Topic: population vector correlation

When rodents learn to navigate in a new environment, high place field density emerge at reward locations, fields elongate against the trajectory, and individual fields change spatial selectivity or drift while demonstrating stable behavior. Why place fields demonstrate these characteristic phenomena during learning remains elusive. We develop a model that optimizes for a normative goal, which is to maximize cumulative rewards. Place fields are modelled using Gaussian basis functions to represent spatial information of an environment, and directly synapse to an actor-critic for policy learning

The fundamental assumption of a functional system for encoding spatial information lies in the stability and preserved integrity of neural representations. In extreme cases, such as psychotic states in schizophrenia, there is a disruption of this stability, often resulting in the fusion of distinct neural representations. In this study, both familiar (F) and novel (N) linear mazes were utilized to assess the quality of neural coding across various time scales in 3-6 months old rats (N = 12). Lower (0.1 mg/kg) and higher doses (0.15 mg/kg) of MK-801 were administered. We observed disrupted spat