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Neural networks / Neuroscience / Cybernetics / Non-linear systems / Computational statistics / Hodgkin–Huxley model / Neuromorphic engineering / Biological neuron model / Mathematical model / Computational neuroscience / Biology / Science


Dynamic State and Parameter Estimation applied to Neuromorphic Systems Emre Neftci∗,1 , Bryan Toth†,2 , Giacomo Indiveri,1 , and Henry D. I. Abarbanel2,3 1
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Document Date: 2012-03-07 07:38:31


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File Size: 1,77 MB

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City

La Jolla / /

Company

Mead / Wilson / Marine Physical Laboratory / /

Country

Switzerland / United States / /

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Facility

International Neuroinformatics Coordinating Facility / University of California / University of Zurich / Institute of Neuroinformatics / /

IndustryTerm

off-chip / functional neural imaging data / hardware systems / studied neural systems / numerical integration algorithm / neuromorphic multi-neuron chips / neural network / computational technologies / non-linear systems / neural networks / typical solution / software simulations / hardware neural systems / communication protocol / biological neural systems / neuromorphic multi-chip / optimization software / neuromorphic systems / neural systems / parallel networks / linear systems / /

Organization

University of Zurich / Scripps Institution of Oceanography / Center for Theoretical Biological Physics / Institute of Neuroinformatics / Department of Physics and Marine Physical Laboratory / U.S. Securities and Exchange Commission / University of California / San Diego / /

Person

Giacomo Indiveri / Bryan Toth / Deco / Ben Dayan Rubin / /

ProvinceOrState

California / /

Technology

neuroscience / neural network / neuromorphic multi-chip / communication protocol / neuromorphic multi-neuron chips / numerical integration algorithm / neuromorphic VLSI chip / /

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