S. Zomorodi, B. Knauer, Y. Brahimi, A. Reboreda, M. Yoshida, Z. Tiganj. Intrinsic persistent firing in CA1 encodes elapsed time across behaviorally relevant scales. Hippocampus, in press.Paper
S. Singh Maini, R. Goldstone, Z. Tiganj. High Volatility and Action Bias Distinguish LLMs from Humans in Group Coordination. arXiv preprint, 2026.Project pagePaper
A. Bajaj*, D. M. Mistry*, S. Singh Maini, Y. Aggarwal, Z. Tiganj. Beyond Semantics: How Temporal Biases Shape Retrieval in Transformer and State-Space Models. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2026.Paper
S. Duncan, S. Rehman, V. Segen, I. Choi, S. Lawrence, O. Kalani, L. Gold, L. Goldman, S. Ramlo, K. Stickel, D. Layfield, T. Wolbers, Z. Tiganj, E. L. Newman. rTCT: Rodent Triangle Completion Task to Facilitate Reverse Translational Study of Path Integration. Hippocampus, 36(3), e70090, 2026.Paper
A. Bajaj*, D. M. Mistry*, S. Singh Maini*, Y. Aggarwal, B. Dickson, Z. Tiganj. Temporal Dependencies in In-Context Learning: The Role of Induction Heads. arXiv preprint, 2026.Paper
A. Bajaj, Z. Tiganj. Who Do LLMs Trust? Human Experts Matter More Than Other LLMs. arXiv preprint, 2026.Paper
E. L. Newman, I. Mashanova-Galikova, Z. Tiganj, C. Lever. Boundary Vector Cells Encode a Future-Biased Spectrum of Positions in the Rat. bioRxiv preprint, 2026. Under revision.Paper
B. Dickson, Z. Tiganj. Gradual Forgetting: Logarithmic Compression for Extending Transformer Context Windows. NeurIPS Workshop CogInterp: Interpreting Cognition in Deep Learning Models, 2025.Project pagePaper
S. Singh Maini, Z. Tiganj. Reinforcement Learning with Adaptive Temporal Discounting. Reinforcement Learning Journal, 6, pp. 2667–2684, 2025. Reinforcement Learning Conference (RLC), 2025.Paper
C. Sanders, B. Dickson, S. Singh Maini, R. Nosofsky, Z. Tiganj. Vision-language models learn the geometry of human perceptual space. arXiv preprint, 2025.Paper
J. Mochizuki-Freeman, S. Zomorodi, S. Singh Maini, Z. Tiganj. Computational Model for Episodic Timeline Based on a Spectrum of Synaptic Decay Rates. Cognitive Computational Neuroscience (CCN) Conference, 2025.Project pagePaper
V. Segen, M. R. Kabir, A. Streck, J. Slavik, W. Glanz, M. Butryn, E. Newman, Z. Tiganj*, T. Wolbers*. Path integration impairments reveal early cognitive changes in subjective cognitive decline. Science Advances, 11(36), eadw6404, 2025.Project pagePaper
B. Dickson*, S. Singh Maini*, C. Sanders, R. Nosofsky, Z. Tiganj. Comparing perceptual judgments in large multimodal models and humans. Behavior Research Methods, 57, article 203, 2025.Project pagePaper
B. Dickson, J. Mochizuki-Freeman, M. R. Kabir, Z. Tiganj. Time-local transformer. Computational Brain & Behavior, pp. 1–13, 2025.Project pagePaper
D. M. Mistry, A. Bajaj, Y. Aggarwal, S. Singh Maini, Z. Tiganj. Emergence of Episodic Memory in Transformers: Characterizing Changes in Temporal Structure of Attention Scores During Training. NAACL 2025.Paper
M. R. Kabir, J. Mochizuki-Freeman, Z. Tiganj. Deep reinforcement learning with time-scale invariant memory. The 39th Annual AAAI Conference on Artificial Intelligence, 2025.Project pagePaper
A. Alipour, T. James, J. Brown, Z. Tiganj. Self-supervised learning of scale-invariant neural representations of space and time. Journal of Computational Neuroscience, 53, pp. 131–162, 2025.Paper
D. Ćavar, Z. Tiganj, L. V. Mompelat, B. Dickson. Computing Ellipsis Constructions: Comparing Classical NLP and LLM Approaches. In Proceedings of the Society for Computation in Linguistics, pp. 217–226, 2024.Paper
J. Mochizuki-Freeman, M. R. Kabir, M. Gulecha, Z. Tiganj. Incorporating a cognitive model for evidence accumulation into deep reinforcement learning agents. CogSci 2024, Conference Proceedings.Paper
S. Sheybani, S. Singh Maini, A. Dendukuri, Z. Tiganj, L. B. Smith. ModelVsBaby: A developmentally motivated benchmark of out-of-distribution object recognition. PsyArXiv preprint, 2024. Presented at ICDL, 2024.Project pagePaper
J. Mochizuki-Freeman, M. R. Kabir, M. Gulecha, Z. Tiganj. Geometry of abstract learned knowledge in deep RL agents. NeurIPS Workshop on Symmetry and Geometry in Neural Representations (Proceedings track, Oral presentation), 2023.Paper
S. Sheybani, H. Hansaria, J. N. Wood, L. B. Smith, Z. Tiganj. Curriculum learning with infant egocentric videos. NeurIPS (Spotlight paper), 2023.Project pagePaper
Z. Tiganj. Accumulating evidence by sampling from temporally organized memory. Learning & Behavior, 51, pp. 351–352, 2023.Paper
J. Mochizuki-Freeman, S. Singh Maini, Z. Tiganj. Characterizing neural representation of cognitively-inspired deep RL agents during an evidence accumulation task. NeurIPS MemARI workshop, 2022 & International Joint Conference on Neural Networks (IJCNN), 2023.Project pagePaper
