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Features such as heterogeneous f-I curves and spike frequency adaptation are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single-neuron flexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and neural circuits must be considered as a complete system. In this work, we use artificial neural networks to systematically investigate single-neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function parametrized to mimic f-I curves of biological neurons, either by learning an individual static function or via a learned and shared adaptation mechanism to modify activation functions in real-time during a task. We find that such adaptive networks show much-improved robustness to noise and changes in input statistics. Using tools from dynamical systems theory, we analyze the role of these emergent single-neuron properties and argue that neural diversity and adaptation play an active regularization role, enabling neural circuits to optimally propagate information across time. Finally, we outline similarities between these optimized solutions and known coding strategies found in biological neurons, such as gain scaling and fractional order differentiation\/integration.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1012567","type":"journal-article","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T18:41:38Z","timestamp":1734115298000},"page":"e1012567","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Neural networks with optimized single-neuron adaptation uncover biologically plausible regularization"],"prefix":"10.1371","volume":"20","author":[{"given":"Victor","family":"Geadah","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2951-2600","authenticated-orcid":true,"given":"Stefan","family":"Horoi","sequence":"additional","affiliation":[]},{"given":"Giancarlo","family":"Kerg","sequence":"additional","affiliation":[]},{"given":"Guy","family":"Wolf","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2730-7291","authenticated-orcid":true,"given":"Guillaume","family":"Lajoie","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,12,13]]},"reference":[{"key":"pcbi.1012567.ref001","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.conb.2015.12.008","article-title":"ScienceDirect Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance","volume":"37","author":"J Gjorgjieva","year":"2016","journal-title":"Current Opinion in Neurobiology"},{"issue":"1","key":"pcbi.1012567.ref002","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1146\/annurev-vision-091718-014818","article-title":"Coding Principles in Adaptation","volume":"5","author":"AI Weber","year":"2019","journal-title":"Annual Review of Vision Science"},{"key":"pcbi.1012567.ref003","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1038\/nn.4244","article-title":"Using goal-driven deep learning models to understand sensory cortex","volume":"19","author":"DLK Yamins","year":"2016","journal-title":"Nature Neuroscience"},{"key":"pcbi.1012567.ref004","article-title":"Possible Principles Underlying the Transformations of Sensory Messages","volume":"1","author":"H Barlow","year":"1961","journal-title":"Sensory Communication"},{"key":"pcbi.1012567.ref005","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1515\/znc-1981-9-1040","article-title":"A Simple Coding Procedure Enhances a Neuron\u2019s Information Capacity","volume":"36","author":"SB Laughlin","year":"1981","journal-title":"Zeitschrift f\u00fcr Naturforschung C"},{"issue":"11","key":"pcbi.1012567.ref006","doi-asserted-by":"crossref","first-page":"2523","DOI":"10.1162\/089976603322385063","article-title":"A Universal Model for Spike-Frequency Adaptation","volume":"15","author":"J Benda","year":"2003","journal-title":"Neural Computation"},{"issue":"6849","key":"pcbi.1012567.ref007","first-page":"787","article-title":"Efficiency and ambiguity in an adaptive neural code","volume":"412","author":"AL Fairhall","year":"2001","journal-title":"Nature Publishing Group"},{"key":"pcbi.1012567.ref008","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1038\/nn.2259","article-title":"Spike frequency adaptation mediates looming stimulus selectivity in a collision-detecting neuron","volume":"12","author":"S Peron","year":"2009","journal-title":"Nature neuroscience"},{"issue":"6","key":"pcbi.1012567.ref009","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1004275","article-title":"Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models","volume":"11","author":"C Pozzorini","year":"2015","journal-title":"PLOS Computational Biology"},{"issue":"11","key":"pcbi.1012567.ref010","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1038\/nn.2212","article-title":"Fractional differentiation by neocortical pyramidal neurons","volume":"11","author":"BN Lundstrom","year":"2008","journal-title":"Nature Neuroscience"},{"key":"pcbi.1012567.ref011","doi-asserted-by":"crossref","first-page":"e46926","DOI":"10.7554\/eLife.46926","article-title":"Population adaptation in efficient balanced networks","volume":"8","author":"GJ Gutierrez","year":"2019","journal-title":"eLife"},{"key":"pcbi.1012567.ref012","unstructured":"Bellec G, Salaj D, Subramoney A, Legenstein R, Maass W. 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