ABSTRACT Video surveillance systems are omnipresent and automatic monitoring of human activities is gaining importance in highly secured environments. The proposed work explores the use of the bio-inspired third generation neural network called spiking neural network (SNN) in order to recognize the action sequences present in a video. The SNN used in this work carries the neural information in terms of timing of spikes rather than the shape of the spikes. The learning technique used herein is reward-modulated spike time-dependent plasticity (R-STDP). It is based on reinforcement learning that modulates or demodulates the synaptic weights depending on the reward or the punishment signal that it receives from the decision layer. The absence of gradient descent techniques and external classifiers makes the system computationally efficient and simple. Finally, the performance of the network is evaluated on the two benchmark datasets, viz., Weizmann and KTH datasets.

中文翻译：

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基于 R-STDP 的脉冲神经网络用于人类行为识别

摘要 视频监控系统无处不在，人类活动的自动监控在高度安全的环境中变得越来越重要。拟议的工作探索了使用称为尖峰神经网络 (SNN) 的仿生第三代神经网络来识别视频中存在的动作序列。在这项工作中使用的 SNN 携带关于尖峰时间而不是尖峰形状的神经信息。这里使用的学习技术是奖励调制的尖峰时间依赖可塑性 (R-STDP)。它基于强化学习，根据从决策层接收到的奖励或惩罚信号来调制或解调突触权重。梯度下降技术和外部分类器的缺失使系统计算效率高且简单。