{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T08:10:24Z","timestamp":1779264624117,"version":"3.51.4"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1008775","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000}}],"reference-count":52,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>While vision evokes a dense network of feedforward and feedback neural processes in the brain, visual processes are primarily modeled with feedforward hierarchical neural networks, leaving the computational role of feedback processes poorly understood. Here, we developed a generative autoencoder neural network model and adversarially trained it on a categorically diverse data set of images. We hypothesized that the feedback processes in the ventral visual pathway can be represented by reconstruction of the visual information performed by the generative model. We compared representational similarity of the activity patterns in the proposed model with temporal (magnetoencephalography) and spatial (functional magnetic resonance imaging) visual brain responses. The proposed generative model identified two segregated neural dynamics in the visual brain. A temporal hierarchy of processes transforming low level visual information into high level semantics in the feedforward sweep, and a temporally later dynamics of inverse processes reconstructing low level visual information from a high level latent representation in the feedback sweep. Our results append to previous studies on neural feedback processes by presenting a new insight into the algorithmic function and the information carried by the feedback processes in the ventral visual pathway.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008775","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T13:46:36Z","timestamp":1616593596000},"page":"e1008775","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":19,"title":["Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2296-6477","authenticated-orcid":true,"given":"Haider","family":"Al-Tahan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8525-957X","authenticated-orcid":true,"given":"Yalda","family":"Mohsenzadeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,3,24]]},"reference":[{"issue":"6676","key":"pcbi.1008775.ref001","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1038\/33402","article-title":"A cortical representation the local visual environment","volume":"392","author":"R Epstein","year":"1998","journal-title":"Nature"},{"issue":"1","key":"pcbi.1008775.ref002","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/S0896-6273(00)80758-8","article-title":"The parahippocampal place area: Recognition, navigation, or encoding?","volume":"23","author":"R Epstein","year":"1999","journal-title":"Neuron"},{"key":"pcbi.1008775.ref003","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.neuroimage.2016.03.063","article-title":"Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks","volume":"153","author":"RM Cichy","year":"2017","journal-title":"NeuroImage"},{"key":"pcbi.1008775.ref004","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.cortex.2018.06.006","article-title":"Discriminating scene categories from brain activity within 100 milliseconds","volume":"106","author":"MX Lowe","year":"2018","journal-title":"Cortex"},{"issue":"1","key":"pcbi.1008775.ref005","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.neuron.2019.04.014","article-title":"Rapid invariant encoding of scene layout in human OPA","volume":"103","author":"L Henriksson","year":"2019","journal-title":"Neuron"},{"issue":"11","key":"pcbi.1008775.ref006","doi-asserted-by":"crossref","first-page":"4302","DOI":"10.1523\/JNEUROSCI.17-11-04302.1997","article-title":"The fusiform face area: a module in human extrastriate cortex specialized for face perception","volume":"17","author":"N Kanwisher","year":"1997","journal-title":"Journal of neuroscience"},{"issue":"1","key":"pcbi.1008775.ref007","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-09239-1","article-title":"How face perception unfolds over time","volume":"10","author":"K Dobs","year":"2019","journal-title":"Nature communications"},{"issue":"4","key":"pcbi.1008775.ref008","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1002\/(SICI)1097-0193(1998)6:4<316::AID-HBM9>3.0.CO;2-6","article-title":"A sequence of object-processing stages revealed by fMRI in the human occipital lobe","volume":"6","author":"K Grill-Spector","year":"1998","journal-title":"Human brain mapping"},{"issue":"10","key":"pcbi.1008775.ref009","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1016\/S0042-6989(01)00073-6","article-title":"The lateral occipital complex and its role in object recognition","volume":"41","author":"K Grill-Spector","year":"2001","journal-title":"Vision Research"},{"issue":"3","key":"pcbi.1008775.ref010","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1038\/nn.3635","article-title":"Resolving human object recognition in space and time","volume":"17","author":"RM Cichy","year":"2014","journal-title":"Nature neuroscience"},{"issue":"1","key":"pcbi.1008775.ref011","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1152\/jn.00394.2013","article-title":"The dynamics of invariant object recognition in the human visual system","volume":"111","author":"L Isik","year":"2014","journal-title":"Journal of neurophysiology"},{"key":"pcbi.1008775.ref012","doi-asserted-by":"crossref","first-page":"e36329","DOI":"10.7554\/eLife.36329","article-title":"Ultra-Rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway","volume":"7","author":"Y