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This paper demonstrates how State Space Models can naturally generate neural phenomena like time cells and oscillatory behaviors through structured multi-timescale bases, offering a computationally tractable bridge between microscale circuits and abstract cognitive functions. By showing that these temporal dynamics emerge prior to training, the work provides a plausible framework for understanding how low-level biological mechanisms scale to complex tasks such as event counting.
State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions
arXiv:2507.13638v4 Announce Type: replace-cross Abstract: A grand challenge in modern neuroscience is bridging the gap between the microscale neural circuits and cognitive functions. While extensive knowledge exists about neuronal connectivity and biophysics, how low-level phenomena give rise to abstract behaviors remains largely unresolved. Here, we propose that a mathematical framework based on State Space Models offers mathematical sufficiency and computational plausibility to generate response profiles that resemble time cells, ramping activity, and oscillatory responses. Our results suggest that such behaviors can emerge from a structured multi-timescale complex rotational basis, which enables optimized history compression and naturally generates structured temporal dynamics even prior to training, mirroring recent findings in biological circuits. Learning exploits and adapts to the pre-existing temporal basis for specific tasks. We show that the model can readily scale to abstract cognitive functions such as event counting, supporting its use as a computationally tractable framework for understanding neural activities.
Abstract:A grand challenge in modern neuroscience is bridging the gap between the microscale neural circuits and cognitive functions. While extensive knowledge exists about neuronal connectivity and biophysics, how low-level phenomena give rise to abstract behaviors remains largely unresolved. Here, we propose that a mathematical framework based on State Space Models offers mathematical sufficiency and computational plausibility to generate response profiles that resemble time cells, ramping activity, and oscillatory responses. Our results suggest that such behaviors can emerge from a structured multi-timescale complex rotational basis, which enables optimized history compression and naturally generates structured temporal dynamics even prior to training, mirroring recent findings in biological circuits. Learning exploits and adapts to the pre-existing temporal basis for specific tasks. We show that the model can readily scale to abstract cognitive functions such as event counting, supporting its use as a computationally tractable framework for understanding neural activities.
Sen Lu and Xiaoyu Zhang contributed equally. Wei D. Lu is the corresponding author. 5 figures are included in 15 pages
Neurons and Cognition (q-bio.NC); Machine Learning (cs.LG)
arXiv:2507.13638 [q-bio.NC]
(or arXiv:2507.13638v4 [q-bio.NC] for this version)
https://doi.org/10.48550/arXiv.2507.13638 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 262 words
