Abstract
The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates.
Highlights
Spatial cognition requires long-term neural representations of the spatiotemporal properties of the environment [1]
A high-level brain function based upon the ability to elaborate mental representations of the environment supporting goal-oriented navigation
The hippocampal formation and the prefrontal cortex are two neural substrates likely involved in navigation planning
Summary
Spatial cognition requires long-term neural representations of the spatiotemporal properties of the environment [1] These representations are encoded in terms of multimodal descriptions of the animal-environment interaction during active exploration. Exploiting these contextual representations (e.g. through rewardbased learning) can produce goal-oriented behavior under different environmental conditions and across subsequent visits to the environment. Route-based representations encode sequences of place-action-place associations independently from each other, which does not guarantee optimal goal-oriented behavior (e.g. in terms of capability of either finding the shortest pathway or solving detour tasks). Behavioral and neurophysiological data suggest the coexistence of multiple memory systems that, by being instrumental in the encoding of routes, topological maps and metrical information, cooperate to subserve goaloriented navigation planning [9]
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