Direct training high-performance deep spiking neural networks: a review of theories and methods
Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …
Glif: A unified gated leaky integrate-and-fire neuron for spiking neural networks
X Yao, F Li, Z Mo, J Cheng - Advances in Neural …, 2022 - proceedings.neurips.cc
Abstract Spiking Neural Networks (SNNs) have been studied over decades to incorporate
their biological plausibility and leverage their promising energy efficiency. Throughout …
their biological plausibility and leverage their promising energy efficiency. Throughout …
Moiré synaptic transistor with room-temperature neuromorphic functionality
Moiré quantum materials host exotic electronic phenomena through enhanced internal
Coulomb interactions in twisted two-dimensional heterostructures,,–. When combined with …
Coulomb interactions in twisted two-dimensional heterostructures,,–. When combined with …
Acoustic-based machine condition monitoring—methods and challenges
The traditional means of monitoring the health of industrial systems involves the use of
vibration and performance monitoring techniques amongst others. In these approaches …
vibration and performance monitoring techniques amongst others. In these approaches …
Brain-inspired neural circuit evolution for spiking neural networks
In biological neural systems, different neurons are capable of self-organizing to form
different neural circuits for achieving a variety of cognitive functions. However, the current …
different neural circuits for achieving a variety of cognitive functions. However, the current …
Optoelectronic Devices for In‐Sensor Computing
The demand for accurate perception of the physical world leads to a dramatic increase in
sensory nodes. However, the transmission of massive and unstructured sensory data from …
sensory nodes. However, the transmission of massive and unstructured sensory data from …
Sparse-firing regularization methods for spiking neural networks with time-to-first-spike coding
The training of multilayer spiking neural networks (SNNs) using the error backpropagation
algorithm has made significant progress in recent years. Among the various training …
algorithm has made significant progress in recent years. Among the various training …
Rate coding or direct coding: Which one is better for accurate, robust, and energy-efficient spiking neural networks?
Recent Spiking Neural Networks (SNNs) works focus on an image classification task,
therefore various coding techniques have been proposed to convert an image into temporal …
therefore various coding techniques have been proposed to convert an image into temporal …
DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays
An increasing number of studies are highlighting the importance of spatial dendritic
branching in pyramidal neurons in the neocortex for supporting non-linear computation …
branching in pyramidal neurons in the neocortex for supporting non-linear computation …
Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task
Spiking Neural Networks (SNNs), known for their potential to enable low energy
consumption and computational cost, can bring significant advantages to the realm of …
consumption and computational cost, can bring significant advantages to the realm of …