<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Nessler</style></author><author><style face="normal" font="default" size="100%">M. Pfeiffer</style></author><author><style face="normal" font="default" size="100%">W. Maass</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">STDP enables spiking neurons to detect hidden causes of their inputs</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS 2009: Advances in Neural Information Processing Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><volume><style face="normal" font="default" size="100%">22</style></volume><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The principles by which spiking neurons contribute to the astounding   computational power of generic cortical microcircuits, and how   spike-timing-dependent plasticity (STDP) of synaptic weights could generate   and maintain this computational function, are unknown. We show here that   STDP, in conjunction with a stochastic soft winner-take-all (WTA) circuit,   induces spiking neurons to generate through their synaptic weights implicit   internal models for subclasses (or &amp;ldquo;causes&amp;rdquo;) of the high-dimensional spike   patterns of hundreds of pre-synaptic neurons. Hence these neurons will fire   after learning whenever the current input best matches their internal model.   The resulting computational function of soft WTA circuits, a common network   motif of cortical microcircuits, could therefore be a drastic dimensionality   reduction of information streams, together with the autonomous creation of   internal models for the probability distributions of their input patterns. We   show that the autonomous generation and maintenance of this computational   function can be explained on the basis of rigorous mathematical principles.   In particular, we show that STDP is able to approximate a stochastic online   Expectation-Maximization (EM) algorithm for modeling the input data. A   corresponding result is shown for Hebbian learning in artificial neural   networks.&lt;/p&gt;</style></abstract></record></records></xml>