{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/assessing-pattern-recognition-performance-of","title":"Assessing Pattern Recognition Performance of Neuronal Cultures through Accurate Simulation","arxiv_id":"2012.10355","date":"2020-12-18","proceeding":null,"authors":["Gabriele Lagani","Raffaele Mazziotti","Fabrizio Falchi","Claudio Gennaro","Guido Marco Cicchini","Tommaso Pizzorusso","Federico Cremisi","Giuseppe Amato"],"abstract":"Previous work has shown that it is possible to train neuronal cultures on Multi-Electrode Arrays (MEAs), to recognize very simple patterns. However, this work was mainly focused to demonstrate that it is possible to induce plasticity in cultures, rather than performing a rigorous assessment of their pattern recognition performance. In this paper, we address this gap by developing a methodology that allows us to assess the performance of neuronal cultures on a learning task. Specifically, we propose a digital model of the real cultured neuronal networks; we identify biologically plausible simulation parameters that allow us to reliably reproduce the behavior of real cultures; we use the simulated culture to perform handwritten digit recognition and rigorously evaluate its performance; we also show that it is possible to find improved simulation parameters for the specific task, which can guide the creation of real cultures.","url_abs":"https://arxiv.org/abs/2012.10355v2","url_pdf":"https://arxiv.org/pdf/2012.10355v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"assessing-pattern-recognition-performance-of","repo_url":"https://github.com/GabrieleLagani/SpikingGrid","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"culture","task_name":"Cultural Vocal Bursts Intensity Prediction"},{"task_slug":"handwritten-digit-recognition","task_name":"Handwritten Digit Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}