4 research outputs found
Spatial Learning and Action Planning in a Prefrontal Cortical Network Model
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