent plasticity:
a Hebbian learning rule,”
N. Caporale, Y. Dan,
Annual Review of Neuroscience,
31:25-46, 2008.
Note: ‘A train traveling down a track can trigger changes in
switches—and not just on that track, but even on distant tracks. How
those switches change may depend on how the switches were set recently,
or perhaps even deep in the past.’:
Neurons may emit any of dozens (or perhaps hundreds) of neuromodulators,
which diffuse through the nervous tissue, altering the behavior
(particularly the synaptic behavior) of other neurons, and that may
depend on what has happened to the worm recently, or even during
development.
(See ‘a worm’s eye view’ and ‘multiple maps smooshed’ above for references.)
- [the hippocampus and memory]
-
The hippocampus is just part of a large circuit that processes and
encodes information from many sensory modalities (all of them, except
for smell) before storage. Damage to any part of it will result in some
form of amnesia.
“Charting the acquisition of semantic knowledge in a case of developmental
amnesia,”
J. M. Gardiner, K. R. Brandt, A. D. Baddeley, F. Vargha-Khadem, M. Mishkin,
Neuropsychologia,
46(11):2865-2868, 2008.
“Human memory development and its dysfunction after early hippocampal
injury,”
M. de Haan, M. Mishkin, T. Baldeweg, F. Vargha-Khadem,
Trends in Neurosciences,
29(7):374-381, 2006.
One theory posits that the hippocampus has evolved for dead-reckoning
(or navigation in the real world), not episodic memory (or navigation
in a remembered world).
Rhythms of the Brain,
György Buzsáki,
Oxford University Press, 2006.
- [...don’t so much remember as reconstruct]
-
The idea is that the hippocampus seems to be vital not just to storing
memories but to building memories in the first place in at least the sense
that it appears to be necessary to the laying down of the spatial context
in which new experiences can be bound together as a single memory.
“Patients with hippocampal amnesia cannot imagine new experiences,”
D. Hassabis, D. Kumaran, S. D. Vann, E. A. Maguire,
Proceedings of the National Academy of Science,
104(5):1726-1731, 2007.
- [artificial hippocampus]
-
“Columnar Processing in Primate pFC:
Evidence for Executive Control Microcircuits,”
I. Opris, R. E. Hampson, G. A. Gerhardt, T. W. Berger, S. A. Deadwyler,
Journal of Cognitive Neuroscience,
24(12):2334-2347, 2012.
“Closing the loop for memory prosthesis:
detecting the role of hippocampal neural ensembles using nonlinear models,”
R. E. Hampson, D. Song, R. H. Chan, A. J. Sweatt, M. R. Riley,
A. V. Goonawardena, V. Z. Marmarelis, G. A. Gerhardt, T. W. Berger,
S. A. Deadwyler,
IEEE transactions on neural systems and rehabilitation
engineering,
20(4):510-525, 2012.
“A cortical neural prosthesis for restoring and enhancing memory,”
T. W. Berger, R. E. Hampson, D. Song, A. Goonawardena,
V. Z. Marmarelis, S. A. Deadwyler,
Journal of Neural Engineering,
8(4):046017, 2011.
“Neuronal network morphology and electrophysiology
of hippocampal neurons cultured on surface-treated multielectrode arrays,”
W. V. Soussou, G. J. Yoon, R. D. Brinton, T. W. Berger,
IEEE Transactions on Biomedical Engineering,
54(7):1309-20, 2007.
“Implantable Biomimetic Electronics as Neural Prostheses for Lost Cognitive
Function,”
T. W. Berger, J. J. Granacki, V. Z. Marmarelis,
A. R. Tanguay, Jr., S. A. Deadwyler, G. A. Gerhardt,
Proceedings of the 2005 IEEE International Joint Conference
on Neural Networks,
2005, pages 3109-3114.
“Brain-implantable biomimetic electronics as neural prosthetics,”
T. W. Berger, J. J. Granacki, V. Z. Marmarelis, B. J. Sheu, A. R. Tanguay, Jr.,
Proceedings of the IEEE EMBS Conference,
2003, pages 1956-1959.
