Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …

Applications that generate huge amounts of data in the form of fast streams are becoming
increasingly prevalent, being therefore necessary to learn in an online manner. These …

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …

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

In order to understand how the mammalian neocortex is performing computations, two
things are necessary; we need to have a good understanding of the available neuronal …

The field of Deep Learning (DL) has seen a remarkable series of developments with
increasingly accurate and robust algorithms. However, the increase in performance has …

The spiking neural network architecture (SpiNNaker) project aims to deliver a massively
parallel million-core computer whose interconnect architecture is inspired by the connectivity …

Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for
recognizing multivariate responses obtained by gas sensors in various applications. Over …

Cognition and behavior emerge from brain network interactions, such that investigating
causal interactions should be central to the study of brain function. Approaches that …

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network
simulations using biologically realistic connectivity. This simulation environment enables the …