{"id":145,"date":"2010-12-06T11:08:01","date_gmt":"2010-12-06T11:08:01","guid":{"rendered":"http:\/\/spikingneural.net\/?p=145"},"modified":"2013-01-19T15:21:32","modified_gmt":"2013-01-19T15:21:32","slug":"information-transformation-from-a-spatiotemporal-pattern-to-synchrony","status":"publish","type":"post","link":"https:\/\/blog.spikingneural.net\/?p=145","title":{"rendered":"Information Transformation from a Spatiotemporal Pattern to Synchrony"},"content":{"rendered":"<p><!-- p { margin-bottom: 0.21cm; } -->I recently reported <a href=\"http:\/\/spikingneural.net\/?p=132\" target=\"_blank\">here<\/a> on feed forward models of spiking neurons. Here is a follow up about an interesting recurrent system.<\/p>\n<p>The computational power of a reciprocally connected group are likely to entail population codes rather than singular neurons encoding for stimuli. As the spiking neurons are either in a state of firing or not, they are not as easy to decode at a specific moment in time as a rate based model which contain an average of time spread information at one moment. <a href=\"http:\/\/ieeexplore.ieee.org\/xpls\/abs_all.jsp?arnumber=1380170&amp;tag=1\" target=\"_blank\">Hosaka <em>et al<\/em><\/a><em> <\/em>demonstrate a recurrent network organized to generate a synchronous firing according to the cycle of repeated external inputs. The timing of the synchrony depends on the input spatio-temporal pattern and the neural network structure. They conclude that  network self-organizes its transformation function from spatio-temporal to temporal information. <a href=\"http:\/\/spikingneural.net\/?p=108\" target=\"_blank\">spike timing dependant plasticity<\/a> makes the recurrent neural network behave as a filter with only one learned spatio-temporal pattern able to go through the filtering network in synchronous form (for more information on synchrony read here). Although their work includes a Monte-Carlo significance test for the synchrony, the synchrony is based on a global metric. Clearly distributed synchrony in which different cell assemblies in the network synchronise a different times due to the influence of stimuli would have to be considered if the network is to respond to multiple stimuli.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>I recently reported here on feed forward models of spiking neurons. Here is a follow up about an interesting recurrent[&#8230;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[8,7],"tags":[29],"_links":{"self":[{"href":"https:\/\/blog.spikingneural.net\/index.php?rest_route=\/wp\/v2\/posts\/145"}],"collection":[{"href":"https:\/\/blog.spikingneural.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.spikingneural.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.spikingneural.net\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.spikingneural.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=145"}],"version-history":[{"count":1,"href":"https:\/\/blog.spikingneural.net\/index.php?rest_route=\/wp\/v2\/posts\/145\/revisions"}],"predecessor-version":[{"id":308,"href":"https:\/\/blog.spikingneural.net\/index.php?rest_route=\/wp\/v2\/posts\/145\/revisions\/308"}],"wp:attachment":[{"href":"https:\/\/blog.spikingneural.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=145"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.spikingneural.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=145"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.spikingneural.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=145"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}