Guided by brain-like 'spiking'computational frameworks, neuromorphic computing—brain-
inspired computing for machine intelligence—promises to realize artificial intelligence while …

With their working mechanisms based on ion migration, the switching dynamics and
electrical behaviour of memristive devices resemble those of synapses and neurons, making …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference
because the neurons in the networks are sparsely activated and computations are event …

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 …

Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

Spiking neural networks (SNNs) are promising in ascertaining brain-like behaviors since
spikes are capable of encoding spatio-temporal information. Recent schemes, eg, pre …

Deep spiking neural networks (SNNs) hold the potential for improving the latency and
energy efficiency of deep neural networks through data-driven event-based computation …

Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief
Networks (DBNs) represent the state-of-the-art for many machine learning and computer …