See also:
Learning in Silicon:
A Neuromorphic Model of the Hippocampus,
John Vernon Arthur,
doctoral thesis,
University of Pennsylvania, 2006.
His artificial hippocampus is quite small.
It has only 1,024 pyramidal neurons, each with 21 plastic synapses.
However, it still learns and recalls patterns.
- [potential barriers to brain augmentation]
-
There are many potential barriers, depending on factors it’s difficult
to estimate accurately until we actually start doing it. First,
we’ll closely watch anything intended not to remove disability but to
add capability. Whatever the proposed change, someone somewhere won’t
like it. Further, for a long time, brain implants will be expensive,
unreliable, and risky. So most of us will prefer external enhancements
unless surgery is unavoidable. Even further, surgical complications rise
with implant size. Today’s chips also have limited power-to-weight ratios,
and they have major heat-dissipation problems. Those limits will likely
keep near-future brain mods small. So if we implant, it’ll only be to
put in the equivalent of an input-output jack. We’ll wear the actual
devices instead of implanting them. And we’ll wirelessly network them
to our brain’s jack. That way we can upgrade them more easily.
Possibly our need to implant will fall as we learn more about the
brain. Once we can both read its state accurately and write new states
into it without surgery—perhaps with a headband or circlet—competition
will disfavor implants. Then again, some of the above mental enhancements
may turn out to be physically, computationally, or economically
impossible. Or enhancer drugs or brain-reader headbands may prove less
expensive, or less risky. Plus, we’re going to make mistakes, especially
early on. So, just like Henry M., some of us are going to be the new
walking wounded. Our alterations will irreparably damage some of us,
just as plastic surgery gone wrong today damages some of us. Lastly,
it’s one thing to experiment on a cat; it’s quite another thing to
experiment on one that has been augmented enough to beg you to stop.
- [mapping the brain]
-
With the latest microscopes we can see individual neurons, although
currently those cells need to be outside the brain for that level
of imaging.
“Subdiffraction Multicolor Imaging of the Nuclear Periphery with 3D
Structured Illumination Microscopy,”
L. Schermelleh, P. M. Carlton, S. Haase, L. Shao, L. Winoto,
P. Kner, B. Burke, C. M. Cardoso, D. A. Agard, M. G Gustafsson,
H. Leonhardt, J. W. Sedat,
Science,
320(5881):1332-1336, 2008.
“STED microscopy reveals that synaptotagmin remains clustered after synaptic
vesicle exocytosis,”
K. I. Willig, S. O. Rizzoli, V. Westphal, R. Jahn, S. W. Hell,
Nature,
440(7086):935-939, 2006.
- [optogenetics and seeing into the brain]
-
The latest, and best way we have to see into the living brain is via
optogenetics, a way to transfect light-sensitive genes to specific neurons,
then modulate those specific neurons individually from outside the brain in
the living organism at the millisecond timescale. There’s been nothing like
it before—not drugs, not surgery, not electrodes, not tissue staining, not
brain scanning—to give us this level of insight and engineering into the
behavior of the dynamic brain.
“Optogenetics,”
K. Deisseroth,
Nature Methods,
8(1):26-29, 2011.
“Optogenetic probing of functional brain circuitry,”
J. J. Mancuso, J. Kim, S. Lee, S. Tsuda, N. B. H. Chow, G. J. Augustine,
Experimental Physiology,
96(1):26-33, 2010.
“Next-Generation Optical Technologies for Illuminating Genetically
Targeted Brain Circuits,”
K. Deisseroth, G. Feng, A. K. Majewska, G. Miesenbock, A. Ting,
M. J. Schnitzer,
Journal of Neuroscience,
26(41):10380-10386, 2006.
- [altering brain activity]
-
We have many mechanisms to do so reliably during surgery; the question
is what can we do reliably and safely today without surgery? One of
the most promising avenues today is Transcranial Magnetic Stimulation.
“A comprehensive review of the effects of rTMS on motor cortical
excitability and inhibition,”
P. B. Fitzgerald, S. Fountain, Z. J. Daskalakis,
Clinical Neurophysiology,
117(12):2584-2596, 2006.
