spiking neural networkIn recent years, spiking neural networks (SNNs), which originated from the theoretical basis of neuroscience, have attracted neuromorphic computing and brain‐like computing due to their advantages, such as neural dynamics and coding mechanism, which are similar to biological ...
中文翻译版:Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks - 穷酸秀才大草包 - 博客园 (cnblogs.com) 针对sequential and streaming tasks,本文提出了一种自适应脉冲递归神经网络(SRNN),把标准的自适应多时间尺度的脉冲神经元建模为self-recurrent的神经单元,利用代理梯度...
CNN-SRNN代码可在https://github.com/byin-cwi/Efficient-spiking-networks/tree/main/DVS128),深度预处理显著提高了诸如GSC和DVS128数据集36等任务的准确性,其中SRNN的分数超过了参考文献33,35报告的分数。
In recent years, spiking neural networks (SNNs), which originated from the theoretical basis of neuroscience, have attracted neuromorphic computing and brain‐like computing due to their advantages, such as neural dynamics and coding mechanism, which are similar to biological neurons. SNNs have become...
We propose a Double EXponential Adaptive Threshold (DEXAT) neuron model that improves the performance of neuromorphic Recurrent Spiking Neural Networks (RSNNs) by providing faster convergence, higher accuracy and a flexible long short-term memory. We present a hardware efficient methodology to realize ...
A recurrent spiking neural network is proposed that implements planning as probabilistic inference for finite and infinite horizon tasks. The architecture splits this problem into two parts: The stochastic transient firing of the network embodies the dynamics of the planning task. With appropriate inject...
Supplementary materials for: A solution to the learning dilemma for recurrent networks of spiking neurons Supplementary Figures Supplementary Tables Supplementary Notes 1 Eligibility traces Results中的“Mathematical basis for e-prop”一节中介绍了资格迹。在这里,我们提供有关资格迹的更多信息。首先,我们讨论作为...
Neuromorphic Recurrent Spiking Neural Networks for EMG Gesture Classification and Low Power Implementation on Loihi In this work, we show an efficient Electromyograph (EMG) gesture recognition using Double Exponential Adaptive Threshold (DEXAT) neuron based Recurrent Spiking Neural Network (RSNN). Our...
A mathematical approach to unsupervised learning in recurrent neural networks In this thesis, we propose to give a mathematical sense to the claim: the neocortex builds itself a model of its environment. We study the neocortex as a network of spiking neurons undergoing slow STDP learning. By ...
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 through synaptic plasticity to carry out complex network computationsremains unclear. We argue that two pieces of this puzzle ...