This paper presents a comprehensive overview of modelling, simulation and implementation
of neural networks, taking into account that two aims have emerged in this area: the …

Brian 2 allows scientists to simply and efficiently simulate spiking neural network models.
These models can feature novel dynamical equations, their interactions with the …

The development of spiking neural network simulation software is a critical component
enabling the modeling of neural systems and the development of biologically inspired …

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 …

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …

Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence
(AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy …

Elucidating the intricate neural mechanisms underlying brain functions requires integrative
brain dynamics modeling. To facilitate this process, it is crucial to develop a general-purpose …

Advances in experimental techniques and computational power allowing researchers to
gather anatomical and electrophysiological data at unprecedented levels of detail have …

Abstract Spiking Neural Network (SNN) is a type of biologically-inspired neural networks that
perform information processing based on discrete-time spikes, different from traditional …

Spiking neural P systems (SN P systems) are a class of distributed and parallel neural-like
computing models, inspired from the way neurons communicate by means of spikes. In this …