Cross-Frequency Coupling Increases Memory Capacity in Oscillatory Neural Networks

TL;DR

This paper introduces a novel oscillator neural network model incorporating cross-frequency coupling, demonstrating that CFC enhances memory capacity and enables error-free pattern retrieval in neural networks, aligning with observed brain oscillations.

Contribution

It presents a new oscillator neural network model with subharmonic injection locking that predicts the functional role of CFC in increasing memory capacity and pattern retrieval in neural systems.

Findings

CFC increases neural network memory capacity.

CFC enables error-free pattern retrieval.

The model aligns with hippocampal and cortical oscillation ratios.

Abstract

An open problem in neuroscience is to explain the functional role of oscillations in neural networks, contributing, for example, to perception, attention, and memory. Cross-frequency coupling (CFC) is associated with information integration across populations of neurons. Impaired CFC is linked to neurological disease. It is unclear what role CFC has in information processing and brain functional connectivity. We construct a model of CFC which predicts a computational role for observed $\theta - \gamma$ oscillatory circuits in the hippocampus and cortex. Our model predicts that the complex dynamics in recurrent and feedforward networks of coupled oscillators performs robust information storage and pattern retrieval. Based on phasor associative memories (PAM), we present a novel oscillator neural network (ONN) model that includes subharmonic injection locking (SHIL) and which reproduces experimental observations of CFC. We show that the presence of CFC increases the memory capacity of a population of neurons connected by plastic synapses. CFC enables error-free pattern retrieval whereas pattern retrieval fails without CFC. In addition, the trade-offs between sparse connectivity, capacity, and information per connection are identified. The associative memory is based on a complex-valued neural network, or phasor neural network (PNN). We show that for values of $Q$ which are the same as the ratio of $\gamma$ to $\theta$ oscillations observed in the hippocampus and the cortex, the associative memory achieves greater capacity and information storage than previous models. The novel contributions of this work are providing a computational framework based on oscillator dynamics which predicts the functional role of neural oscillations and connecting concepts in neural network theory and dynamical system theory.