A More Biologically Plausible Local Learning Rule for ANNs

TL;DR

This paper introduces a biologically plausible local learning rule for artificial neural networks based on spike timing, which performs comparably to backpropagation on simple datasets and may offer enhanced robustness and scalability.

Contribution

The paper proposes a novel local learning rule inspired by spike timing dependent plasticity, avoiding error propagation and enhancing biological plausibility.

Findings

Comparable performance to backpropagation on MNIST and IRIS datasets

Potential for improved adversarial robustness against FGSM attack

Supports large-scale distributed and parallel learning

Abstract

The backpropagation algorithm is often debated for its biological plausibility. However, various learning methods for neural architecture have been proposed in search of more biologically plausible learning. Most of them have tried to solve the "weight transport problem" and try to propagate errors backward in the architecture via some alternative methods. In this work, we investigated a slightly different approach that uses only the local information which captures spike timing information with no propagation of errors. The proposed learning rule is derived from the concepts of spike timing dependant plasticity and neuronal association. A preliminary evaluation done on the binary classification of MNIST and IRIS datasets with two hidden layers shows comparable performance with backpropagation. The model learned using this method also shows a possibility of better adversarial robustness against the FGSM attack compared to the model learned through backpropagation of cross-entropy loss. The local nature of learning gives a possibility of large scale distributed and parallel learning in the network. And finally, the proposed method is a more biologically sound method that can probably help in understanding how biological neurons learn different abstractions.