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Comput. Eng."],"published-print":{"date-parts":[[2025,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Spiking neural networks (SNNs) are neuromorphic systems that emulate certain aspects of biological neural tissue, offering potential advantages in energy efficiency and speed by for example leveraging sparsity. While CMOS-based electronic SNN hardware has shown promise, scalability and parallelism challenges remain. Photonics provides a promising platform for SNNs due to the speed of excitable photonic devices standing in as neurons and the parallelism and low-latency of optical signal conduction. Here, we present a photonic SNN comprising 40\u2009000 neurons using off-the-shelf components, including a spatial light modulator and a CMOS camera, enabling scalable and cost-effective implementations for photonic SNN proof of concept studies. The system is governed by a modified Ikeda map, where adding slow inhibitory feedback forcing introduces excitability akin to biological dynamics. Using latency encoding and sparsity, the network achieves 83.5% accuracy on MNIST handwritten digits using only 22% of neurons, and 77.5% with only 8.5% of neurons. Training is performed via liquid state machine concepts combined with the hardware-compatible simultaneous perturbation stochastic approximation algorithm, marking its first use in photonic neural networks. This demonstration integrates photonic nonlinearity, excitability, and sparse computation, paving the way for efficient large-scale photonic neuromorphic systems.<\/jats:p>","DOI":"10.1088\/2634-4386\/addee7","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T18:50:00Z","timestamp":1748631000000},"page":"034003","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["A spiking photonic neural network of 40\u2009000 neurons, trained with latency and rank-order coding for leveraging sparsity"],"prefix":"10.1088","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5103-3003","authenticated-orcid":true,"given":"Ria","family":"Talukder","sequence":"first","affiliation":[]},{"given":"Anas","family":"Skalli","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9869-7170","authenticated-orcid":true,"given":"Xavier","family":"Porte","sequence":"additional","affiliation":[]},{"given":"Simon","family":"Thorpe","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4003-3056","authenticated-orcid":true,"given":"Daniel","family":"Brunner","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2025,7,4]]},"reference":[{"key":"nceaddee7bib1","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"nceaddee7bib2","first-page":"pp 5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"nceaddee7bib3","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: a probabilistic model for information storage and organization in the brain","volume":"65","author":"Rosenblatt","year":"1958","journal-title":"Psychol. 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Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2024-11-28","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-05-30","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-07-04","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}