Bindsnet: A machine learning-oriented spiking neural networks library in python
The development of spiking neural network simulation software is a critical component
enabling the modeling of neural systems and the development of biologically inspired …
enabling the modeling of neural systems and the development of biologically inspired …
Going deeper with directly-trained larger spiking neural networks
Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal
information and event-driven signal processing, which is very suited for energy-efficient …
information and event-driven signal processing, which is very suited for energy-efficient …
Ternary spike: Learning ternary spikes for spiking neural networks
Y Guo, Y Chen, X Liu, W Peng, Y Zhang… - Proceedings of the …, 2024 - ojs.aaai.org
The Spiking Neural Network (SNN), as one of the biologically inspired neural network
infrastructures, has drawn increasing attention recently. It adopts binary spike activations to …
infrastructures, has drawn increasing attention recently. It adopts binary spike activations to …
Backpropagation-based learning techniques for deep spiking neural networks: A survey
M Dampfhoffer, T Mesquida… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
With the adoption of smart systems, artificial neural networks (ANNs) have become
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …
Training spiking neural networks using lessons from deep learning
The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
networks. The inner workings of our synapses and neurons provide a glimpse at what the …
Differentiable spike: Rethinking gradient-descent for training spiking neural networks
Abstract Spiking Neural Networks (SNNs) have emerged as a biology-inspired method
mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy …
mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy …
Autosnn: Towards energy-efficient spiking neural networks
Spiking neural networks (SNNs) that mimic information transmission in the brain can energy-
efficiently process spatio-temporal information through discrete and sparse spikes, thereby …
efficiently process spatio-temporal information through discrete and sparse spikes, thereby …
A review of learning in biologically plausible spiking neural networks
Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …
Spiking neural networks and their applications: A review
The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …
domains. However, deep neural networks are very resource-intensive in terms of energy …
Learning to be efficient: Algorithms for training low-latency, low-compute deep spiking neural networks
Recent advances have allowed Deep Spiking Neural Networks (SNNs) to perform at the
same accuracy levels as Artificial Neural Networks (ANNs), but have also highlighted a …
same accuracy levels as Artificial Neural Networks (ANNs), but have also highlighted a …