The spiking neural network (SNN) computes and communicates information through discrete binary events. Recent work has achieved essential progress on an excellent performance by converting ANN to SNN.

Section 4 verifies the convergence, the advantage of M-SNN in simulating cognitive phenomena, and the computational cost. Finally, a short conclusion is given in Section 5. For the architecture, the ...

Spiking Neural Networks (SNNs), a cutting-edge paradigm in brain-inspired ... particularly in energy efficiency and performance. However, current SNN-based VAD models still struggle to achieve ...

Abstract: The spiking neural network (SNN) is the third generation of neural networks, valued for its biological interpretability, hardware compatibility, and low power consumption. While field ...

This is the code related to the paper titled "Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation" published in ICLR, 2020 ...

This repository contains the Pytorch-based evaluation codes for [SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks ... the code for ...

Humans and certain animals appear to have an innate capacity to learn relationships between different objects or events in ...

Many conventional computer architectures are ill-equipped to meet the computational demands of machine learning-based models. In recent years, some engineers have thus been trying to design ...

One of the most agonizing experiences a cancer patient suffers is waiting without knowing: waiting for a diagnosis, waiting ...

Neural networks have revolutionized the fields of artificial intelligence (AI) and machine learning by providing a flexible, and scalable, means to solve complex, and traditionall ...

Prophesee’s camera provides a data stream, which includes a pixel location and color values when a change is detected, that’s ...