Supervised Learning with Spiking Neural Networks
نویسندگان
چکیده
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 the spiking neural network is capable of performing nonlinearly separable classification tasks. Several issues concerning the spiking neural network are discussed.
منابع مشابه
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...
متن کاملA 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 ...
متن کاملReSuMe - Proof of convergence
We consider learning convergence in Spiking Neural Networks trained according to the ReSuMe learning rule. We begin with a short introduction of the ReSuMe learning rules and we define the learning scenarios to be considered. Next, we present a formal proof of learning convergence for the Spike Response Model (SRM) trained according to the defined scenarios. Finally, we demonstrate that the pro...
متن کاملLearning in large-scale spiking neural networks
Learning is central to the exploration of intelligence. Psychology and machine learning provide high-level explanations of how rational agents learn. Neuroscience provides lowlevel descriptions of how the brain changes as a result of learning. This thesis attempts to bridge the gap between these two levels of description by solving problems using machine learning ideas, implemented in biologica...
متن کاملSupervised learning based on temporal coding in spiking neural networks
Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is...
متن کامل