Abstract Machine learning (ML) methods has recently contributed very well in the
advancement of the prediction models used for energy consumption. Such models highly …

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Neuromorphic computing systems comprise networks of neurons that use asynchronous
events for both computation and communication. This type of representation offers several …

Event-based cameras have recently drawn the attention of the Computer Vision community
thanks to their advantages in terms of high temporal resolution, low power consumption and …

An ongoing challenge in neuromorphic computing is to devise general and computationally
efficient models of inference and learning which are compatible with the spatial and …

Objective. Computation in nervous systems operates with different computational primitives,
and on different hardware, than traditional digital computation and is thus subjected to …

Spiking neural networks (SNNs) are potentially highly efficient models for inference on fully
parallel neuromorphic hardware, but existing training methods that convert conventional …

This paper proposes a neuromorphic platform for on-FPGA online spike timing dependant
plasticity (STDP) learning, based on the COordinate Rotation DIgital Computer (CORDIC) …

Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism
for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling …

We propose a learning approach to corner detection for event-based cameras that is stable
even under fast and abrupt motions. Event-based cameras offer high temporal resolution …