Cross-Frequency Coupling Increases Memory Capacity in Oscillatory Neural Networks

• URL: http://arxiv.org/abs/2204.07163v1
• Date: Tue, 5 Apr 2022 17:13:36 GMT
• Title: Cross-Frequency Coupling Increases Memory Capacity in Oscillatory Neural Networks
• Authors: Connor Bybee, Alexander Belsten, Friedrich T. Sommer
• Abstract summary: Cross-frequency coupling (CFC) is associated with information integration across populations of neurons. We construct a model of CFC which predicts a computational role for observed $theta - gamma$ oscillatory circuits in the hippocampus and cortex. We show that the presence of CFC increases the memory capacity of a population of neurons connected by plastic synapses.
• Score: 69.42260428921436
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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