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Neural networks / Neurophysiology / Computational neuroscience / Cell signaling / Hebbian theory / Synaptic weight / Synaptic plasticity / Spike-timing-dependent plasticity / Chemical synapse / Biology / Neuroscience / Nervous system


Effectiveness of Neural Network Learning Rules Generated by a Biophysical Model of Synaptic Plasticity David J. Jilk, Daniel M. Cer, and Randall C. O’Reilly Department of Psychology University of Colorado, Boulder 345
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Document Date: 2003-06-14 00:35:46


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City

Berlin / Cambridge / /

Company

Yeung L.C. / PDP Research Group / MIT Press / /

Country

United States / /

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Event

FDA Phase / /

Facility

Carnegie Mellon University / Psychology University of Colorado / /

IndustryTerm

learning networks / artificial networks / artificial neural network / presynaptic frequency protocol / neural network / spike-timing protocol / artificial neural networks / biological and artificial neural networks / physical systems / experimental protocols / /

Organization

Carnegie Mellon University / Randall C. O’Reilly Department / National Academy of Sciences / MIT / University of Colorado / Boulder / /

Person

Redman / Spike / Daniel M. Cer / David J. Jilk / /

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Position

representative / /

Product

Ca2 / /

PublishedMedium

Proceedings of the National Academy of Sciences / Mathematics of Computation / /

Technology

Neuroscience / simulation / Neural Network / LTP Pairing Protocol / pairing protocol / spike-timing protocol / presynaptic frequency protocol / experimental protocols / /

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