Despite a well-established mathematical foundation for neural dynamics, the implementation details vary greatly across different platforms.
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Currently available simulators are catered to either neuroscience workflows (such as NEST and Brian2) or deep learning workflows (such as BindsNET).
The key idea is to inject the input via physical parameters that affect the scattering processes.
Optics Emerging Technologies Data Analysis, Statistics and Probability
Today's heavy machine learning tasks are fueled by large datasets.
While there is still a lot to learn about astrocytes and their neuromodulatory role in the spatial and temporal integration of neuronal activity, their introduction to neuromorphic hardware is timely, facilitating their computational exploration in basic science questions as well as their exploitation in real-world applications.
In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed ``thermal neuristors."
Disordered Systems and Neural Networks Materials Science Adaptation and Self-Organizing Systems
We show that conventional artificial neural networks (ANN) such as multilayer perceptron, convolutional neural networks, as well as the latest residual neural networks can be mapped successfully onto Shenjing, realizing ANNs with SNN's energy efficiency.
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties.
The leading approaches in language modeling are all obsessed with TV shows of my youth - namely Transformers and Sesame Street.
Ranked #27 on Language Modelling on enwik8