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

• URL: http://arxiv.org/abs/2103.08878v1
• Date: Tue, 16 Mar 2021 07:03:04 GMT
• Title: Learning without gradient descent encoded by the dynamics of a neurobiological model
• Authors: Vivek Kurien George, Vikash Morar, Weiwei Yang, Jonathan Larson, Bryan Tower, Shweti Mahajan, Arkin Gupta, Christopher White, Gabriel A. Silva
• Abstract summary: We introduce a conceptual approach to machine learning that takes advantage of a neurobiologically derived model of dynamic signaling. 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.
• Score: 7.952666139462592
• License: http://creativecommons.org/licenses/by/4.0/
• 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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