Noisy Spiking Neurons with Temporal Coding have more Computational Power than Sigmoidal Neurons
نویسنده
چکیده
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.
منابع مشابه
An Eecient Implementation of Sigmoidal Neural Nets in Temporal Coding with Noisy Spiking Neurons
We show that networks of relatively realistic mathematical models for biological neurons can in principle simulate arbitrary feedforward sigmoidal neural nets in a way which has previously not been considered. This new approach is based on temporal coding by single spikes (respectively by the timing of synchronous ring in pools of neurons), rather than on the traditional interpretation of analo...
متن کاملThe Computational Power of Spiking Neurons Depends on the Shape of the Postsynaptic Potentials
Recently one has started to investigate the computational power of spiking neurons (also called \integrate and re neurons"). These are neuron models that are substantially more realistic from the biological point of view than the ones which are traditionally employed in arti cial neural nets. It has turned out that the computational power of networks of spiking neurons is quite large. In partic...
متن کاملThe Computational Power of SpikingNeurons Depends on the Shape of
Recently one has started to investigate the computational power of spiking neurons (also called \integrate and re neurons"). These are neuron models that are substantially more realistic from the biological point of view than the ones which are traditionally employed in arti cial neural nets. It has turned out that the computational power of networks of spiking neurons is quite large. In partic...
متن کاملNetworks of spiking neurons: The third generation of neural network models
The computational power of formal models for networks of spiking neurons is compared with that of other neural network models based on McCulloch Pitts neurons (i.e. threshold gates) respectively sigmoidal gates. In particular it is shown that networks of spiking neurons are computationally more powerful than these other neural network models. A concrete biologically relevant function is exhibit...
متن کاملNetworks of Spiking Neurons :
The computational power of formal models for networks of spiking neurons is compared with that of other neural network models based on McCulloch Pitts neurons (i.e. threshold gates) respectively sigmoidal gates. In particular it is shown that networks of spiking neurons are computationally more powerful than these other neural network models. A concrete biologically relevant function is exhibit...
متن کامل