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Multitask computation through dynamics in recurrent spiking neural networks.


ABSTRACT: In 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.

SUBMITTER: Pugavko MM 

PROVIDER: S-EPMC10006454 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Multitask computation through dynamics in recurrent spiking neural networks.

Pugavko Mechislav M MM   Maslennikov Oleg V OV   Nekorkin Vladimir I VI  

Scientific reports 20230310 1


In 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 ins  ...[more]

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