A Neural Network Model of the Entorhinal Cortex and Hippocampus for Event-order Memory Processing

TL;DR

This paper presents a biologically inspired neural network model that simulates event-order memory processing in the entorhinal cortex and hippocampus, enabling efficient mental navigation to recall reward locations.

Contribution

It introduces a neural network model that stores and recalls sequences of events for navigation, simulating hippocampal-entorhinal functions in a virtual maze environment.

Findings

The model successfully recalls routes to rewards in a maze.

It demonstrates mind travel for goal-directed navigation.

The approach is applicable to autonomous robot navigation.

Abstract

To solve a navigation task based on experiences, we need a mechanism to associate places with objects and to recall them along the course of action. In a reward-oriented task, if the route to a reward location is simulated in mind after experiencing it once, it might be possible that the reward is gained efficiently. One way to solve it is to incorporate a biologically plausible mechanism. In this study, we propose a neural network that stores a sequence of events associated with a reward. The proposed network recalls the reward location by tracing them in its mind in order. We simulated a virtual mouse that explores a figure-eight maze and recalls the route to the reward location. During the learning period, a sequence of events relating to firing along a passage was temporarily stored in the heteroassociative network, and the sequence of events is consolidated in the synaptic weight matrix when a reward is fed. For retrieval, the sequential activation of conjunctive cue-place cells toward the reward location is internally generated by an impetus input. In the figure-eight maze task, the location of the reward was estimated by mind travel, irrespective of whether the reward is in the counterclockwise or distant clockwise route. The mechanism of efficiently reaching the goal by mind travel in the brain based on experiences is beneficial for mobile service robots that perform autonomous navigation.