Handbook of Transcranial Magnetic Stimulation,
Alvaro Pascual-Leone, Nick Davey, John Rothwell, Eric M. Wassermann,
and Besant K. Puri (editors),
Hodder Arnold, 2002.
- [artificial telepathy]
-
With button electrodes on the throat we can already detect tremors in
the neurons controlling our vocal cords when we’re speaking quietly,
or even just reading silently. The sensors relay those subvocal neural
signals to a digital signal processor and then to a software package
trained to recognize certain signals as simple words.
“Articulatory Feature Classification using Surface Electromyography,”
S.-C. Jou, L. Maier-Hein, T. Schultz, A. Waibel,
Proceedings of the IEEE International Conference on Acoustics, Speech
and Signal Processing 2006,
1:I-I, 2006.
“Small-vocabulary speech recognition using surface electromyography,”
B. J. Betts, K. Binsted, C. Jorgensen,
Interacting with Computers,
18(6):1242-1259, 2006.
- [no need for sleep]
-
Today one such drug, modafinil (marketed as Provigil, Modavigil, Vigicer,
and Alertec), is already in wide use, particularly in the military,
and it has now spread to academia as well. Drugs to modulate pain,
stress, attentiveness, and personality are also already on the market.
“Modafinil’s effects on
simulator performance and mood in pilots during 37 h without sleep,”
J. A. Caldwell, J. L. Caldwell, J. K. Smith, D. L. Brown,
Aviation, Space, and Environmental Medicine,
75(9):777-784, 2004.
“The cognitive-enhancing properties of modafinil
are limited in non-sleep-deprived middle aged volunteers,”
D. C. Randall, N. L. Fleck, J. M. Schneerson, S. E. File,
Pharmacology Biochemistry and Behavior,
77(3):547-555, 2004.
“Modafinil affects mood, but not cognitive function,
in healthy young volunteers,”
D. C. Randall, J. M. Schneerson, K. K. Plaha, S. E. File,
Human Psychopharmacology,
18(3):163-173, 2003.
“First evidence of a delay-dependent working memory-enhancing effect of
modafinil in mice,”
D. Beracochea, B. Cagnard, A. Celerier, J. le Merrer, M. Peres, C. Pierard,
Neuroreport,
12(2):375-378, 2001.
“Effects of modafinil on attentional processes
during 60 hours of sleep deprivation,”
P. Stivalet, D. Esquivie, P. A. Barraud, D. Leifflen, C. Raphel,
Human Psychopharmacology-Clinical and Experimental,
13(7):501-507, 1998.
“Modafinil during 64 hr of sleep deprivation:
Dose-related effects on fatigue, alertness, and cognitive performance,”
J. V. Baranski, C. Cian, D. Esquivie, R. A. Pigeau, C. Raphel,
Military Psychology,
10(3):173-193, 1998.
“Self-monitoring cognitive performance during sleep deprivation:
Effects of modafinil, d-amphetamine and placebo,”
J. V. Baranski, R. A. Pigeau,
Journal of Sleep Research,
6(2):84-91, 1997.
“Modafinil, d-amphetamine and placebo during 64 hours of sustained
mental work. I. Effects on mood, fatigue, cognitive performance and body
temperature,”
R. Pigeau, P. Naitoh, A. Buguet, C. McCann,
Journal of Sleep Research,
4(4):212-228, 1995.
- [induced feelings]
-
Even today, using electromagnetic helmets, and without surgery,
Persinger claims to be able to stimulate feelings of déjà
vu, the urge to laugh, and the numinous feeling of a religious
experience. Other researchers have triggered out-of-body experiences
during brain surgery.
The field, which is tiny at present, is coming to be known as neurotheology.
Most of the more outré work is being done by Michael Persinger and
his students at Laurentian University. His results are tantalizing but the
field would have to grow quite substantially before its claimed results
can be considered reliable.
“The spiritual brain:
Selective cortical lesions modulate human self transcendence,”
C. Urgesi, S. M. Aglioti, M. Skrap, F. Fabbro,
Neuron,
65(3):309-319, 2010.
