Neural field model of memory-guided search

TL;DR

This paper introduces a neural field model that encodes memory and guides search behavior, capturing how organisms avoid previously visited locations during search tasks.

Contribution

It presents a novel two-layer neural field model combining position encoding and memory to simulate memory-guided search behavior.

Findings

Model successfully encodes positional memory and influences search paths.

Asymptotic analysis reduces complex dynamics to a low-dimensional system.

Performance varies with different target-finding task parameters.

Abstract

Many organisms can remember locations they have previously visited during a search. Visual search experiments have shown exploration is guided away from these locations, reducing the overlap of the search path before finding a hidden target. We develop and analyze a two-layer neural field model that encodes positional information during a search task. A position-encoding layer sustains a bump attractor corresponding to the searching agent's current location, and search is modeled by velocity input that propagates the bump. A memory layer sustains persistent activity bounded by a wave front, whose edges expand in response to excitatory input from the position layer. Search can then be biased in response to remembered locations, influencing velocity inputs to the position layer. Asymptotic techniques are used to reduce the dynamics of our model to a low-dimensional system of equations that track the bump position and front boundary. Performance is compared for different target-finding tasks.