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To narrow this gap, we developed a training scheme that, in addition to achieving learning goals, respects the structural and dynamic properties of a standard cortical circuit model: strongly coupled excitatory-inhibitory spiking neural networks. By preserving the strong mean excitatory and inhibitory coupling of initial networks, we found that most of trained synapses obeyed Dale's law without additional constraints, exhibited large trial-to-trial spiking variability, and operated in inhibition-stabilized regime. We derived analytical estimates on how training and network parameters constrained the changes in mean synaptic strength during training. Our results demonstrate that training recurrent neural networks subject to strong coupling constraints can result in connectivity structure and dynamic regime relevant to cortical circuits.<\/jats:p>","DOI":"10.1162\/neco_a_01379","type":"journal-article","created":{"date-parts":[[2021,4,14]],"date-time":"2021-04-14T20:35:38Z","timestamp":1618432538000},"page":"1199-1233","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":6,"title":["Training Spiking Neural Networks in the Strong Coupling Regime"],"prefix":"10.1162","volume":"33","author":[{"given":"Christopher M.","family":"Kim","sequence":"first","affiliation":[{"name":"Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases\/National Institutes of Health, Bethesda, MD 20814, U.S.A. chrismkkim@gmail.com"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carson 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