S. Singh Maini, J. Mochizuki-Freeman, C. S. Indi, B. G. Jacques, P. B. Sederberg, M. W. Howard, Z. Tiganj. Constructing compressed number lines of latent variables using a cognitive model of memory and deep neural networks. NeurIPS MemARI workshop, 2022 & International Joint Conference on Neural Networks (IJCNN), 2023.Project pagePaper
S. Singh Maini, L. Labuzienski, S. Gulati, Z. Tiganj. Comparing Impact of Time Lag and Item Lag in Relative Judgment of Recency. CogSci 2022, Conference Proceedings.Paper
B. Jacques, Z. Tiganj, A. Sarkar, M. W. Howard, P. B. Sederberg. A deep convolutional neural network that is invariant to time rescaling. ICML, 2022.Project pagePaper
Z. Tiganj*, I. Singh*, Z. Esfahani, M. W. Howard. Scanning a compressed ordered representation of the future. Journal of Experimental Psychology: General, 151(12), 3082–3096, 2022. doi: 10.1037/xge0001243.Paper
B. Jacques, Z. Tiganj, M. W. Howard, P. B. Sederberg. DeepSITH: Efficient learning via decomposition of what and when across time scales. NeurIPS, 2021.Project pagePaper
Z. Tiganj, W. Tang, M. W. Howard. A computational model for simulating the future using a memory timeline. CogSci 2021, Conference Proceedings.Paper
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. Hippocampus 30(12), pp. 1332–1346, 2020.Paper
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. PNAS 117(33), pp. 20274–20283, 2020.Paper
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.Paper
Z. Tiganj, S. J. Gershman, P. B. Sederberg, and M. W. Howard. Estimating scale-invariant future in continuous time. Neural Computation, 31(4), pp. 681–709, 2019.Paper
Y. Liu, Z. Tiganj, M. E. Hasselmo, and M. W. Howard. A neural microcircuit model for a scalable scale‐invariant representation of time. Hippocampus, 29(3), pp. 260–274, 2019.Paper
M. W. Howard, A. Luzardo and Z. Tiganj. Evidence accumulation in a Laplace domain decision space. Computational Brain and Behavior, 1(3–4), pp. 237–251, 2018.Paper
I. Singh*, Z. Tiganj*, and M. W. Howard. *co-first authors. Is working memory stored along a logarithmic timeline? Converging evidence from neuroscience, behavior and models. Neurobiology of Learning and Memory, 153A, pp. 104–110, 2018.Paper
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), pp. 935–950, 2018.Paper
Z. Tiganj, K. H. Shankar and M. W. Howard. Power-law temporal discounting over a logarithmically compressed timeline for scale invariant reinforcement learning. Proceedings of the NIPS 2017 Workshop on Cognitively Informed Artificial Intelligence.Paper
Z. Tiganj, J. Kim, M. W. Jung and M. W. Howard. Sequential firing codes for time in rodent medial prefrontal cortex. Cerebral Cortex, 27(12), pp. 5663–5671, 2017.Paper
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, 2017.Paper
Z. Tiganj, K. H. Shankar and M. W. Howard. Neural and computational arguments for memory as a compressed supported timeline. CogSci 2017, Conference Proceedings.Paper
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, 2016.Paper
D. Salz, Z. Tiganj, S. Khasnabish, A. Kohley, D. Sheehan, M. W. Howard, and H. B. Eichenbaum. Time cells in hippocampal area CA3. Journal of Neuroscience, 36(28), pp. 7476–7484, 2016.Paper
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.Paper
Z. Tiganj, M. E. Hasselmo, and M. W. Howard. A simple biophysically plausible model for long time constants in single neurons. Hippocampus, 25(1), pp. 27–37, 2015.Paper
M. W. Howard, C. MacDonald, Z. Tiganj, K. H. Shankar, Q. Du, M. E. Hasselmo and H. B. Eichenbaum. A unified mathematical framework for coding time, space, and sequences in the hippocampal region. Journal of Neuroscience, 34(13), pp. 4692–4707, 2014.Paper
Z. Tiganj, S. Chevallier and Eric Monacelli. Influence of extracellular oscillations on neural communication: a computational perspective. Frontiers in Computational Neuroscience, 8, 2014.Paper
Z. Tiganj and M. Mboup. Neural spike sorting using iterative ICA and deflation based approach. Journal of Neural Engineering, 9(6), p. 066002, 2012.Paper
Z. Tiganj, M. Mboup, S. Chevallier and E. Kalunga. Online frequency band estimation and change-point detection. IEEE International Conference on Systems and Computer Science, pp. 1–6, 2012.Paper
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, 8(6), p. 066014, 2011.Paper
Z. Tiganj and M. Mboup. Deflation technique for neural spike sorting in multi-channel recordings. IEEE International Workshop on Machine Learning for Signal Processing, pp. 1–6, Beijing, China, 2011.Paper
Z. Tiganj, M. Mboup, C. Pouzat and L. Belkoura. An algebraic method for eye blink artifacts detection in single channel EEG recordings. IFMBE Proceedings, 28(6), pp. 175–178, 2010.Paper
Z. Tiganj and M. Mboup. Spike detection and sorting: combining algebraic differentiations with ICA. Lecture Notes in Computer Science, 5441, pp. 475–482, 2009.Paper
Cognitive Science and Artificial Intelligence Lab resources
