{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:50:23Z","timestamp":1785408623537,"version":"3.56.0"},"posted":{"date-parts":[[2026,5,27]]},"group-title":"Neuroscience","reference-count":57,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nd\/4.0\/"}],"funder":[{"award":["276693517"],"award-info":[{"award-number":["276693517"]}],"id":[{"id":"https:\/\/ror.org\/018mejw64","id-type":"ROR","asserted-by":"publisher"}]},{"award":["UM1MH130981"],"award-info":[{"award-number":["UM1MH130981"]}],"id":[{"id":"https:\/\/ror.org\/04xeg9z08","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"crossref","award":["R01 NS113890"],"award-info":[{"award-number":["R01 NS113890"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Intelligence Advanced Research Projects Activity","award":["D16PC00003"],"award-info":[{"award-number":["D16PC00003"]}]},{"name":"The NSF AI Institute for Artificial and Natural Intelligence","award":["DBI-2229929"],"award-info":[{"award-number":["DBI-2229929"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,5,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  <jats:italic>Neural system identification<\/jats:italic>\n                  approaches use empirical data to fit the stimulus-response functions of neurons. Augmented by deep neural networks, such models have achieved high predictive performance and allow to perform\n                  <jats:italic>in silico<\/jats:italic>\n                  experiments to test hypotheses. Yet, many of these methods ignore common features in visual systems, such as inhibitory interactions between neurons, which are essential for nonlinear neural computation. Here, we incorporate inhibition as an inductive bias into a deep model for neural prediction and investigate the influence of inhibition on the learned transfer functions. To this end, we employ difference-of-Gaussian (subtraction) and within-channel divisive normalization (division), which have been proposed to relate inhibition to neural processing, in deep networks for predicting visual responses. We observe that incorporating such operations maintains the predictive performance and encourages the learning of biologically plausible kernels reminiscent of neural representation in early vision. Additionally, our\n                  <jats:italic>in silico<\/jats:italic>\n                  experiments demonstrate that implementing either sub-tractive or divisive operation benefits the learning of surround suppression but not cross-orientation inhibition. Interestingly, while division increases the sparsity of activation and reduces the sparsity of weights, subtraction has the reverse effect.\n                <\/jats:p>","DOI":"10.64898\/2026.05.27.728086","type":"posted-content","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T22:55:22Z","timestamp":1779922522000},"source":"Crossref","is-referenced-by-count":0,"title":["Inhibition benefits neural system identification"],"prefix":"10.64898","author":[{"given":"Yuyao","family":"Deng","sequence":"first","affiliation":[{"name":"Institute for Ophthalmic Research, University of T\u00fcbingen, T\u00fcbingen, Germany"},{"name":"Graduate Training Centre of Neuroscience, University of T\u00fcbingen, T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuokun","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA"},{"name":"Stanford Bio-X, Stanford University, Stanford, CA, USA"},{"name":"Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiakun","family":"Fu","sequence":"additional","affiliation":[{"name":"Salk Institute for Biological Studies, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Oesterle","sequence":"additional","affiliation":[{"name":"Institute for Ophthalmic Research, University of T\u00fcbingen, T\u00fcbingen, Germany"},{"name":"Hertie Institute for AI in Brain Health, University of T\u00fcbingen, T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas S.","family":"Tolias","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA"},{"name":"Stanford Bio-X, Stanford University, Stanford, CA, USA"},{"name":"Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA"},{"name":"Department of Electrical Engineering, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4567-6966","authenticated-orcid":false,"given":"Thomas","family":"Euler","sequence":"additional","affiliation":[{"name":"Institute for Ophthalmic Research, University of T\u00fcbingen, T\u00fcbingen, Germany"},{"name":"Bernstein Centre for Computational Neuroscience, T\u00fcbingen, Germany"},{"name":"Centre for Integrative Neuroscience, University of T\u00fcbingen, T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongrong","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Byers Eye Institute, Stanford University School of Medicine, Stanford, CA, USA"},{"name":"Stanford Bio-X, Stanford University, Stanford, CA, USA"},{"name":"Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026052920552732000_2026.05.27.728086v1.1","doi-asserted-by":"publisher","DOI":"10.1017\/S0952523800010336"},{"key":"2026052920552732000_2026.05.27.728086v1.2","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2108713118"},{"key":"2026052920552732000_2026.05.27.728086v1.3","doi-asserted-by":"publisher","DOI":"10.1113\/jphysiol.1953.sp004829"},{"key":"2026052920552732000_2026.05.27.728086v1.4","doi-asserted-by":"crossref","unstructured":"Pouya Bashivan , Kohitij Kar , and James J DiCarlo . 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