Spiking neural network2024Decoding firing rates, averaged from cortical spike trains (CST), has yielded significant progress in invasive brain-machine interfaces (BMI). CSTs are theoretically more informative and efficient than firing rates. By directly decoding CST, spiking neural networks (SNN) ...
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 ...
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...
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...
In a canonical recurrent spiking neural network model, we study how these two forms of heterogeneity lead to different distributions of excitatory firing rates. To analytically characterize how these types of heterogeneities affect the network, we employ a dimension reduction method that relies on a ...
例如: - 分段线性递归神经网络(piecewise-linear recurrent neural network, PLRNN)(见 Fig. 2)可被训练来逼近生物物理放电神经元模型(biophysical spiking neuron model)(见 Fig. 3a,另见 Video)。 - 生物物理放电神经元模型 由包含指数和多项式项的微分方程 组成,而 PLRNN 仅使用分段线性函数(piecewise-linear ...
Paper tables with annotated results for A Recurrent Spiking Network with Hierarchical Intrinsic Excitability Modulation for Schema Learning
Arbor simulation of memory formation and consolidation in recurrent spiking neural networks with synaptic tagging and capture This package serves to simulate recurrent spiking neural networks, consisting of single-compartment (approximate leaky integrate-and-fire) neurons connected via current-based plastic ...
Weight quantization is used to deploy high-performance deep learning models on resource-limited hardware, enabling the use of low-precision integers for storage and computation. Spiking neural networks (SNNs) share the goal of enhancing efficiency, but adopt an 'event-driven' approach to reduce the...
Ly BMC Neuroscience 2015, 16(Suppl 1):P150 http://www.biomedcentral.com/1471-2202/16/S1/P150 POSTER PRESENTATION Open Access Interplay of intrinsic and network heterogeneity in strongly recurrent spiking networks Cheng Ly From 24th Annual Computational Neuroscience Meeting: CNS*2015 Prague, Czech ...