# Repeated sequential learning increases memory capacity via effective decorrelation in a recurrent neural network

## TL;DR

This paper demonstrates that repeated sequential learning in a recurrent neural network significantly enhances memory capacity by inducing effective decorrelation through a local learning rule, leading to spontaneous activity that stabilizes embedded memories.

## Contribution

It introduces a simple local learning rule that, when applied sequentially, drastically increases memory capacity via correlation decorrelation and spontaneous activity emergence.

## Key findings

- Memory capacity is greatly increased by sequential learning.
- Spontaneous neural activity correlates with embedded memories.
- Memory stabilization occurs through a bifurcation mechanism.

## Abstract

Memories in neural system are shaped through the interplay of neural and learning dynamics under external inputs. By introducing a simple local learning rule to a neural network, we found that the memory capacity is drastically increased by sequentially repeating the learning steps of input-output mappings. The origin of this enhancement is attributed to the generation of a Psuedo-inverse correlation in the connectivity. This is associated with the emergence of spontaneous activity that intermittently exhibits neural patterns corresponding to embedded memories. Stablization of memories is achieved by a distinct bifurcation from the spontaneous activity under the application of each input.