文章基本信息

标题：Robust Reservoir Generation by Correlation-Based Learning
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作者：Tadashi Yamazaki ; Shigeru Tanaka
期刊名称：Advances in Artificial Neural Systems
印刷版ISSN：1687-7594
电子版ISSN：1687-7608
出版年度：2009
卷号：2009
DOI：10.1155/2009/467128
出版社：Hindawi Publishing Corporation
摘要：Reservoir computing (RC) is a new framework for neural computation. A reservoir is usually a recurrent neural network with fixed random connections. In this article, we propose an RC model in which the connections in the reservoir are modifiable. Specifically, we consider correlation-based learning (CBL), which modifies the connection weight between a given pair of neurons according to the correlation in their activities. We demonstrate that CBL enables the reservoir to reproduce almost the same spatiotemporal activity patterns in response to an identical input stimulus in the presence of noise. This result suggests that CBL enhances the robustness in the generation of the spatiotemporal activity pattern against noise in input signals. We apply our RC model to trace eyeblink conditioning. The reservoir bridged the gap of an interstimulus interval between the conditioned and unconditioned stimuli, and a readout neuron was able to learn and express the timed conditioned response.