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Sigmoid-like activation functions implemented in analog hardware differ in various ways from the standard sigmoidal function as they are asymmetric, truncated, and have a non-standard gain. It is demonstrated how one can adapt the backpropagation learning ...
A correlation-based (Hebbian'') learning rule at the spike level is formulated, mathematically analyzed, and compared with learning in a firing-rate description. As for spike coding, we take advantage of a learning window'' that describes the effect of ...
All-optical multilayer perceptrons differ in various ways from the ideal neural network model. Examples are the use of non-ideal activation functions which are truncated, asymmetric, and have a non-standard gain, restriction of the network parameters to no ...
An unresolved paradox exists in auditory and electrosensory neural systems {Carr93,Heiligenberg91}: they encode behaviourally relevant signals in the range of a few microseconds with neurons that are at least one order of magnitude slower. We take the barn ...
All-optical multilayer perceptrons differ in various ways from the ideal neural network model. Examples are the use of non-ideal activation functions which are truncated, asymmetric, and have a non-standard gain, restriction of the network parameters to no ...
Research on artificial neural networks (ANNs) has been carried out for more than five decades. A renewed interest appeared in the 80's with the finding of powerful models like J. Hopfield's recurrent networks, T. Kohonen's self-organizing feature maps, and ...
The domain of artificial neural networks has evolved rapidly during the last decade, and many research groups are presently working on new neuronal algorithms and investigating their potential for technological applications. The idea to use biologically in ...