000117796 001__ 117796
000117796 005__ 20180317093947.0
000117796 037__ $$aCONF
000117796 245__ $$aA Model for Real-Time Computation in Generic Neural Microcircuits
000117796 269__ $$a2003
000117796 260__ $$bMIT Press$$c2003
000117796 336__ $$aConference Papers
000117796 490__ $$aAdvances in Neural Information Processing Systems$$v15
000117796 520__ $$aA key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real-time. We propose a new computational model that is based on principles of high dimensional dynamical systems in combination with statistical learning theory. It can be implemented on generic evolved or found recurrent circuitry
000117796 700__ $$aMaass, W.
000117796 700__ $$aNatschläger, T.
000117796 700__ $$0240392$$aMarkram, H.$$g150822
000117796 700__ $$aBecker, S.
000117796 700__ $$aThrun, S.
000117796 700__ $$aObermayer, K.
000117796 7112_ $$aNIPS 2002$$cVancouver, British Columbia$$dDecember 12-14, 2002
000117796 909CO $$ooai:infoscience.tind.io:117796$$pSV$$pconf
000117796 909C0 $$0252120$$pLNMC$$xU10458
000117796 937__ $$aLNMC-CONF-2008-001
000117796 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000117796 980__ $$aCONF