Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Various hypotheses of information representation in brain, referred to as neural codes, have
been proposed to explain the information transmission between neurons. Neural coding …

As an emerging in-memory element, memristor has been widely used in various neural
network circuits to represent the weights and accelerate the calculation. However, the …

The performance of a biologically plausible spiking neural network (SNN) largely depends
on the model parameters and neural dynamics. This article proposes a parameter …

Memristive devices, known for their nonvolatile resistive switching, are promising
components for next-generation neuromorphic computing systems, which mimic the brain's …

Recent breakthroughs in neuromorphic computing show that local forms of gradient descent
learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity …

Resistive Crossbar Arrays present an elegant implementation solution for Deep Neural
Networks acceleration. The Matrix-Vector Multiplication, which is the corner-stone of DNNs …

Deep neural networks (DNNs), as a subset of machine learning (ML) techniques, entail that
real-world data can be learned, and decisions can be made in real time. However, their wide …

Recent breakthroughs suggest that local, approximate gradient descent learning is
compatible with Spiking Neural Networks (SNNs). Although SNNs can be scalably …

The proliferation of machine learning algorithms in everyday applications such as image
recognition or language translation has increased the pressure to adapt underlying …