Author:
Pugavko Mechislav M.,Maslennikov Oleg V.,Nekorkin Vladimir I.
Abstract
AbstractIn this work, inspired by cognitive neuroscience experiments, we propose recurrent spiking neural networks trained to perform multiple target tasks. These models are designed by considering neurocognitive activity as computational processes through dynamics. Trained by input–output examples, these spiking neural networks are reverse engineered to find the dynamic mechanisms that are fundamental to their performance. We show that considering multitasking and spiking within one system provides insightful ideas on the principles of neural computation.
Funder
Russian Science Foundation
Publisher
Springer Science and Business Media LLC
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