{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T05:00:08Z","timestamp":1773982808431,"version":"3.50.1"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T00:00:00Z","timestamp":1687824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000011","name":"Howard Hughes Medical Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000011","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neuroinform."],"abstract":"<jats:p>\n                    Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a popular way to study computations performed by the nervous system. As the size and complexity of neural recordings increase, there is a need for efficient algorithms that can train models in a short period of time using minimal resources. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation can train networks of one million neurons, with 100 million plastic synapses and a billion static synapses, about 1,000 times faster than an unoptimized reference CPU implementation. We demonstrate the code's utility by training a network, in less than an hour, to reproduce the activity of &amp;gt; 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables a more interactive\n                    <jats:italic>in-silico<\/jats:italic>\n                    study of the dynamics and connectivity underlying multi-area computations. It also admits the possibility to train models as\n                    <jats:italic>in-vivo<\/jats:italic>\n                    experiments are being conducted, thus closing the loop between modeling and experiments.\n                  <\/jats:p>","DOI":"10.3389\/fninf.2023.1099510","type":"journal-article","created":{"date-parts":[[2023,6,27]],"date-time":"2023-06-27T01:38:48Z","timestamp":1687829928000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["A scalable implementation of the recursive least-squares algorithm for training spiking neural networks"],"prefix":"10.3389","volume":"17","author":[{"given":"Benjamin J.","family":"Arthur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher M.","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephan","family":"Preibisch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ran","family":"Darshan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,6,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"883700","DOI":"10.3389\/fninf.2022.883700","article-title":"Brian2CUDA: flexible and efficient simulation of spiking neural network models on GPUs","volume":"16","author":"Alevi","year":"2022","journal-title":"Front. 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