“Visualizing Out-of-Body Experience in the Brain,”
D. De Ridder, K. Van Laere, P. Dupont, T. Menovsky, P. Van de Heyning,
New England Journal of Medicine,
357(18):1829-1833, 2007.
“Video Ergo Sum:
Manipulating Bodily Self-Consciousness,”
B. Lenggenhager, T. Tadi, T. Metzinger, O. Blanke,
Science,
317(5841):1096-1099, 2007.
“Experimental facilitation of the sensed presence is predicted by the
specific patterns of the applied magnetic fields, not by suggestibility:
re-analyses of 19 experiments,”
L. S. St-Pierre, M. A. Persinger,
International Journal of Neuroscience,
116(9):1079-1096, 2006.
“Contribution of religiousness in the prediction and interpretation of
mystical experiences in a sensory deprivation context:
activation of religious schemas,”
P. Granqvist, M. Larsson,
Journal of Psychology,
140(4):319-327, 2006.
“Stimulating illusory own-body perceptions,”
O. Blanke, S. Ortigue, T. Landis, M. Seeck,
Nature,
419(6904):269-270, 2002.
- [synthetic brains]
-
In sum, we each appear to be carrying around a network in our heads.
We’ve made up a bunch of words, like ‘intelligence,’ for a bundle of
its traits, just as we’ve made up a bunch of words, like ‘life,’ for a
bundle of other traits. But that doesn’t mean that we understand them.
Nor need it mean that we can control them. It’s thus not unlikely that
we’ll one day build synthetic brains. But we may not have much idea how
to get them to do what we want. Even after we figure out how to make them
biddable, making them sane might well be hard. Then once we do manage
to make them sane, they may be no more able to explain how they work
than we can explain how we work. However, no matter how alien they may
be underneath, we’re likely to project our passions onto their actions,
just as we do with pets and babies—and even, it seems, ourselves.
- [thinking with the world]
-
To imagine that all that distinguishes us from other life-forms on this
planet is our stupendous brain is to mistake the instrument for the
symphony. A single neuron is complicated, but it isn’t complicated enough
to produce the wide variety of behaviors that we are capable of. However,
any one neuron, call it neuronA, might be directly linked to another. Yet
the behavior of the second neuron, call it neuronB, often isn’t simply
driven by neuronA’s behavior. More often, both influence each other.
NeuronA is, in some sense, using neuronB to help it decide something
(and vice versa). That’s a bit like autocatalysis. NeuronA may also
be linked, but more indirectly, to many other neurons via short or long
loops (neuronA links to another, which links to another, and so on until
we get back to neuronA). If so, then neuronA is in some sense using all
the neurons that it’s linked to to help it decide something (and vice
versa). That’s a bit like synergy. Any of those neurons might also link
outside the brain to an effector, for example, a muscle in the body. If
neuronA sends a signal to a muscle to contract, the muscle tenses. But
at some point the muscle will relax, which might send a signal back, via
another neuron chain to neuronA. That’s a bit like stigmergy. Further,
that muscle tension might result in change in the world outside the body.
In turn, that change might then result in further change inside the brain
that’s detecting the outside world, and thus it might lead to change in
neuronA. That’s also a bit like stigmergy. Yet further, other beings may
affect change in the same outside world based on the change just made,
which may lead back to neuronA, and so on.
Each such recursion means longer and longer time delays. Direct
neuron-to-neuron links might trigger in perhaps four thousandths of a
second. Long chains of neurons might introduce delays of up to perhaps a
twentieth of a second. Having to first affect the world outside the brain
(a muscle, say) then get feedback from that effect might take perhaps
two tenths of a second. Having to first alter the world outside the body
then have it respond might take anywhere from seconds to years. Each
recursive loop is longer and longer, and more and more memory effects
might return to affect any neuron at any stage along the loop. In this
sense, no neuron alone decides anything; it uses all other neurons in
the brain, plus all the body’s effectors, plus all the body’s sensors,
plus the whole world that its body is situated in to decide whether to
fire or not. In brief, we all use the world to think with.
