Associative memory model with arbitrary Hebbian length

TL;DR

This paper introduces a generalized associative memory model with arbitrary Hebbian length, demonstrating how small Hebbian lengths enhance correlation conversion and how anti-Hebbian components reshape memory landscapes, linking memory, Hebbian length, and correlation in the brain.

Contribution

The paper presents an analytically solvable associative memory model with arbitrary Hebbian length, revealing its effects on correlation conversion and memory landscape shaping.

Findings

Small Hebbian length significantly improves correlation conversion.

Anti-Hebbian components reshape the energy landscape of memories.

Model predicts enhanced state transitions in neural representations.

Abstract

Conversion of temporal to spatial correlations in the cortex is one of the most intriguing functions in the brain. The learning at synapses triggering the correlation conversion can take place in a wide integration window, whose influence on the correlation conversion remains elusive. Here, we propose a generalized associative memory model with arbitrary Hebbian length. The model can be analytically solved, and predicts that a small Hebbian length can already significantly enhance the correlation conversion, i.e., the stimulus-induced attractor can be highly correlated with a significant number of patterns in the stored sequence, thereby facilitating state transitions in the neural representation space. Moreover, an anti-Hebbian component is able to reshape the energy landscape of memories, akin to the function of sleep. Our work thus establishes the fundamental connection between associative memory, Hebbian length, and correlation conversion in the brain.