NDLI: Neuromorphic modeling abstractions and simulation of large-scale cortical networks

Content Provider	IEEE Xplore Digital Library
Author	Krichmar, J.L. Dutt, N. Nageswaran, J.M. Richert, M.
Copyright Year	2011
Description	Author affiliation: Department of Computer Science, University of California, Irvine, Irvine, CA, USA (Dutt, N.) \|\| Department of Cognitive Sciences (Krichmar, J.L.) \|\| Brain Corporation, San Diego, CA, USA (Nageswaran, J.M.; Richert, M.)
Abstract	Biological neural systems are well known for their robust and power-efficient operation in highly noisy environments. We outline key modeling abstractions for the brain and focus on spiking neural network models. We discuss aspects of neuronal processing and computational issues related to modeling these processes. Although many of these algorithms can be efficiently realized in specialized hardware, we present a case study of simulation of the visual cortex using a GPU based simulation environment that is readily usable by neuroscientists and computer scientists and efficient enough to construct very large networks comparable to brain networks.
Starting Page	334
Ending Page	338
File Size	709539
Page Count	5
File Format	PDF
ISBN	9781457713996
ISSN	10923152
e-ISBN	9781457714009
e-ISBN	9781457713989
DOI	10.1109/ICCAD.2011.6105350
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Computational modeling Brain models Biological system modeling Biological neural networks Visualization synapse Spiking neural networks GPU vision computational neuroscience parallel processing
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Computer Science Applications Software

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