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 …

Neural network technologies have taken center stage owing to their powerful computing
capability for supporting deep learning in artificial intelligence. However, conventional …

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

A striking difference between brain-inspired neuromorphic processors and current von
Neumann processor architectures is the way in which memory and processing is organized …

Large-scale numerical simulations of detailed brain circuit models are important for
identifying hypotheses on brain functions and testing their consistency and plausibility. An …

While neuromorphic systems may be the ultimate platform for deploying spiking neural
networks (SNNs), their distributed nature and optimization for specific types of models …

Many modern neural simulators focus on the simulation of networks of spiking neurons on
parallel hardware. Another important framework in computational neuroscience, rate-coded …

Computational models are increasingly important for studying complex neurophysiological
systems. As scientific tools, it is essential that such models can be reproduced and critically …

Compared to conventional artificial neural networks, spiking neural networks (SNNs) are
more biologically plausible and require less computation due to their event-driven nature of …

A spiking neural network (SNN) is a type of biological plausibility model that performs
information processing based on spikes. Training a deep SNN effectively is challenging due …