State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions
The research proposes that State Space Models (SSMs) can naturally generate time cells and oscillations, key for cognition, by using a structured rotational basis. The model shows these temporal patterns emerge without explicit training when initialized correctly. This framework is scalable to abstract tasks like event counting, suggesting SSMs are a computationally viable way to model neural activity.