An Oscillatory Neural Autoencoder Based on Frequency Modulation and Multiplexing.

Karthik Soman,V Srinivasa Chakravarthy,Vignesh Muralidharan

doi:10.3389/fncom.2018.00052

Abstract

Oscillatory phenomena are ubiquitous in the brain. Although there are oscillator-based models of brain dynamics, their universal computational properties have not been explored much unlike in the case of rate-coded and spiking neuron network models. Use of oscillator-based models is often limited to special phenomena like locomotor rhythms and oscillatory attractor-based memories. If neuronal ensembles are taken to be the basic functional units of brain dynamics, it is desirable to develop oscillator-based models that can explain a wide variety of neural phenomena. Autoencoders are a special type of feed forward networks that have been used for construction of large-scale deep networks. Although autoencoders based on rate-coded and spiking neuron networks have been proposed, there are no autoencoders based on oscillators. We propose here an oscillatory neural network model that performs the function of an autoencoder. The model is a hybrid of rate-coded neurons and neural oscillators. Input signals modulate the frequency of the neural encoder oscillators. These signals are then multiplexed using a network of rate-code neurons that has afferent Hebbian and lateral anti-Hebbian connectivity, termed as Lateral Anti Hebbian Network (LAHN). Finally the LAHN output is de-multiplexed using an output neural layer which is a combination of adaptive Hopf and Kuramoto oscillators for the signal reconstruction. The Kuramoto-Hopf combination performing demodulation is a novel way of describing a neural phase-locked loop. The proposed model is tested using both synthetic signals and real world EEG signals. The proposed model arises out of the general motivation to construct biologically inspired, oscillatory versions of some of the standard neural network models, and presents itself as an autoencoder network based on oscillatory neurons applicable to time series signals. As a demonstration, the model is applied to compression of EEG signals.

Highlights

Despite decades of research, the question of neural code is still controversial
The proposed model uses the dynamics of oscillatory system such as phase synchronization, frequency tuning, and uses the signal processing concepts such as frequency modulation (FM) and multiplexing (MUX) to shed light on the possible information transfer mechanisms in the brain
We propose here an oscillatory autoencoder that reconstructs the input signal using a well defined encoder and decoder using the principles of FM, MUX, adaptive frequency dynamics, and phase synchronization

Summary

Introduction

The question of neural code is still controversial. Currently there are two well-accepted approaches to the problem: the spike rate code and the spike timing code. Most of the functional neuro–imaging data including the electroencephalogram (EEG) and functional Magnetic Resonance Imaging (fMRI) encompass the description of the neural activity at this level (Logothetis et al, 2001; David and Friston, 2003; Whittingstall and Logothetis, 2009) When it comes to the description of neural activity at the level of cell assemblies the standard tools and concepts of signal processing could be deployed. Visual information fetched by ∼125 million retinal photoreceptors converge to ∼1 million neurons of the lateral geniculate nucleus in the thalamus (Hubel, 1995) This is one of the instances (among many) of huge dimensionality reduction that takes place in the real brain. The proposed model uses the dynamics of oscillatory system such as phase synchronization, frequency tuning, and uses the signal processing concepts such as frequency modulation (FM) and multiplexing (MUX) to shed light on the possible information transfer mechanisms in the brain

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