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 …
[HTML][HTML] 脉冲神经网络研究进展综述
胡一凡， 李国齐， 吴郁杰， 邓磊 - 2021 - kzyjc.alljournals.cn
近年来, 起源于计算神经科学的脉冲神经网络因其具有丰富的时空动力学特征, 多样的编码机制,
契合硬件的事件驱动特性等优势, 在神经形态工程和类脑计算领域已得到广泛的关注 …
契合硬件的事件驱动特性等优势, 在神经形态工程和类脑计算领域已得到广泛的关注 …
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 …
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 …
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 …
Spiking neural networks: A survey
JD Nunes, M Carvalho, D Carneiro, JS Cardoso - IEEE Access, 2022 - ieeexplore.ieee.org
The field of Deep Learning (DL) has seen a remarkable series of developments with
increasingly accurate and robust algorithms. However, the increase in performance has …
increasingly accurate and robust algorithms. However, the increase in performance has …
Diet-snn: Direct input encoding with leakage and threshold optimization in deep spiking neural networks
Bio-inspired spiking neural networks (SNNs), operating with asynchronous binary signals
(or spikes) distributed over time, can potentially lead to greater computational efficiency on …
(or spikes) distributed over time, can potentially lead to greater computational efficiency on …
Training spiking neural networks with event-driven backpropagation
Abstract Spiking Neural networks (SNNs) represent and transmit information by
spatiotemporal spike patterns, which bring two major advantages: biological plausibility and …
spatiotemporal spike patterns, which bring two major advantages: biological plausibility and …