Memory recall by controlling chaos

TL;DR

This paper proposes that human memory retrieval can be modeled as controlling chaos in neural networks, where feedback mechanisms help stabilize chaotic neural activity into specific memory states.

Contribution

It introduces a novel hypothesis linking chaos control theory to memory encoding and retrieval in biological neural networks.

Findings

Memory may be encoded by neural cycles that can be stabilized through feedback control.

Sensory cues can act as references to retrieve specific memories by controlling neural chaos.

The approach offers a new perspective on neural dynamics and memory processes.

Abstract

By incorporating feedback loops, that engender amplification and damping so that output is not proportional to input, the biological neural networks become highly nonlinear and thus very likely chaotic in nature. Research in control theory reveals that strange attractors can be approximated by collection of cycles, and be collapsed into a more coherent state centered on one of them if we exert control. We speculate that human memories are encoded by such cycles, and can be retrieved once sensory or virtual cues, acting as references, enable feedback controls that nucleates the otherwise chaotic wandering mind.