Spiking Neuron Models - single neurons, populations, plasticity _ W.Gerstner,_W.M.Kistler 计算神经科学专著之一，学习spiking neuron 的好书。作者GERSTNER是个牛人。

通过混合转换和依赖于峰值时序的反向传播来启用深度峰值神经网络 这是与在发表的论文“使用混合转换和峰值定时依赖的反向传播实现深度尖峰神经网络”相关的代码。 培训方法 培训分以下两个步骤进行： 训练ANN（'ann.py'） 将ANN转换为SNN并执行基于尖峰的反向传播（'snn.py'） 档案文件 'ann.py'：训练一个ANN，可以提供输入参数来提供建筑设计，数据集，训练设置 'snn.py'：从头开始训练SNN或执行ANN-SNN转换（如果有预训练的ANN可用）。 / self_models：包含ANN和SNN的模型文件 'ann_script.py'和'snn_script.py'：这些脚本可用于设计各种实验，它创建可用于运行多个模型的'script.sh' 训练有素的人工神经网络模型 训练有素的SNN模型 问题 有时，“ STDB”的激活在训练过程中会变得不稳定，从而

针对光伏发电功率预测精度不高的问题，提出一种基于相似日和云自适应粒子群优化(CAPSO)算法优化Spiking神经网络(SNN)的发电功率预测模型。考虑到季节类型、天气类型和气象等主要影响因素，提出以综合相似度指标进行相似日选取；以SNN强大的计算能力和其善于处理时间序列问题的特点为基础，结合CAPSO算法搜索的随机性和稳定性优化SNN的多突触连接权值，减少对权值的约束，提高算法的收敛精度。根据某光伏电站的实测功率数据对所提模型进行测试和评估，结果表明，该模型比传统预测模型具有更高的预测精度和更好的适用性。

这个是网页模式的，每一章都有。 Gerstner and Kistler Spiking Neuron Models. Single Neurons, Populations, Plasticity Cambridge University Press, 2002

Spiking neuron models single neurons_ populations_plasticity

Abstract— With the increasing popularity of social media and smart devices, the face as one of the key biometrics becomes vital for person identification. Among those face recognition algorithms, video-based face recognition methods could make use of both temporal and spatial information just as hum

Spiking Neuron Models:Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this introductory text aimed at those taking courses in computational neuroscience, theoretical biology, neuronal modeling, biophysics, or neural networks. The authors focus on phenomenological approaches so that beginners can get to grips with the theoretical concepts before confronting the wealth of detail in biological systems. The book is in three Parts dealing, in order, with neurons and connections, collective behavior in networks, and synaptic plasticity and its role in learning, memory, and development. Each chapter ends with a literature survey, and a comprehensive bibliography is included. As such the book will also introduce readers to current research.

第三代神经网络——脉冲神经网络，更加接近于生物神经元。

基于粒子群算法优化脉冲神经网络，更进一步优化现有的脉冲网络。

Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence，非常有用的资料