Spike timing dependent plasticity (STDP) is a learning rule that modifies synaptic strength
as a function of the relative timing of pre- and postsynaptic spikes. When a neuron is …

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 …

Previous studies have shown that spike-timing-dependent plasticity (STDP) can be used in
spiking neural networks (SNN) to extract visual features of low or intermediate complexity in …

We present a highly hardware friendly STDP (Spike Timing Dependent Plasticity) learning
rule for training Spiking Convolutional Cores in Unsupervised mode and training Fully …

Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal
information processing capability, low power consumption, and high biological plausibility. …

Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-based
approaches due to discrete binary activation and complex spatial-temporal dynamics. …

In this paper we present a very exciting overlap between emergent nanotechnology and
neuroscience, which has been discovered by neuromorphic engineers. Specifically, we are …

In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity
(STDP) using memristors as synapses. Our focus is on how to use individual …

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 the …

Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in
equally dense distracter spike trains can be robustly detected and learned by a single …