{"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/synthesizing-realistic-neural-population","title":"Synthesizing realistic neural population activity patterns using Generative Adversarial Networks","arxiv_id":"1803.00338","date":"2018-03-01","proceeding":"ICLR 2018 1","authors":["Manuel Molano-Mazon","Arno Onken","Eugenio Piasini","Stefano Panzeri"],"abstract":"The ability to synthesize realistic patterns of neural activity is crucial\nfor studying neural information processing. Here we used the Generative\nAdversarial Networks (GANs) framework to simulate the concerted activity of a\npopulation of neurons. We adapted the Wasserstein-GAN variant to facilitate the\ngeneration of unconstrained neural population activity patterns while still\nbenefiting from parameter sharing in the temporal domain. We demonstrate that\nour proposed GAN, which we termed Spike-GAN, generates spike trains that match\naccurately the first- and second-order statistics of datasets of tens of\nneurons and also approximates well their higher-order statistics. We applied\nSpike-GAN to a real dataset recorded from salamander retina and showed that it\nperforms as well as state-of-the-art approaches based on the maximum entropy\nand the dichotomized Gaussian frameworks. Importantly, Spike-GAN does not\nrequire to specify a priori the statistics to be matched by the model, and so\nconstitutes a more flexible method than these alternative approaches. Finally,\nwe show how to exploit a trained Spike-GAN to construct 'importance maps' to\ndetect the most relevant statistical structures present in a spike train.\nSpike-GAN provides a powerful, easy-to-use technique for generating realistic\nspiking neural activity and for describing the most relevant features of the\nlarge-scale neural population recordings studied in modern systems\nneuroscience.","url_abs":"http://arxiv.org/abs/1803.00338v2","url_pdf":"http://arxiv.org/pdf/1803.00338v2.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":"synthesizing-realistic-neural-population","repo_url":"https://github.com/manuelmolano/Spike-GAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}