Learning predictive cognitive maps with spiking neurons during behavior and replays.

ABSTRACT: The hippocampus has been proposed to encode environments using a representation that contains predictive information about likely future states, called the successor representation. However, it is not clear how such a representation could be learned in the hippocampal circuit. Here, we propose a plasticity rule that can learn this predictive map of the environment using a spiking neural network. We connect this biologically plausible plasticity rule to reinforcement learning, mathematically and numerically showing that it implements the TD-lambda algorithm. By spanning these different levels, we show how our framework naturally encompasses behavioral activity and replays, smoothly moving from rate to temporal coding, and allows learning over behavioral timescales with a plasticity rule act

SUBMITTER: Bono J 

PROVIDER: S-EPMC10019888 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature