Introducing principles of synaptic integration in the optimization of deep neural networks

Tasks involving continual learning and adaptation to real-time scenarios remain challenging for artificial neural networks in contrast to real brain. The authors propose here a brain-inspired optimizer based on mechanisms of synaptic integration and strength regulation for improved performance of both artificial and spiking neural networks.

Abstract licence: CC BY-NC-ND

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37 more references, whose wording the paper’s licence keeps private.

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