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 …
Direct learning-based deep spiking neural networks: a review
The spiking neural network (SNN), as a promising brain-inspired computational model with
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …
Advancing neuromorphic computing with loihi: A survey of results and outlook
Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …
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 …
Online training through time for spiking neural networks
Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models.
Recent progress in training methods has enabled successful deep SNNs on large-scale …
Recent progress in training methods has enabled successful deep SNNs on large-scale …
Diet-snn: A low-latency spiking neural network with direct input encoding and leakage and threshold optimization
Bioinspired spiking neural networks (SNNs), operating with asynchronous binary signals (or
spikes) distributed over time, can potentially lead to greater computational efficiency on …
spikes) distributed over time, can potentially lead to greater computational efficiency on …
Reducing ann-snn conversion error through residual membrane potential
Abstract Spiking Neural Networks (SNNs) have received extensive academic attention due
to the unique properties of low power consumption and high-speed computing on …
to the unique properties of low power consumption and high-speed computing on …
Optimized potential initialization for low-latency spiking neural networks
Abstract Spiking Neural Networks (SNNs) have been attached great importance due to the
distinctive properties of low power consumption, biological plausibility, and adversarial …
distinctive properties of low power consumption, biological plausibility, and adversarial …
Optimizing deeper spiking neural networks for dynamic vision sensing
Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …
Rectified linear postsynaptic potential function for backpropagation in deep spiking neural networks
Spiking neural networks (SNNs) use spatiotemporal spike patterns to represent and transmit
information, which are not only biologically realistic but also suitable for ultralow-power …
information, which are not only biologically realistic but also suitable for ultralow-power …