We review different aspects of the simulation of spiking neural networks. We start by
reviewing the different types of simulation strategies and algorithms that are currently …

For simulations of neural networks, there is a trade-off between the size of the network that
can be simulated and the complexity of the model used for individual neurons. In this study …

Abstract Spiking Neural Networks (SNNs) are biologically-inspired models that are capable
of processing information in streams of action potentials. However, simulating and training …

This is a book about 'Neurodynamics'. What we mean is that this is a book about how ideas
from dynamical systems theory have been developed and employed in recent years to give …

This paper proposes a spiking-neural-network-based robot controller inspired by the control
structures of biological systems. Information is routed through the network using facilitating …

Despite the fact that neural dynamics is triggered by discrete synaptic events, the neural
response is usually obtained within the diffusion approximation representing the synaptic …

The main contribution of this letter is the derivation of a steepest gradient descent learning
rule for a multilayer network of theta neurons, a one-dimensional nonlinear neuron model …

NEVESIM is a software package for event-driven simulation of networks of spiking neurons
with a fast simulation core in C++, and a scripting user interface in the Python programming …

The simulation of spiking neural networks (SNNs) is known to be a very time-consuming
task. This limits the size of SNN that can be simulated in reasonable time or forces users to …

Neural modelling tools are increasingly employed to describe, explain, and predict the
human brain's behavior. Among them, spiking neural networks (SNNs) make possible the …