{"doi":"10.1101/2022.09.26.509578","title":"A scalable implementation of the recursive least-squares algorithm for training spiking neural networks","abstract":"Abstract 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 1000 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 > 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables a more interactive in-silico study of the dynamics and connectivity underlying multi-area computations. It also admits the possibility to train models as in-vivo experiments are being conducted, thus closing the loop between modeling and experiments.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2022,"id":304387,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9494,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":582541,"name":"Christopher M. Kim","orcid":"0000-0002-1322-6207","position":1,"is_corresponding":false},{"id":559027,"name":"Susu Chen","orcid":"0000-0002-5065-1157","position":2,"is_corresponding":false},{"id":52909,"name":"Stephan Preibisch","orcid":"0000-0002-0276-494X","position":3,"is_corresponding":false},{"id":990658,"name":"Ran Darshan","orcid":"0000-0003-3078-4857","position":4,"is_corresponding":false},{"id":997233,"name":"Benjamin J. Arthur","orcid":"0000-0003-3545-8807","position":0,"is_corresponding":true}],"reference_count":38,"raw_metadata":null,"created_at":"2026-07-19T00:32:32.651796Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}