{"componentChunkName":"component---src-templates-blog-post-js","path":"/blog/cortical-response-to-naturalistic-stimuli-is-largely-predictable-with-deep-neural-networks/","result":{"data":{"site":{"siteMetadata":{"title":"No Frills News"}},"contentfulNfnPost":{"postTitle":"Cortical response to naturalistic stimuli is largely predictable with deep neural networks","slug":"cortical-response-to-naturalistic-stimuli-is-largely-predictable-with-deep-neural-networks","createdLocal":"2021-05-29 14:31:03.646760","publishDate":"2021-05-01 00:00:00","feedName":"Image Recognition","sourceUrl":{"sourceUrl":"https://advances.sciencemag.org/content/7/22/eabe7547"},"postSummary":{"childMarkdownRemark":{"html":"<p>Together, our results highlight the advantages and ubiquitous applications of DNN encoding models of naturalistic stimuli.\nIn contrast to existing works that use a linear response model ( 4 , 7 ), we propose a convolutional neural network (CNN)–based response model where stimulus features are mapped onto neural data using nonlinear transformations.\nMost studies in visual encoding remain limited to static stimuli and evoked responses in relatively small cortical populations.\nHere, we demonstrate that encoding models trained with naturalistic data are not limited to modeling responses of their constrained stimuli set.\nHowever, previous research with naturalistic stimuli has shown that some brain regions maintain memory of the order of minutes during naturalistic viewing (49).</p>"}}}},"pageContext":{"slug":"cortical-response-to-naturalistic-stimuli-is-largely-predictable-with-deep-neural-networks"}}}