Modeling flexible behavior with remapping-based hippocampal sequence learning

TL;DR

This paper introduces a biologically plausible reinforcement learning model that explains how hippocampal remapping supports flexible, context-dependent behavior and predicts implications for neuropsychiatric disorders.

Contribution

It presents a novel model linking hippocampal remapping to flexible behavior and neuropsychiatric disorder predictions, advancing understanding of neural basis of context-dependent actions.

Findings

Reproduces neural activity and behavioral findings from multiple studies.

Predicts that imbalances in sensory and contextual representations relate to SZ and ASD behaviors.

Supports the role of hippocampal remapping in flexible behavior.

Abstract

Animals flexibly change their behavior depending on context. It is reported that the hippocampus is one of the most prominent regions for contextual behaviors, and its sequential activity shows context dependency. However, how such context-dependent sequential activity is established through reorganization of neuronal activity (remapping) is unclear. To better understand the formation of hippocampal activity and its contribution to context-dependent flexible behavior, we present a novel biologically plausible reinforcement learning model. In this model, Context selector promotes the formation of context-dependent sequential activity and allows for flexible switching of behavior in multiple contexts. This model reproduces a variety of findings from neural activity, optogenetic inactivation, human fMRI, and clinical research. Furthermore, our model predicts that imbalances in the ratio between sensory and contextual representations in Context selector account for schizophrenia (SZ) and autism spectrum disorder (ASD)-like behaviors.