Mohsenzadeh","year":"2018","journal-title":"Elife"},{"issue":"1","key":"pcbi.1008775.ref013","doi-asserted-by":"crossref","first-page":"8","DOI":"10.3390\/vision3010008","article-title":"Reliability and Generalizability of Similarity-Based Fusion of MEG and fMRI Data in Human Ventral and Dorsal Visual Streams","volume":"3","author":"Y Mohsenzadeh","year":"2019","journal-title":"Vision"},{"issue":"1","key":"pcbi.1008775.ref014","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/0166-2236(92)90344-8","article-title":"Separate visual pathways for perception and action","volume":"15","author":"MA Goodale","year":"1992","journal-title":"Trends in neurosciences"},{"issue":"2","key":"pcbi.1008775.ref015","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/0959-4388(94)90066-3","article-title":"\u2018What\u2019 and \u2018where\u2019 in the human brain","volume":"4","author":"LG Ungerleider","year":"1994","journal-title":"Current opinion in neurobiology"},{"issue":"2","key":"pcbi.1008775.ref016","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.neuron.2013.02.024","article-title":"A motion direction preference map in monkey V4","volume":"78","author":"P Li","year":"2013","journal-title":"Neuron"},{"issue":"6","key":"pcbi.1008775.ref017","doi-asserted-by":"crossref","first-page":"3264","DOI":"10.1152\/jn.00358.2002","article-title":"Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position","volume":"89","author":"JJ DiCarlo","year":"2003","journal-title":"Journal of neurophysiology"},{"issue":"10","key":"pcbi.1008775.ref018","doi-asserted-by":"crossref","first-page":"2370","DOI":"10.1162\/jocn_a_00644","article-title":"The temporal evolution of coarse location coding of objects: Evidence for feedback","volume":"26","author":"R Chakravarthi","year":"2014","journal-title":"Journal of cognitive neuroscience"},{"issue":"15","key":"pcbi.1008775.ref019","doi-asserted-by":"crossref","first-page":"6424","DOI":"10.1073\/pnas.0700622104","article-title":"A feedforward architecture accounts for rapid categorization","volume":"104","author":"T Serre","year":"2007","journal-title":"Proceedings of the national academy of sciences"},{"issue":"3","key":"pcbi.1008775.ref020","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.neuron.2012.01.010","article-title":"How does the brain solve visual object recognition?","volume":"73","author":"JJ DiCarlo","year":"2012","journal-title":"Neuron"},{"issue":"23","key":"pcbi.1008775.ref021","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":"DL Yamins","year":"2014","journal-title":"Proceedings of the national academy of sciences"},{"key":"pcbi.1008775.ref022","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/978-981-10-0213-7_3","volume-title":"Computational and cognitive neuroscience of vision","author":"H Tang","year":"2017"},{"issue":"5","key":"pcbi.1008775.ref023","doi-asserted-by":"crossref","first-page":"e1007001","DOI":"10.1371\/journal.pcbi.1007001","article-title":"Beyond core object recognition: Recurrent processes account for object recognition under occlusion","volume":"15","author":"K Rajaei","year":"2019","journal-title":"PLOS Computational Biology"},{"issue":"6","key":"pcbi.1008775.ref024","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":"Nature neuroscience"},{"issue":"43","key":"pcbi.1008775.ref025","doi-asserted-by":"crossref","first-page":"21854","DOI":"10.1073\/pnas.1905544116","article-title":"Recurrence is required to capture the representational dynamics of the human visual system","volume":"116","author":"TC Kietzmann","year":"2019","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"pcbi.1008775.ref026","unstructured":"Yamins D, Hong H, Cadieu C, DiCarlo JJ. Hierarchical modular optimization of convolutional networks achieves representations similar to macaque IT and human ventral stream. Neural Information Processing Systems Foundation. 2013."},{"issue":"11","key":"pcbi.1008775.ref027","doi-asserted-by":"crossref","first-page":"e1003915","DOI":"10.1371\/journal.pcbi.1003915","article-title":"Deep supervised, but not unsupervised, models may explain IT cortical representation","volume":"10","author":"SM Khaligh-Razavi","year":"2014","journal-title":"PLoS computational biology"},{"issue":"18","key":"pcbi.1008775.ref028","doi-asserted-by":"crossref","first-page":"R921","DOI":"10.1016\/j.cub.2014.08.026","article-title":"Neural networks and neuroscience-inspired computer vision","volume":"24","author":"DD Cox","year":"2014","journal-title":"Current Biology"},{"issue":"1","key":"pcbi.1008775.ref029","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep27755","article-title":"Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence","volume":"6","author":"RM Cichy","year":"2016","journal-title":"Scientific reports"},{"issue":"4","key":"pcbi.1008775.ref030","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.tics.2019.01.009","article-title":"Deep neural networks as scientific models","volume":"23","author":"RM Cichy","year":"2019","journal-title":"Trends in cognitive sciences"},{"issue":"27","key":"pcbi.1008775.ref031","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":"Journal of Neuroscience"},{"issue":"1","key":"pcbi.1008775.ref032","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-61409-0","article-title":"emergence of Visual center-periphery Spatial organization in Deep convolutional neural networks","volume":"10","author":"Y Mohsenzadeh","year":"2020","journal-title":"Scientific reports"},{"key":"pcbi.1008775.ref033","doi-asserted-by":"crossref","unstructured":"Cichy RM, Roig G, Andonian A, Dwivedi K, Lahner B, Lascelles A, et al. The algonauts project: A platform for communication between the sciences of biological and artificial intelligence. arXiv preprint arXiv:190505675. 