- [predicting the future]
-
Our swarm’s growing computational power means that those of us alive
today form one of the first generations with more data and more analytic
power than ever in history. But our swarm’s growing opacity means that
we’re also one of the first generations with no idea what our future
will be. Our past innovators had no large credit pool to pay for tools or
labor. They had no large team of research talent to work with. Nor could
they draw on the published work of millions of professional thinkers. They
had no automated factories to turn an idea into reality. Nor did
they have global distribution mechanisms to move it. Nor global stock
markets to fuel it. Nor global competitors slobbering after the next
big idea. All that is completely new. Up to even our recent past, and
for millennia before, almost nothing changed. Oh, we made babies, and
we died, and famine and war and disease came and went, and so on, so in
that sense we changed. But our tools didn’t change much. For example,
in 1753, the colonies that were just about to become the United States
got their first steam engine. It was smuggled from Britain to New Jersey
that year. Folks living only two days’ walk away still hadn’t heard of
it 17 years later. Until recently, major changes—printing presses,
steam engines, banks, bonds, anesthetics, antibiotics, and such—were
rare. Thus, the bundle of tools supporting farming took us about 2,500
years to figure out. Then that bundle took another 5,500 years to seep
from Iraq to Britain. Our numbers there then went up fourfold in 400
years. Much later, it took us centuries to figure out the synergetic
bundle of tools supporting industry. Then that bundle took over a century
to seep from Britain back to Iraq. Our numbers there then went up tenfold
in 100 years. So, until recently, it was easy to guess what our future
would be. It would be much the same as our past. A Nostradamus could
natter on about future wars and plagues and such, and no one would
complain that he somehow never mentioned penicillin.
- [the network decides, not the individual]
-
“The Surprising Power of Neighborly Advice,”
D. T. Gilbert, M. A. Killingsworth, R. N. Eyre, T. D. Wilson,
Science,
323(5921):1617-1619, 2009.
“Leading the Herd Astray:
An Experimental Study of Self-Fulfilling
Prophecies in an Artificial Cultural Market,”
M. J. Salganik, D. J. Watts,
Social Psychology Quarterly,
71(4):338-355, 2008.
“Emergent Processes in Group Behavior,”
R. L. Goldstone, M. E. Roberts, T. M. Gureckis,
Current Directions in Psychological Science,
17, 10-15, 2008.
“Influentials, Networks, and Public Opinion Formation,”
D. J. Watts, P. S. Dodds,
Journal of Consumer Research,
34(4):441-458, 2007.
“Experimental Study of Inequality and Unpredictability in an Artificial
Cultural Market,”
M. J. Salganik, P. S. Dodds, D. J. Watts,
Science,
331(5762):854-856, 2006.
“Experience-Based Discrimination:
Classroom Games,”
R. G. Fryer, J. K. Goeree, C. A. Holt,
The Journal of Economic Education,
36(2):160-170, 2005.
- [airy nothings...]
-
“Theseus [To Hippolyta]: [...] The poet’s eye, in fine frenzy rolling, /
Doth glance from heaven to earth, from earth to heaven; /
And as imagination bodies forth /
The forms of things unknown, the poet’s pen /
Turns them to shapes and gives to airy nothing /
A local habitation and a name.”
A Midsummer Night’s Dream,
William Shakespeare,
Act V, Scene I.
- [love looks not with the eyes...]
-
“Helena: [...] Love looks not with the eyes but with the mind. /
And therefore is winged Cupid painted blind. /
Nor hath Love’s mind of any judgment taste— /
Wings and no eyes figure unheedy haste. /
And therefore is Love said to be a child, /
Because in choice he is so oft beguiled.”
A Midsummer Night’s Dream,
William Shakespeare,
Act I, Scene I.
- [there’s no art...]
-
“Duncan: There’s no art /
To find the mind’s construction in the face. /
He was a gentleman on whom I built /
An absolute trust.”
Macbeth,
William Shakespeare,
Act I, Scene IV.
- [the unlikelihood of predictability of ai’s timeline]
-
Of 95 predictions made between 1950 and the present, there is no
difference between predictions made by experts and non-experts, and
there is a strong bias towards predicting the arrival of human-level AI
as between 15 and 25 years from the time the prediction was made.
