{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T18:22:25Z","timestamp":1788459745777,"version":"build-2803163510"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1014653","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000}}],"reference-count":53,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union \u2013 NextGenerationEU \u2013 PNRRM4C2-I.1.1 \/ PRIN","award":["2022XE8X9E"],"award-info":[{"award-number":["2022XE8X9E"]}]},{"name":"European Research Council","award":["101166056 (Project \u201cbeyond2\u201d)"],"award-info":[{"award-number":["101166056 (Project \u201cbeyond2\u201d)"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>In vision neuroscience, the temporal dynamics of the sensory stream and of its neural representations are thought to be deeply linked to the function of the hierarchy of cortical areas that deal with object recognition, known as the visual ventral stream. Neural representations that are invariant under identity-preserving object transformations, and therefore allow for efficient learning of object identity, are theorized to emerge from a self-supervised learning process that attempts to extract \u201ctemporally stable\u201d features from the sensory input. Conversely, invariance increases along the hierarchy, putatively implying progressively slower neural codes in higher-level areas. Recent neurophysiological evidence shows that indeed, as one moves along this cortical hierarchy, neural representations of dynamic stimuli become slower, and additionally the temporal scales of the within-trial fluctuations of these representations (called \u201cintrinsic timescales\u201d) increase starkly. However, while these timescale hierarchies have been reproduced in biologically grounded recurrent models, their network determinants have remained largely unexplored in image-computable models of the ventral stream. Here we investigate the temporal structure of the neural codes in a noisy, recurrent and adaptive model of the ventral visual stream. We show that, surprisingly, the organization of the representation timescales is set by the broad architectural features of the network, regardless of training, while the ordering of the intrinsic timescales across layers is sensitive to the details of the functions implemented by each layer. Our work underscores the importance of the temporal structure of the neural code as a probe for the link between structure and function in models of the vertebrate visual system.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1014653","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:48:04Z","timestamp":1787852884000},"page":"e1014653","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Noisy models of the ventral stream reveal the impact of recurrence and learned representations on information processing timescales"],"prefix":"10.1371","volume":"22","author":[{"given":"Sara","family":"Varetti","sequence":"first","affiliation":[{"name":"Cognitive Neuroscience, International School for Advanced Studies (SISSA)","place":["Trieste, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Goldt","sequence":"additional","affiliation":[{"name":"Theoretical and Scientific Data Science, International School for Advanced Studies (SISSA)","place":["Trieste, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0384-7699","authenticated-orcid":true,"given":"Eugenio","family":"Piasini","sequence":"additional","affiliation":[{"name":"Cognitive Neuroscience, International School for Advanced Studies (SISSA)","place":["Trieste, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"issue":"23","key":"pcbi.1014653.ref001","doi-asserted-by":"crossref","first-page":"8619","DOI":"10.1073\/pnas.1403112111","article-title":"Performance-optimized hierarchical models predict neural responses in higher visual cortex","volume":"111","author":"DLK Yamins","year":"2014","journal-title":"Proc Natl Acad Sci U S A"},{"issue":"12","key":"pcbi.1014653.ref002","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1003963","article-title":"Deep neural networks rival the representation of primate IT cortex for core visual object recognition","volume":"10","author":"CF Cadieu","year":"2014","journal-title":"PLoS Comput Biol"},{"issue":"27","key":"pcbi.1014653.ref003","doi-asserted-by":"crossref","first-page":"10005","DOI":"10.1523\/JNEUROSCI.5023-14.2015","article-title":"Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream","volume":"35","author":"U G\u00fc\u00e7l\u00fc","year":"2015","journal-title":"J Neurosci"},{"issue":"6439","key":"pcbi.1014653.ref004","doi-asserted-by":"crossref","DOI":"10.1126\/science.aav9436","article-title":"Neural population control via deep image synthesis","volume":"364","author":"P Bashivan","year":"2019","journal-title":"Science"},{"issue":"6","key":"pcbi.1014653.ref005","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1038\/s41593-019-0392-5","article-title":"Evidence that recurrent circuits are critical to the ventral stream\u2019s execution of core object recognition behavior","volume":"22","author":"K Kar","year":"2019","journal-title":"Nat Neurosci"},{"issue":"3","key":"pcbi.1014653.ref006","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1113\/jphysiol.1959.sp006308","article-title":"Receptive fields of single neurones in the cat\u2019s striate cortex","volume":"148","author":"DH Hubel","year":"1959","journal-title":"J Physiol"},{"issue":"1","key":"pcbi.1014653.ref007","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1113\/jphysiol.1968.sp008455","article-title":"Receptive fields and functional architecture of monkey striate cortex","volume":"195","author":"DH Hubel","year":"1968","journal-title":"J Physiol"},{"issue":"10","key":"pcbi.1014653.ref008","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1523\/JNEUROSCI.5487-07.2008","article-title":"A hierarchy of temporal receptive windows in human cortex","volume":"28","author":"U Hasson","year":"2008","journal-title":"J Neurosci"},{"issue":"35","key":"pcbi.1014653.ref009","doi-asserted-by":"crossref","first-page":"8835","DOI":"10.1073\/pnas.1719397115","article-title":"Recurrent computations for visual pattern completion","volume":"115","author":"H Tang","year":"2018","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1014653.ref010","unstructured":"Liao Q, Poggio T. 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