{"action":"opened","issue":{"url":"https://api.github.com/repos/ctn-waterloo/modelling_ideas/issues/1","labels_url":"https://api.github.com/repos/ctn-waterloo/modelling_ideas/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/ctn-waterloo/modelling_ideas/issues/1/comments","events_url":"https://api.github.com/repos/ctn-waterloo/modelling_ideas/issues/1/events","html_url":"https://github.com/ctn-waterloo/modelling_ideas/issues/1","id":72252117,"number":1,"title":"Neural connectivity due to contextual learning","user":{"login":"tcstewar","id":2380772,"avatar_url":"https://avatars.githubusercontent.com/u/2380772?v=3","gravatar_id":"","url":"https://api.github.com/users/tcstewar","html_url":"https://github.com/tcstewar","followers_url":"https://api.github.com/users/tcstewar/followers","following_url":"https://api.github.com/users/tcstewar/following{/other_user}","gists_url":"https://api.github.com/users/tcstewar/gists{/gist_id}","starred_url":"https://api.github.com/users/tcstewar/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/tcstewar/subscriptions","organizations_url":"https://api.github.com/users/tcstewar/orgs","repos_url":"https://api.github.com/users/tcstewar/repos","events_url":"https://api.github.com/users/tcstewar/events{/privacy}","received_events_url":"https://api.github.com/users/tcstewar/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-04-30T18:58:42Z","updated_at":"2015-04-30T18:58:42Z","closed_at":null,"body":"Chris mentioned that an early talk he saw was on messing around with auditory localization in owls.  You put prism glasses on an owl to mess up its mapping between where it sees something and where it actually is.  After a while, the owl adapts.  Then you take the glasses off, and it takes a while to adapt back.  But soon it gets really good at swapping back and forth between those states.  Importantly, they went and looked at what's happening to neural connectivity across this time, and they found that new connections formed, but it didn't really get rid of the old ones, allowing it to swap back and forth.\r\n\r\nThe traditional explanation is that there's some other mechanism that disables the old connections when in the new context (i.e. when the glasses are on), but it strikes me that this is exactly what we'd expect with a model where the state contains context information, and we just use the standard PES rule to do error-driven training.  (Note: \"forming new connections\" here would mean \"connection weights starting near zero and then getting larger due to learning)."}}