NDLI: Application of local learning and biological activation functions to networks of neurons for motor control

Content Provider	IEEE Xplore Digital Library
Author	Hugh, G.S. Henriquez, C.S.
Copyright Year	2003
Description	Author affiliation: Dept. of Biomed. Eng., Duke Univ., Durham, NC, USA (Hugh, G.S.; Henriquez, C.S.)
Abstract	Models of networks of neurons involved in motor control have been largely based on concepts derived for artificial neural networks such as global learning and idealized activation functions. The neurons in these models frequently fail to incorporate measured spike rates and baseline, background firing and thus the neuronal outputs may be less useful for testing and developing analysis techniques that can eventually be used on experimental data. In this paper we present an approach for creating large-scale networks of neurons that include local learning and more biological features of neuronal spiking and demonstrate that the models are able to learn a generalized two-dimensional reaching task. This approach opens the possibility for the development of more biologically realistic network models with an increased capacity for adaptation, with a possible tradeoff of reduced learning rates.
Starting Page	233
Ending Page	236
File Size	464169
Page Count	4
File Format	PDF
ISBN	0780375793
DOI	10.1109/CNE.2003.1196801
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-03-20
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Motor drives Biological system modeling Neurons Artificial neural networks Brain modeling Large-scale systems Biomedical measurements Decoding Biomedical engineering Testing
Content Type	Text
Resource Type	Article

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