2019.","DOI":"10.32470\/CCN.2019.1018-0"},{"key":"pcbi.1008775.ref034","unstructured":"Makhzani A, Shlens J, Jaitly N, Goodfellow I, Frey B. Adversarial autoencoders. arXiv preprint arXiv:151105644. 2015."},{"key":"pcbi.1008775.ref035","unstructured":"Goodfellow IJ, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial networks. arXiv preprint arXiv:14062661. 2014."},{"issue":"1","key":"pcbi.1008775.ref036","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1006\/nimg.2001.0978","article-title":"Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","journal-title":"Neuroimage"},{"key":"pcbi.1008775.ref037","first-page":"4","article-title":"Representational similarity analysis-connecting the branches of systems neuroscience","volume":"2","author":"N Kriegeskorte","year":"2008","journal-title":"Frontiers in systems neuroscience"},{"issue":"8","key":"pcbi.1008775.ref038","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.tics.2013.06.007","article-title":"Representational geometry: integrating cognition, computation, and the brain","volume":"17","author":"N Kriegeskorte","year":"2013","journal-title":"Trends in cognitive sciences"},{"key":"pcbi.1008775.ref039","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"A Krizhevsky","year":"2012","journal-title":"Advances in neural information processing systems"},{"issue":"4","key":"pcbi.1008775.ref040","doi-asserted-by":"crossref","first-page":"e1006897","DOI":"10.1371\/journal.pcbi.1006897","article-title":"Deep convolutional models improve predictions of macaque V1 responses to natural images","volume":"15","author":"SA Cadena","year":"2019","journal-title":"PLoS computational biology"},{"key":"pcbi.1008775.ref041","doi-asserted-by":"crossref","DOI":"10.1155\/2011\/879716","article-title":"Brainstorm: a user-friendly application for MEG\/EEG analysis","volume":"2011","author":"F Tadel","year":"2011","journal-title":"Computational intelligence and neuroscience"},{"issue":"7","key":"pcbi.1008775.ref042","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.1088\/0031-9155\/51\/7\/008","article-title":"Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements","volume":"51","author":"S Taulu","year":"2006","journal-title":"Physics in Medicine & Biology"},{"issue":"10","key":"pcbi.1008775.ref043","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1167\/13.10.1","article-title":"Representational dynamics of object vision: the first 1000 ms","volume":"13","author":"T Carlson","year":"2013","journal-title":"Journal of vision"},{"key":"pcbi.1008775.ref044","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.neuroimage.2017.07.022","article-title":"Decoding the orientation of contrast edges from MEG evoked and induced responses","volume":"180","author":"D Pantazis","year":"2018","journal-title":"NeuroImage"},{"key":"pcbi.1008775.ref045","doi-asserted-by":"crossref","unstructured":"Cao Q, Shen L, Xie W, Parkhi OM, Zisserman A. Vggface2: A dataset for recognising faces across pose and age. In: 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018). IEEE; 2018. p. 67\u201374.","DOI":"10.1109\/FG.2018.00020"},{"issue":"3","key":"pcbi.1008775.ref046","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"O Russakovsky","year":"2015","journal-title":"International journal of computer vision"},{"issue":"6","key":"pcbi.1008775.ref047","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","article-title":"Places: A 10 million image database for scene recognition","volume":"40","author":"B Zhou","year":"2017","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"issue":"4","key":"pcbi.1008775.ref048","doi-asserted-by":"crossref","first-page":"e1003553","DOI":"10.1371\/journal.pcbi.1003553","article-title":"A toolbox for representational similarity analysis","volume":"10","author":"H Nili","year":"2014","journal-title":"PLoS computational biology"},{"key":"pcbi.1008775.ref049","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/B978-012375731-9\/50045-8","volume-title":"Neurobiology of attention","author":"A Oliva","year":"2005"},{"issue":"11","key":"pcbi.1008775.ref050","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1162\/jocn_a_01290","article-title":"Tracking the spatiotemporal neural dynamics of real-world object size and animacy in the human brain","volume":"30","author":"SM Khaligh-Razavi","year":"2018","journal-title":"Journal of cognitive neuroscience"},{"issue":"1","key":"pcbi.1008775.ref051","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.jneumeth.2007.03.024","article-title":"Nonparametric statistical testing of EEG-and MEG-data","volume":"164","author":"E Maris","year":"2007","journal-title":"Journal of neuroscience methods"},{"issue":"2","key":"pcbi.1008775.ref052","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.neuroimage.2004.09.040","article-title":"A comparison of random field theory and permutation methods for the statistical analysis of MEG data","volume":"25","author":"D Pantazis","year":"2005","journal-title":"Neuroimage"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1008775","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1008775","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T18:50:49Z","timestamp":1619031049000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1008775"}},"subtitle":[],"editor":[{"given":"Saad","family":"Jbabdi","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,3,24]]},"references-count":52,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3,24]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1008775","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.07.23.218859","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,24]]}}}