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arXiv · 2103.08878

Learning without gradient descent encoded by the dynamics of a neurobiological model

Vivek Kurien George·Vikash Morar·Weiwei Yang·Jonathan Larson·Bryan Tower·Shweti Mahajan·Arkin Gupta·Christopher White·Gabriel A. Silva
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Abstract

The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss function. A fundamental limitation, however, is the need to train these systems in either supervised or unsupervised ways by exposing them to typically large numbers of training examples. Here, we introduce a fundamentally novel conceptual approach to machine learning that takes advantage of a neurobiologically derived model of dynamic signaling, constrained by the geometric structure of a network. We show that MNIST images can be uniquely encoded and classified by the dynamics of geometric networks with nearly state-of-the-art accuracy in an unsupervised way, and without the need for any training.

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Vivek Kurien George, Vikash Morar, Weiwei Yang, Jonathan Larson, Bryan Tower, Shweti Mahajan, Arkin Gupta, Christopher White, Gabriel A. Silva. 2021-03-16. Learning without gradient descent encoded by the dynamics of a neurobiological model. https://arxiv.org/abs/2103.08878

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