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
Reference53 articles.
1. Sporns, O., Tononi, G. & Kötter, R. The human connectome: A structural description of the human brain. PLoS Comput. Biol. 1, e42 (2005).
2. Vyas, S., Golub, M. D., Sussillo, D. & Shenoy, K. V. Computation through neural population dynamics. Annu. Rev. Neurosci. 43, 249–275 (2020).
3. Rabinovich, M. I., Friston, K. J. & Varona, P. Principles of brain dynamics: Global state interactions (MIT Press, 2012).
4. Anokhin, K. Cognitome: In search of fundamental neuroscience theory of consciousness. Zhurnal Vysshei Nervnoi Deyatelnosti Imeni I.P Pavlova 71, 39–71 (2021).
5. Ramezanian-Panahi, M. et al. Generative models of brain dynamics. Front. Artif. Intell. 5, 807406. https://doi.org/10.3389/frai.2022.807406 (2022).
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献