Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity

TL;DR

This paper introduces a novel method for optimizing heterogeneity in Operational Neural Networks by applying synaptic plasticity principles, leading to improved learning performance over traditional methods and CNNs.

Contribution

It proposes a synaptic plasticity-based search approach for selecting optimal operator sets in ONNs, enhancing heterogeneity and learning efficiency.

Findings

Elite ONNs outperform GIS-based ONNs in complex tasks.

The proposed method achieves better accuracy with fewer neurons and layers.

Performance gap between ONNs and CNNs widens with the new approach.

Abstract

The recently proposed network model, Operational Neural Networks (ONNs), can generalize the conventional Convolutional Neural Networks (CNNs) that are homogenous only with a linear neuron model. As a heterogenous network model, ONNs are based on a generalized neuron model that can encapsulate any set of non-linear operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. However, the default search method to find optimal operators in ONNs, the so-called Greedy Iterative Search (GIS) method, usually takes several training sessions to find a single operator set per layer. This is not only computationally demanding, also the network heterogeneity is limited since the same set of operators will then be used for all neurons in each layer. To address this deficiency and exploit a superior level of heterogeneity, in this study the focus is drawn on searching the best-possible operator set(s) for the hidden neurons of the network based on the Synaptic Plasticity paradigm that poses the essential learning theory in biological neurons. During training, each operator set in the library can be evaluated by their synaptic plasticity level, ranked from the worst to the best, and an elite ONN can then be configured using the top ranked operator sets found at each hidden layer. Experimental results over highly challenging problems demonstrate that the elite ONNs even with few neurons and layers can achieve a superior learning performance than GIS-based ONNs and as a result the performance gap over the CNNs further widens.