Supervised Associative Learning in Spiking Neural Network

Supervised and unsupervised weight and delay adaptation learning in temporal coding spiking neural networks

Artificial neural networks are learning paradigms which mimic the biological neu­ ral system. The temporal coding Spiking Neural Network, a relatively new artifi­ cial neural network paradigm, is considered to be computationally more powerful than the conventional neural network. Research on the network of spiking neurons is an emerging field and has potential for wider investigation. This rese...

Supervised Learning with Spiking Neural Networks

In this paper we derive a supervised learning algorithm for a spiking neural network which encodes information in the timing of spike trains. This algorithm is similar to the classical error back propagation algorithm for sigmoidal neural network but the learning parameter is adaptively changed. The algorithm is applied to the complex nonlinear classification problem and the results show that t...

Supervised learning in spiking neural networks

A Survey on Pattern Recognition Using Spiking Neural Networks with Temporal Encoding and Learning

This paper, recognize of the patterns using spiking neural networks with temporal encoding and learning. Neural networks place the important role in cognitive and decision making process. Processing the different type of inputs lead to find the discriminate the pattern. Leaky Integrate Fire Neurons are used to recognize the patterns. During the recognition supervised learning method is used to ...

Training of spiking neural networks based on information theoretic costs

Spiking neural network is a type of artificial neural network in which neurons communicate between each other with spikes. Spikes are identical Boolean events characterized by the time of their arrival. A spiking neuron has internal dynamics and responds to the history of inputs as opposed to the current inputs only. Because of such properties a spiking neural network has rich intrinsic capabil...