Fast computation with neural oscillators

被引：10

作者：

Wang, Wei ^{[1
]}

Slotine, Jean-Jacques E. ^{[1
]}

机构：

[1] MIT, Nonlinear Syst Lab, Cambridge, MA 02139 USA

来源：

NEUROCOMPUTING | 2006年 / 69卷 / 16-18期

基金：

美国国家卫生研究院;

关键词：

neural network; FitzHugh-Nagumo neuron; winner-take-all; coincidence detection;

D O I：

10.1016/j.neucom.2005.04.012

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies new spike-based models for winner-take-all computation and coincidence detection. In both cases, fast convergence is achieved independent of initial conditions, and network complexity is linear in the number of inputs. Fully distributed versions can be modelled based on groups of interneurons connected through electrical synapses. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：2320 / 2326

页数：7

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