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Neural Networks and Computational Learning Theory

 

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NeuroCOLT Technical Report NC-TR-99-037


Noisy Spiking Neurons with Temporal Coding have more
Computational Power than Sigmoidal Neurons



Wolfgang Maass
Institute for Theoretical Computer Science
Technische Universität Graz

Received:22-MAR-1999


Abstract
We exhibit a novel way of simulating sigmoidal neural nets by networks of noisy spiking neurons in temporal coding. Furthermore it is shown that networks of noisy spiking neurons with temporal coding have a strictly larger computational power than sigmoidal neural nets with the same number of units.

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