Inspired by the sparse and hierarchical features representation in the ventral stream of the
human visual system, the biologically inspired multi-scale contourlet attention network …

Biological spiking neural networks (SNNs) can temporally encode information in their
outputs, eg in the rank order in which neurons fire, whereas artificial neural networks (ANNs) …

We propose a new learning algorithm to train spiking neural networks (SNN) using
conventional artificial neural networks (ANN) as proxy. We couple two SNN and ANN …

Spiking neural networks (SNNs) are well-suited for neuromorphic hardware due to their
biological plausibility and energy efficiency. These networks utilize sparse, asynchronous …

Spiking neural networks (SNNs), particularly the single-spike variant in which neurons spike
at most once, are considerably more energy efficient than standard artificial neural networks …

Introduction Hyperdimensional Computing (HDC) is a brain-inspired and lightweight
machine learning method. It has received significant attention in the literature as a candidate …

For mathematical completeness, we propose an error-backpropagation algorithm based on
latency code (BPLC) with spiking neurons conforming to the spike–response model but …

Direct training of Spiking Neural Networks (SNNs) on neuromorphic hardware has the
potential to significantly reduce the high energy consumption of Artificial Neural Networks …

Compared to traditional convolutional neural networks (CNNs), the Capsule network
(CapsNet), due to its capsule-based design that aligns better with the principle of human …

Backpropagation is the most popular and common algorithm for training of traditional deep
neural networks. Herewe propose temporal version of backpropagation to directly train …