The training of multilayer spiking neural networks (SNNs) using the error backpropagation
algorithm has made significant progress in recent years. Among the various training …

Modelling functionalities of the brain in human-robot interaction contexts requires a real-time
understanding of how each part of a robot (motors, sensors, emotions, etc.) works and how …

Spiking neural networks are becoming increasingly popular for their low energy requirement
in real-world tasks with accuracy comparable to traditional ANNs. SNN training algorithms …

Abstract Spiking Neural Networks (SNNs) are more biologically plausible and
computationally efficient. Therefore, SNNs have the natural advantage of drawing the sparse …

Abstract Recurrent Spiking Neural Networks (RSNNs) are bio-plausible computational
models to detect temporal patterns in data and mimic nonlinear dynamical systems. Due to …

Fast and accurate online processing is essential for smooth prosthetic hand control with
Surface Electromyography signals (sEMG). Although transformers are state-of-the-art deep …

In this work, we present a neuromorphic processor for artificial intelligence of things (AIoT)
applications featuring low-power consumption, a small footprint, STDP-based online …

This paper introduces Elegans-AI models, a class of neural networks that leverage the
connectome topology of the Caenorhabditis elegans to design deep and reservoir …

In this work, Au loading and micro-morphology regulation were used to synergistically
enhance the gas sensing properties of LaFeO 3-based materials. 2 wt% Au: LaFeO 3 …

We propose a novel backpropagation algorithm for training spiking neural networks (SNNs)
that allows each neuron to fire multiple times, unlike conventional methods where each …