Abstract Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue
for low power spike event based computation. However, the spike generation function is non …

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 …

Abstract Recurrent Neural Networks (RNNs) have become the state-of-the-art choice for
extracting patterns from temporal sequences. Current RNN models are ill suited to process …

Spiking neural networks (SNNs) are positioned to enable spatio-temporal information
processing and ultra-low power event-driven neuromorphic hardware. However, SNNs are …

Neuromorphic data, recording frameless spike events, have attracted considerable attention
for the spatiotemporal information components and the event-driven processing fashion …

Spiking neural networks (SNNs) well support spatiotemporal learning and energy-efficient
event-driven hardware neuromorphic processors. As an important class of SNNs, recurrent …

This paper introduces a novel methodology for training an event-driven classifier within a
Spiking Neural Network (SNN) System capable of yielding good classification results when …

Spiking Neural Networks (SNNs) are bio-inspired networks that process information
conveyed as temporal spikes rather than numeric values. An example of a sensor providing …

Abstract Spiking Neural Networks (SNNs) have shown substantial promise in processing
spatio-temporal data, mimicking biological neuronal mechanisms, and saving computational …