“How We’re Predicting AI—or Failing To,”
S. Armstrong, K. Sotala,
in:
Beyond AI:
Artificial Dreams,
Jan Romportl, Pavel Ircing, Eva Zackova, Michal Polak, and Radek Schuster
(editors),
University of West Bohemia, 2012, pages 52-75.
- [the singularity]
-
The core idea behind the singularity is not just acceleration but
subsequent impenetrability. Science fiction has a long history of
imagining various kinds of acceleration but the singularity became
common currency in science fiction only recently, notably in the works
of Vernor Vinge, Greg Egan, Michael Flynn, Greg Bear, Marc Stiegler,
James Patrick Kelly, Ted Chiang, Charles Stross, Corey Doctorow, and
Harry Harrison and Marvin Minsky.
The first mention of the idea, but not the name, goes back to John W. Campbell
in his 1932 short story “The Last Evolution.”
Vernor Vinge sketched the idea in a short story in 1966, and first used the
term in a 1986 novel.
Marooned in Realtime,
1986,
Tor, Reprint Edition, 2004.
“Bookworm Run,”
The Collected Stories of Vernor Vinge,
Orb, 2002.
“The Last Evolution,”
The Best of John W. Campbell,
Nelson Doubleday, Inc., 1976.
Vinge’s first non-fiction singularity paper traces non-fiction
references back to speculations in the 1950s and 1960s by John von
Neumann and I. J. Good.
“What is the Singularity?”
V. Vinge,
Whole Earth Review,
Winter, 1993.
Eric Drexler then got the nanotechnology (molecular robotics) ball rolling.
Engines of Creation:
The Coming Era of Nanotechnology,
K. Eric Drexler,
Anchor Press/Doubleday, 1986.
Hans Moravec is the chief representative of the robotic side of the
singularity cascade. He was also the first reliable estimator of the
raw computing power that might be needed to simulate the human brain.
Robot:
Mere Machine to Transcendent Mind,
Hans Moravec,
Oxford University Press, 1999.
Mind Children:
The Future of Robot and Human Intelligence,
Hans Moravec,
Harvard University Press, 1988.
Ray Kurzweil is an inventor and also a writer on the singularity. Today
he is its chief representative.
The Singularity Is Near:
When Humans Transcend Biology,
Ray Kurzweil,
Viking, 2005.
A few other futurist writers have commented as well, notably Damien Broderick.
The Spike:
How Our Lives Are Being Transformed by Rapidly Advancing Technologies,
Damien Broderick,
Forge, 2001.
And Danny Hillis has sketched some of the computer technology that may be
needed.
“Close to the Singularity,”
Danny Hillis,
in:
The Third Culture:
Beyond the Scientific Revolution,
John Brockman (editor),
Simon & Schuster, 1995.
A small movement has sprung up in the high-technology world around
the idea of the singularity—either of accelerating change, of an
intelligence explosion, or of an impenetrale future—populated mainly by
adherents calling themselves extropians, transhumanists, or posthumanists.
“The Singularity:
A Philosophical Analysis,”
D. J. Chalmers,
Journal of Consciousness Studies,
17(9-10):7-65, 2010.
Some authors have argued that the idea of using continual exponential
change in computers is a poor model of how fast our future is likely to
actually change:
“The Singularity Isn’t Near,”
P. G. Allen, M. Greaves,
Technology Review,
(Global edition),
October 12, 2011.
Future Hype:
The Myths of Technological Change,
Bob Seindensticker,
Berrett-Koehler, 2006.
“The Singularity Myth,”
T. Modis,
Technological Forecasting & Social Change,
73(2):104-112, 2006.
“A Possible Declining Trend for Worldwide Innovation,”
J. Huebner,
Technological Forecasting & Social Change,
72(8):988-995, 2005.
“Moore’s law, Artificial Evolution, and the Fate of Humanity,”
D. R. Hofstadter,
in:
Perspectives on Adaptation in Natural and Artificial Systems,
Lashon Booker, Stephanie Forrest, Melanie Mitchell, and Rick Riolo (editors),
Oxford University Press, 2005, pages 163-198.
At least one author has argued that the singularity would be a disaster:
“Why the Future Doesn’t Need Us,”
W. Joy,
Wired,
8(04):238-262, 2000.
