{"id":2377,"date":"2020-12-22T16:41:06","date_gmt":"2020-12-22T06:41:06","guid":{"rendered":"https:\/\/www.cognav.net\/?p=2377"},"modified":"2020-12-23T10:42:37","modified_gmt":"2020-12-23T00:42:37","slug":"how-the-brain-encodes-abstract-state-space-representations-in-high-dimensional-environments","status":"publish","type":"post","link":"https:\/\/braininspirednavigation.com\/?p=2377","title":{"rendered":"How does the brain encode abstract state-space representations in high-dimensional environments?"},"content":{"rendered":"<p style=\"text-align: justify;\">Cross L, Cockburn J, Yue Y, O&#8217;Doherty JP. <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0896627320308990\"><strong>Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments<\/strong><\/a>. Neuron. 2020 Dec 7:S0896-6273(20)30899-0. doi: 10.1016\/j.neuron.2020.11.021.<\/p>\n<p style=\"text-align: justify;\">In Brief<br \/>\n&#8220;<strong><span style=\"color: #ff0000;\">Cross et al. scanned humans playing Atari games and utilized a deep reinforcement learning algorithm as a model for how humans can map high-dimensional sensory inputs in actions<\/span><\/strong>.\u00a0Representations in the intermediate layers of the algorithm were used to predict behavior and neural activity throughout a sensorimotor pathway.&#8221;<\/p>\n<p style=\"text-align: justify;\">Summary<br \/>\n&#8220;<strong><span style=\"color: #ff0000;\">Humans possess an exceptional aptitude to efficiently make decisions from high-dimensional sensory observations<\/span><\/strong>. However, <strong><span style=\"color: #ff0000;\">it is unknown how the brain compactly represents the current state of the environment to guide this process<\/span><\/strong>. The deep Q-network (DQN) achieves this by capturing highly nonlinear mappings from multivariate inputs to the values of potential actions. We deployed DQN as a model of brain activity and behavior in participants playing three Atari video games during fMRI. Hidden layers of DQN exhibited a striking resemblance to voxel activity in a distributed sensorimotor network, extending throughout the dorsal visual pathway into posterior parietal cortex.<strong><span style=\"color: #ff0000;\"> Neural state-space representations emerged from nonlinear transformations of the pixel space bridging perception to action and reward<\/span><\/strong>. <strong><span style=\"color: #ff0000;\">These transformations reshape axes to reflect relevant high-level features and strip away information about task-irrelevant sensory features<\/span><\/strong>. Our findings shed light on the neural encoding of task representations for decision-making in real-world situations.&#8221;<\/p>\n<p style=\"text-align: justify;\">Cross L, Cockburn J, Yue Y, O&#8217;Doherty JP. <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0896627320308990\"><strong>Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments<\/strong><\/a>. Neuron. 2020 Dec 7:S0896-6273(20)30899-0. doi: 10.1016\/j.neuron.2020.11.021.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Cross L, Cockburn J, Yue Y, O&#8217;Doherty JP. Using deep reinforcement learning to reveal how the brain encodes abstract state-space representations in high-dimensional environments. Neuron. 2020 Dec 7:S0896-6273(20)30899-0. doi: 10.1016\/j.neuron.2020.11.021. In Brief &#8220;Cross et al. scanned humans playing Atari games and utilized a deep reinforcement learning algorithm as a model for how humans can map [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[389,96,419],"tags":[868,587,204,867,869,772,243,870],"_links":{"self":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/2377"}],"collection":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2377"}],"version-history":[{"count":2,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/2377\/revisions"}],"predecessor-version":[{"id":2379,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/2377\/revisions\/2379"}],"wp:attachment":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2377"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2377"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2377"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}