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Solving Nonlinear Optimization Problems Using Networks Of Spiking Neurons (bibtex)
  author = {Malaka, Rainer and Buck, Sebastian},
  title = {Solving Nonlinear Optimization Problems Using Networks Of Spiking
  booktitle = {{IEEE} International Joint Conference on Neural Networks},
  year = {2000},
  volume = {6},
  pages = {486–491},
  abstract = {Most artificial neural networks used in practical applications are
	based on simple neuron types in a multi-layer architecture. Here,
	we propose to solve optimization problems using a fully recurrent
	network of spiking neurons mimicking the response behavior of biological
	neurons. Such networks can compute a series of different solutions
	for a given problem and converge into a periodical sequence of such
	solutions. The goal of this paper is to prove that neural networks
	like the {SRM} (Spike Response Model) are able to solve nonlinear
	optimization problems. We demonstrate this for the traveling salesman
	problem. Our network model is able to compute multiple solutions
	and can use its dynamics to leave local minima in which classical
	models would be stuck. For adapting the model, we introduce a suitable
	network architecture and show how to encode the problem directly
	into the network weights.}
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Last edited 29.01.2013 17:37 by Quirin Lohr