## Analog VLSI Stochastic Perturbative Learning Architectures (1997)

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Venue: | J. Analog Integrated Circuits and Signal Processing |

Citations: | 15 - 7 self |

### BibTeX

@INPROCEEDINGS{Cauwenberghs97analogvlsi,

author = {Gert Cauwenberghs},

title = {Analog VLSI Stochastic Perturbative Learning Architectures},

booktitle = {J. Analog Integrated Circuits and Signal Processing},

year = {1997},

pages = {195--209}

}

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### Abstract

We present analog VLSI neuromorphic architectures for a general class of learning tasks, which include supervised learning, reinforcement learning, and temporal di erence learning. The presented architectures are parallel, cellular, sparse in global interconnects, distributed in representation, and robust to noise and mismatches in the implementation. They use a parallel stochastic perturbation technique to estimate the e ect of weight changes on network outputs, rather than calculating derivatives based on a model of the network. This \model-free " technique avoids errors due to mismatchesinthephysical implementation of the network, and more generally allows to train networks of which the exact characteristics and structure are not known. With additional mechanisms of reinforcement learning, networks of fairly general structure are trained e ectively from an arbitrarily supplied reward signal. No prior assumptions are required on the structure of the network nor on the speci cs of the desired network response.