Retrieval of branching sequences in associative memory model with common external input and bias input

TL;DR

This paper introduces a neural network model capable of retrieving branching memory sequences, using common external and bias inputs, and provides a theoretical framework that aligns with simulation results.

Contribution

The study proposes a novel associative memory model that can retrieve branching sequences, extending prior fixed-sequence models with controllable retrieval via external and bias inputs.

Findings

Retrieval of branching sequences is controllable with bias and external inputs.

The macroscopic dynamical description matches simulation results.

The model extends fixed-sequence memory retrieval to branching sequences.

Abstract

We investigate a recurrent neural network model with common external and bias inputs that can retrieve branching sequences. Retrieval of memory sequences is one of the most important functions of the brain. A lot of research has been done on neural networks that process memory sequences. Most of it has focused on fixed memory sequences. However, many animals can remember and recall branching sequences. Therefore, we propose an associative memory model that can retrieve branching sequences. Our model has bias input and common external input. Kawamura and Okada reported that common external input enables sequential memory retrieval in an associative memory model with auto- and weak cross-correlation connections. We show that retrieval processes along branching sequences are controllable with both the bias input and the common external input. To analyze the behaviors of our model, we derived the macroscopic dynamical description as a probability density function. The results obtained by our theory agree with those obtained by computer simulations.