Research interests

I want to understand the brain by building it. My research interests are in the fields of cognitive and computational neuroscience and artificial intelligence. I am using mathematical models and computer simulations inspired by cognitive and neural data to construct and test hypotheses on how neural dynamics gives rise to cognitive processes. I am trying to integrate computational models of perception, memory, learning and higher cognition into a unified cognitive and cognitively inspired AI framework.

Publications

  • Articles

    • Tiganj, Z., Gershman, S.J., Sederberg, P.B. and Howard, M.W. Estimating scale-invariant future in continuous time. In review. (pdf)

    • Singh, I., Tiganj, Z. (co-first), and Howard, M.W. Is working memory stored along a logarithmic timeline? Converging evidence from neuroscience, behavior and models. Invited for special issue at Neurobiology of Learning and Memory. In review. (pdf)

    • Y. Liu, Z. Tiganj, M. E. Hasselmo, and M. W. Howard. Biological simulation of scale-invariant time cells. In review. 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, In press. 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 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, In press. 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 International Workshop on 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

  • Conference abstracts

    • Z. Tiganj, J. A Cromer, J. E Roy, E. K Miller and M. W Howard. Memory of what happened when as a compressed timeline in monkey lPFC. SFN, Washington DC, US, 2017.

    • N. Cruzado, Z. Tiganj, S. Brincat, E. Miller, M. Howard. Compressed Temporal Representation During Visual Paired Associate Task in Monkey PFC and Hippocampus. Context and Episodic Memory Symposium (CEMS), Philadelphia, US, 2017.

    • M. Howard, Z. Tiganj and K. Shankar. A mathematical framework for flexible and efficient neural cognitive computation. 15th Neural Computation and Psychology Workshop, Philadelphia, US, 2016.

    • Z. Tiganj, K. Shankar, M. Hasselmo and M. Howard. Representations of space and time in the brain. 49th Annual Meeting of the Society of Mathematical Psychology, New Brunswick, US, 2016.

    • Z. Tiganj, J. M. Di Lascio, P. B. Sederberg, M. J. Kahana, D. S. Rizzuto and M. W. Howard. Identifying the neural processes that govern contextual encoding and contextual retrieval. Context and Episodic Memory Symposium (CEMS), Philadelphia, US, 2016.

    • M. W. Howard, K. H. Shankar and Z. Tiganj. Computing with a scale-invariant representation of time, space, and number. Cosyne, Salt Lake City, US, 2016.

    • Z. Tiganj, J. M. Di Lascio, J. F. Burke, Y. Ezzyat, P. B. Sederberg, M. J. Kahana, D. Rizzuto and M. W. Howard. Using a detailed computational model of behavior to decompose the subsequent memory effect in free recall SFN, Chikago US, 2015.

    • Z. Tiganj, J. Kim, M. W. Jung and M. W. Howard. Neural activity in the medial prefrontal cortex changes across a range of time scales. SFN, Washington DC, US, 2014.

    • Z. Tiganj, S. Nakamura, I. Singh and M. W. Howard. Does a slowly-changing representation of what and when exist in multiple brain regions? Cognitive Neuroscience (CNS) meeting, Boston, US, 2014.

    • Z. Tiganj, K. H. Shankar and M. W. Howard. Computational model of exponentially decaying persistent firing for encoding stimulus history. SFN, San Diego, US, 2013.

    • Z. Tiganj, K. H. Shankar and M. W. Howard. Encoding the Laplace transform of stimulus history using mechanisms for persistent firing. BMC Neuroscience, Volume 14(Suppl 1), Page 356, 2013; Computational Neuroscience (CNS) conference, Paris, France, 2013. PDF

    • Z. Tiganj, K. H. Shankar and M. W. Howard. Computational model of decaying persistent firing for encoding stimulus history. Context and Episodic Memory Symposium (CEMS), Philadelphia, US, 2013.

    • Z. Tiganj, S. Chevallier and E. Monacelli. On the role of extracellular oscillations for efficient neural synchronization. Forum of Neuroscience (FENS), Barcelona, Spain, 2012.

    • Z. Tiganj, M. Mboup and C. Pouzat. Eye blink artifacts detection in single channel EEG recordings. Consciousness and its Measures Workshop, Limassol, Cyprus, 2009.
Zoran Tiganj

Contact information

Address:
Theoretical Cognitive Neuroscience Lab
Center for Memory and Brain
2 Cummington Mall
Boston University
Boston, MA 02215
E-Mail: zoran (dot) tiganj (at ) gmail (dot) com
Web: http://people.bu.edu/zorant
Google Scholar: Zoran Tiganj
GitHub: zorant
Phone: (1) 857 241 7430