Networks of Spiking Neurons: The Third Generation of Neural Network Models

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Networks of Spiking Neurons: The Third Generation of Neural Network Models

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! 顺便安利一下同组的大佬做的SNN教程:https://spikingflow.readthedocs.io/zh_CN/latest/Tutorials.html Abstract 本文在计算能力上对脉冲神经网络模型与基于McCulloch-Pitts神经元(阈值门)和基于sigmoidal门的其他神经网络模型加以比较.特别是,研究表明,就所需神经元数量而言,脉冲神经网络的计算能力比其他神经网络模型更强.这显示了一个具体的生物学相关函…

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郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Abstract 动物会重复奖励的行为,但基于奖励的学习的生理基础仅得到了部分阐明.一方面,实验证据表明神经调节剂多巴胺携带有关奖励的信息并影响突触可塑性.另一方面,强化学习理论为基于奖励的学习提供了框架.奖励调节的脉冲时序依赖可塑性(R-STDP)的最新模型已迈出了弥合两种方法之间差距的第一步,但仍面临两个问题.首先,强化学习通常是在不适合自然情况描述的离散框架中制定的.其次,生物学合理的R-STDP模型需要精确计算奖励预测误差,但…