- [over 75 percent decline in insects over last 27 years to 2017...]
-
“Global declines in insects have sparked wide interest among scientists,
politicians, and the general public. Loss of insect diversity and abundance
is expected to provoke cascading effects on food webs and to jeopardize
ecosystem services. Our understanding of the extent and underlying causes
of this decline is based on the abundance of single species or taxonomic
groups only, rather than changes in insect biomass which is more relevant
for ecological functioning. Here, we used a standardized protocol to
measure total insect biomass using Malaise traps, deployed over 27 years in
63 nature protection areas in Germany (96 unique location-year
combinations) to infer on the status and trend of local entomofauna. Our
analysis estimates a seasonal decline of 76%, and mid-summer decline of 82%
in flying insect biomass over the 27 years of study. We show that this
decline is apparent regardless of habitat type, while changes in weather,
land use, and habitat characteristics cannot explain this overall decline.
This yet unrecognized loss of insect biomass must be taken into account in
evaluating declines in abundance of species depending on insects as a food
source, and ecosystem functioning in the European landscape.”
From:
“More than 75 percent decline over 27 years in total flying
insect biomass in protected areas,”
C. A. Hallmann, M. Sorg, E. Jongejans, H. Siepel, N. Hofland, H. Schwan,
W. Stenmans, A. Müller, H. Sumser, T. Hörren, D. Goulson,
H. de Kroon,
PLoS ONE,
12(10):e0185809, 2017.
- [29 percent decline in birds over last 49 years to 2019...]
-
“Species extinctions have defined the global biodiversity crisis, but
extinction begins with loss in abundanceof individuals that can result in
compositional and functional changes of ecosystems. Using multiple
and independent monitoring networks, we report population losses across much
of the North American avifaunaover 48 years, including once-common species
and from most biomes. Integration of range-wide population trajectories and
size estimates indicates a net loss approaching 3 billion birds, or 29% of
1970 abundance. A continent-wide weather radar network also reveals a
similarly steep decline in biomass passage of migrating birds over a recent
10-year period. This loss of bird abundance signals an urgent need to
address threats to avert future avifaunal collapse and associated loss of
ecosystem integrity, function, and services.”
From:
“Decline of the North American avifauna,”
K. V. Rosenberg, A. M. Dokter, P. J. Blancher, J. R. Sauer,
A. C. Smith, P. A. Smith, J. C. Stanton, A. Panjabi,
L. Helft, M. Parr, P. P. Marra,
Science,
366(6461):120-124, 2019.
- [by 2030, about 1.7 billion...]
-
“Over the next 25 years, the world will see the addition of nearly one
million km2 of urban area.” [About 247 million acres.]
“Global urbanization:
can ecologists identify a sustainable way forward,”
R. I. McDonald,
Frontiers in Ecology and the Environment,
6(2):99-104, 2008.
- [by 2030, half again as much food...]
-
“Food Security — the Challenge of Sustainability in a Changing World,”
a talk by John Beddington,
Chief Scientific Adviser to Her Majesty’s Government,
given in London on October 28th, 2008.
Figures are from the United Nations Food and Agriculture Organization,
the International Energy Agency, and the International Food Policy
Research Institute.
- [potential impact of climate change]
-
“Radically Rethinking Agriculture for the 21st Century Export,”
N. V. Fedoroff, D. S. Battisti, R. N. Beachy, P. J. M. Cooper,
D. A. Fischhoff, C. N. Hodges, V. C. Knauf, D. Lobell, B. J. Mazur,
D. Molden, M. P. Reynolds, P. C. Ronald, M. W. Rosegrant, P. A. Sanchez,
A. Vonshak, J. K. Zhu,
Science,
327(5967):833-834, 2010.
“Deforestation driven by urban population growth and agricultural trade in
the twenty-first century,”
R. S. DeFries, T. Rudel, M. Uriarte, M. Hansen,
Nature Geoscience,
3(3):178-181, 2010.
Agricultural Adaptation to Climate Change in the Developing World:
What will it Cost?
International Food Policy Research Institute, 2009.