Deep learning in spiking neural networks
A Tavanaei, M Ghodrati, SR Kheradpisheh… - Neural networks, 2019 - Elsevier
In recent years, deep learning has revolutionized the field of machine learning, for computer
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is …
Artificial neural networks for neuroscientists: a primer
Artificial neural networks (ANNs) are essential tools in machine learning that have drawn
increasing attention in neuroscience. Besides offering powerful techniques for data analysis …
increasing attention in neuroscience. Besides offering powerful techniques for data analysis …
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 …
Deep residual learning in spiking neural networks
Abstract Deep Spiking Neural Networks (SNNs) present optimization difficulties for gradient-
based approaches due to discrete binary activation and complex spatial-temporal dynamics …
based approaches due to discrete binary activation and complex spatial-temporal dynamics …
Training high-performance low-latency spiking neural networks by differentiation on spike representation
Abstract Spiking Neural Network (SNN) is a promising energy-efficient AI model when
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …
Temporal effective batch normalization in spiking neural networks
Abstract Spiking Neural Networks (SNNs) are promising in neuromorphic hardware owing to
utilizing spatio-temporal information and sparse event-driven signal processing. However, it …
utilizing spatio-temporal information and sparse event-driven signal processing. However, it …
Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks
Spiking neural networks (SNNs) are nature's versatile solution to fault-tolerant, energy-
efficient signal processing. To translate these benefits into hardware, a growing number of …
efficient signal processing. To translate these benefits into hardware, a growing number of …
A solution to the learning dilemma for recurrent networks of spiking neurons
Recurrently connected networks of spiking neurons underlie the astounding information
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …
processing capabilities of the brain. Yet in spite of extensive research, how they can learn …
Recdis-snn: Rectifying membrane potential distribution for directly training spiking neural networks
Y Guo, X Tong, Y Chen, L Zhang… - Proceedings of the …, 2022 - openaccess.thecvf.com
The brain-inspired and event-driven Spiking Neural Network (SNN) aims at mimicking the
synaptic activity of biological neurons, which transmits binary spike signals between network …
synaptic activity of biological neurons, which transmits binary spike signals between network …
The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks
Brains process information in spiking neural networks. Their intricate connections shape the
diverse functions these networks perform. Yet how network connectivity relates to function is …
diverse functions these networks perform. Yet how network connectivity relates to function is …