A biologically plausible neural network for local supervision in cortical microcircuits

• URL: http://arxiv.org/abs/2011.15031v1
• Date: Mon, 30 Nov 2020 17:35:22 GMT
• Title: A biologically plausible neural network for local supervision in cortical microcircuits
• Authors: Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii
• Abstract summary: We derive an algorithm for training a neural network which avoids explicit error and backpropagation. Our algorithm maps onto a neural network that bears a remarkable resemblance to the connectivity structure and learning rules of the cortex.
• Score: 17.00937011213428
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The backpropagation algorithm is an invaluable tool for training artificial neural networks; however, because of a weight sharing requirement, it does not provide a plausible model of brain function. Here, in the context of a two-layer network, we derive an algorithm for training a neural network which avoids this problem by not requiring explicit error computation and backpropagation. Furthermore, our algorithm maps onto a neural network that bears a remarkable resemblance to the connectivity structure and learning rules of the cortex. We find that our algorithm empirically performs comparably to backprop on a number of datasets.

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