Research interests

Broadly speaking, my research aims to understand the computational basis of cognition. More precisely, I am using data from human behavioral experiments to investigate computational properties of cognitive processes, especially perception, memory, learning and higher cognition. Behavioral data motivate the development of detailed hypotheses on how neural dynamics gives rise to these processes. I am using data from electrophysiology (primarily activity of individual neurons recorded in vivo) to test and refine these hypotheses and build neural-level models of cognitive processes. Finally I am using these models to build artificial systems that aim to mimic the human ability to learn and reason. The goal is to build agents that can learn in an unsupervised manner from temporal and spatial regularities in the real world. Specifically, domains of application include reinforcement learning, spatial navigation, natural language processing and computer vision.


  • I. M. Bright*, M. L. R. Meister*, N. A. Cruzado, Z. Tiganj, M. W. Howard, E. A. Buffalo. A temporal record of the past with a spectrum of time constants in the monkey entorhinal cortex. Under review. bioRxiv, 688341. PDF

  • N. Cruzado, Z. Tiganj, S. Brincat, E. K. Miller, M. W. Howard. Conjunctive representation of what and when in monkey hippocampus and lateral prefrontal cortex during an associative memory task. Under review. bioRxiv, 709659. PDF

  • Z. Tiganj, N. Cruzado and M. W. Howard. Towards a neural-level cognitive architecture: modeling behavior in working memory tasks with neurons. CogSci 2019, Conference proceedings. PDF

  • Z. Tiganj, S. J. Gershman, P. B. Sederberg and M. W. Howard. Estimating scale-invariant future in continuous time. Neural Computation, 31(4), p. 681-709, 2019. PDF

  • Y. Liu, Z. Tiganj, M. E. Hasselmo, and M. W. Howard. Biological simulation of scale-invariant time cells. Hippocampus, 1-15, 2018. PDF

  • M. W. Howard, A. Luzardo and Z. Tiganj. Evidence accumulation in a Laplace domain decision space. Computational brain and behavior, 1, 237-251, 2018. PDF

  • Z. Tiganj, N. Cruzado and M. W. Howard. Constructing neural-level models of behavior in working memory tasks. Conference on Cognitive Computational Neuroscience, 2018. PDF

  • I. Singh*, Z. Tiganj* and M. W. Howard. Is working memory stored along a logarithmic timeline? Converging evidence from neuroscience, behavior and models. Neurobiology of Learning and Memory, 153A, p. 104-110, 2018. PDF

  • Z. Tiganj, J. A Cromer, J. E Roy, E. K Miller and M. W Howard. Compressed timeline of recent experience in monkey lPFC. Journal of Cognitive Neuroscience, 30(7), p. 935-950, 2018. PDF

  • Z. Tiganj, J. Kim, M. W. Jung and M. W. Howard. Sequential firing codes for time in rodent medial prefrontal cortex. Cerebral Cortex, volume 27, number 12, Pages 5663--5671, 2017. PDF

  • B. Podobnik, M. Jusup, Z. Tiganj, W. X. Wang, J. M. Buldu, and H. E. Stanley. Biological conservation law as an emerging functionality in dynamical neuronal networks. PNAS, p. 201705704, 2017. PDF

  • Z. Tiganj, K. H., Shankar and M. W. Howard. Neural and computational arguments for memory as a compressed supported timeline. CogSci 2017, Conference proceedings. PDF

  • Z. Tiganj, K. H., Shankar and M. W. Howard. Scale invariant value computation for reinforcement learning in continuous time. AAAI Spring Symposium Series - Science of Intelligence: Computational Principles of Natural and Artificial Intelligence, Technical report, 2016. PDF

  • D. Salz, Z. Tiganj, S. Khasnabish, A. Kohley, D. Sheehan, M. W. Howard, and H. Eichenbaum. Time cells in hippocampal area CA3. Journal of Neuroscience, Volume 36, Number 28, Pages 7476--7484, 2016. PDF

  • M. W. Howard, K. H. Shankar and Z. Tiganj. Efficient neural computation in the Laplace domain. Proceedings of the NIPS 2015 workshop on Cognitive Computation. PDF

  • Z. Tiganj, M. E. Hasselmo, and M. W. Howard. A simple biophysically plausible model for long time constants in single neurons. Hippocampus, Volume 25, Number 1, Pages 27-37, 2015. PDF

  • M. W. Howard, C. J. MacDonald, Z. Tiganj, K. H. Shankar, Q. Du, M. E. Hasselmo and H. Eichenbaum. A unified mathematical framework for coding time, space, and sequences in the hippocampal region. Journal of Neuroscience, Volume 34, Number 13, Pages 4692-4707, 2014. PDF

  • Z. Tiganj, S. Chevallier and Eric Monacelli. Influence of extracellular oscillations on neural communication: a computational perspective. Frontiers in Computational Neuroscience, Volume. 8, 2014. PDF

  • Z. Tiganj, M. Mboup, S. Chevallier and E. Kalunga. Online frequency band estimation and change-point detection. International Conference on Systems and Computer Science, Pages: 1-6, 2012. PDF

  • Z. Tiganj and M. Mboup. Neural spike sorting using iterative ICA and deflation based approach. Journal of Neural Engineering, Volume 9, Number 6, Pages 066002, 2012. PDF

  • Z. Tiganj and M. Mboup. Deflation technique for neural spike sorting in multi-channel recordings. IEEE conference Machine Learning for Signal Processing, Pages: 1-6, 2011. PDF

  • Z. Tiganj and M. Mboup. A non-parametric method for automatic neural spikes clustering based on the non-uniform distribution of the data. Journal of Neural Engineering, Volume 8, Number 6, Pages 066014, 2011. PDF

  • Z. Tiganj, M. Mboup, C. Pouzat and L. Belkoura. An Algebraic Method for Eye Blink Artifacts Detection in Single Channel EEG Recordings. International converence on Biomagnetism, IFMBE Proceedings, Volume 28, Part 6, Pages 175-178, 2010. PDF

  • Z. Tiganj and M. Mboup. Spike Detection and Sorting: Combining Algebraic Differentiations with ICA. Independent Component Analysis and signal separation, Lecture Notes in Computer Science, Volume 5441, Pages 475-482, 2009. PDF