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To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e.,\n                    <jats:italic>z<\/jats:italic>\n                    - and\n                    <jats:italic>w<\/jats:italic>\n                    -latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled\n                    <jats:italic>w<\/jats:italic>\n                    representations outperform both\n                    <jats:italic>z<\/jats:italic>\n                    and CLIP representations in explaining neural responses. Further,\n                    <jats:italic>w<\/jats:italic>\n                    -latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1012058","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T14:02:52Z","timestamp":1715004172000},"page":"e1012058","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4462-4256","authenticated-orcid":true,"given":"Thirza","family":"Dado","sequence":"first","affiliation":[]},{"given":"Paolo","family":"Papale","sequence":"additional","affiliation":[]},{"given":"Antonio","family":"Lozano","sequence":"additional","affiliation":[]},{"given":"Lynn","family":"Le","sequence":"additional","affiliation":[]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Marcel","family":"van Gerven","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1625-0034","authenticated-orcid":true,"given":"Pieter","family":"Roelfsema","sequence":"additional","affiliation":[]},{"given":"Ya\u011fmur","family":"G\u00fc\u00e7l\u00fct\u00fcrk","sequence":"additional","affiliation":[]},{"given":"Umut","family":"G\u00fc\u00e7l\u00fc","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2024,5,6]]},"reference":[{"issue":"6005","key":"pcbi.1012058.ref001","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1126\/science.1194908","article-title":"Functional compartmentalization and viewpoint generalization within the macaque face-processing system","volume":"330","author":"WA Freiwald","year":"2010","journal-title":"Science"},{"issue":"8","key":"pcbi.1012058.ref002","doi-asserted-by":"crossref","first-page":"e1003724","DOI":"10.1371\/journal.pcbi.1003724","article-title":"Unsupervised feature learning improves prediction of human brain activity in response to natural images","volume":"10","author":"U G\u00fc\u00e7l\u00fc","year":"2014","journal-title":"PLoS computational biology"},{"issue":"23","key":"pcbi.1012058.ref003","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"},{"issue":"12","key":"pcbi.1012058.ref004","doi-asserted-by":"crossref","first-page":"e1003963","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 computational biology"},{"issue":"11","key":"pcbi.1012058.ref005","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":"27","key":"pcbi.1012058.ref006","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":"3","key":"pcbi.1012058.ref007","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":"DL Yamins","year":"2016","journal-title":"Nature neuroscience"},{"issue":"1","key":"pcbi.1012058.ref008","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"},{"key":"pcbi.1012058.ref009","unstructured":"G\u00fc\u00e7l\u00fc U, Thielen J, Hanke M, van Gerven M. 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