Models of attractor dynamics in the brain

TL;DR

This paper reviews how attractor neural network models explain various cognitive functions by demonstrating stable neural activity patterns across different brain regions, supported by empirical evidence.

Contribution

It synthesizes four key examples illustrating the computational mechanisms of attractor dynamics in biological neural systems, highlighting their role in cognition.

Findings

Attractor models explain hippocampal spatial representations.

They elucidate visual classification processes.

They account for perceptual adaptation and priming.

Abstract

Attractor dynamics are a fundamental computational motif in neural circuits, supporting diverse cognitive functions through stable, self-sustaining patterns of neural activity. In these lecture notes, we review four key examples that demonstrate how autoassociative neural network models can elucidate the computational mechanisms underlying attractor-based information processing in biological neural systems performing cognitive functions. Drawing on empirical evidence, we explore hippocampal spatial representations, visual classification in the inferotemporal cortex, perceptual adaptation and priming, and working-memory biases shaped by sensory history. Across these domains, attractor network models reveal common computational principles and provide analytical insights into how experience shapes neural activity and behavior. Our synthesis underscores the value of attractor models as powerful tools for probing the neural basis of cognition and behavior.