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Abstract: Recently one has started to investigate the computational power of spiking neurons (also called "integrate and fire neurons"). These are neuron models that are substantially more realistic from the biological point of view than the ones which are traditionally employed in artificial neural nets. It has turned out that the computational power of networks of spiking neurons is quite large. In particular they have the ability to communicate and manipulate analog variables in spatio-temporal... (Update)
Context of citations to this paper: More
.... is the fact that these networks are capable of computing functions with fewer neurons than multi layer perceptrons on some problems [5]. The first example function that demonstrates this is termed coincidence detection (CD n : B 2n B ) Formally, CD n (x 1 ; x n ;...
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BibTeX entry: (Update)
W. Maass and B. Ruf. The computational power of spiking neurons depends on the shape of the postsynaptic potential. Technical Report TR96-025, Electronic Colloquium on Computational Complexity, 1996. http://citeseer.ist.psu.edu/maass96computational.html More
@article{ maass96computational,
author = "Wolfgang Maass and Berthold Ruf",
title = "The Computational Power of Spiking Neurons Depends on the Shape of the Postsynaptic Potentials",
journal = "Electronic Colloquium on Computational Complexity (ECCC)",
volume = "3",
number = "025",
year = "1996",
url = "citeseer.ist.psu.edu/maass96computational.html" }
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