The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

Spiking Neural Networks (SNN) were shown to be suitable tools for the processing of spatio-
temporal information. However, due to their inherent complexity, the formulation of efficient …

Epilepsy is a prevalent neurological condition characterized by repeated seizures resulting
from structural or functional changes in the brain. Standard treatment approaches include …

In a previous work (Mohemmed et al., Method for training a spiking neuron to associate input–
output spike trains)[1] we have proposed a supervised learning algorithm based on temporal …

The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming
known in increasing detail, but the computational principles by which cortical plasticity …

This work investigates adaptive behaviours for an intelligent robotic agent when subjected to
temporal stimuli consisting of associations of contextual cues and simple reflexes. This is …

Spiking model neurons can be set up to respond selectively to specific spatio-temporal spike
patterns by optimization of their input weights. It is unknown, however, if existing synaptic …

In a previous work (Mohemmed et al.[11]), the authors proposed a supervised learning
algorithm to train a spiking neuron to associate input/output spike patterns. In this paper, the …

The paper proposes a new supervised learning algorithm for spiking neural networks
(SNNs). This algorithm is based on the error back-propagation algorithms. However, it is …

Abstract Information processes in the brain, such as gene and protein expression, learning,
memory, perception, cognition, consciousness are all spatio-and/or spectro temporal …