As a new brain-inspired computational model of the artificial neural network, a spiking
neural network encodes and processes neural information through precisely timed spike …

Deep neural networks with rate-based neurons have exhibited tremendous progress in the
last decade. However, the same level of progress has not been observed in research on …

This paper presents a new spiking neural network for pattern classification problems,
referred to as the Self-Regulating Evolving Spiking Neural (SRESN) classifier, that regulates …

The spiking neural network (SNN) is the third generation of neural networks and performs
remarkably well in cognitive tasks, such as pattern recognition. The temporal neural encode …

This paper presents a new learning algorithm developed for a three layered spiking neural
network for pattern classification problems. The learning algorithm maximizes the interclass …

This paper presents a two stage learning algorithm for a Growing-Pruning Spiking Neural
Network (GPSNN) for pattern classification problems. The GPSNN uses three layered …

This paper presents a biologically inspired, sequential learning spiking neural classifier
(SLSNC) for pattern classification problems. It consists of a two layered neural network and a …

Spiking neural networks (SNNs), termed as the third generation of neural networks, are
inspired by the information processing mechanisms employed by biological neurons in the …

Evolving spiking neural networks (ESNNs) evolve the output neurons dynamically based on
the information presented in the incoming samples and the information stored in the …

ABSTRACT Evolving Spiking Neural Network (ESNN) is widely used in classification
problem. However, ESNN like any other neural networks is incapable to find its own …