State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions
A grand challenge in modern neuroscience is to bridge the gap between the detailed mapping of microscale neural circuits and mechanistic understanding of cognitive functions. While extensive knowledge exists about neuronal connectivity and biophysics, how these low-level phenomena eventually produce abstract behaviors remains largely unresolved. Here, we propose that a model based on State Space Models, an emerging class of deep learning architectures, can be a potential biological model for analysis. We suggest that the differential equations governing elements in a State Space Model are conceptually consistent with the dynamics of biophysical processes, while the model offers a scalable framework to build on the dynamics to produce emergent behaviors observed in experimental neuroscience. We test this model by training a network employing a diagonal state transition matrix on temporal discrimination tasks with reinforcement learning. Our results suggest that neural behaviors such as time cells naturally emerge from two fundamental principles: optimal pre-configuration and rotational dynamics. These features are shown mathematically to optimize history compression, and naturally generate structured temporal dynamics even prior to training, mirroring recent findings in biological circuits. We show that learning acts primarily as a selection mechanism that fine-tunes these pre-configured oscillatory modes, rather than constructing temporal codes de novo. The model can be readily scaled to abstract cognitive functions such as event counting, supporting the use of State Space Models as a computationally tractable framework for understanding neural activities.