神经科学揭示生物神经元学习过程是Correlation-Based，而生物的认知行为通常是Target-Driven。对Spiking神经网络学习算法的研究，目前多数是借鉴生物correlation-based学习特性，缺少对认知行为的模拟，存在不少缺陷，很难用于解决实际工程问题。本课题在分析神经科学、认知科学最新成果基础上，利用机器学习理论方法，研究Spiking神经网络的学习算法。具体而言，是分析Spiking神经网络时间编码机制，综合考虑神经系统Correlation-Based学习特性和认知行为Target-Driven学习常规，利用符合认知行为的监督学习和强化学习策略，研究既具有高度仿生物性能、又符合认知行为规则,有广泛适用范围的以目标为向导、以目标为驱动、不依赖特定任务和特定输入输出的高效Spiking神经网络学习算法。从而使认知科学、神经科学和机器学习达到高度统一。

Spiking neuron model is widely studied in biological sciences and neuroscience. Whereas,few success have been reported to solve real world problems. One important reason for this is that the current learning algorithms for spiking neural networks are too limited. Today much evidence has been revealed in neuroscience that learning in biological neural networks is correlation-based. However cognitive behavior is often considered to be target-driven, which indicates a supervised approach to learning rather than pure correlation-based learning. Whereas some learning algorithms have been developed for spiking neural networks, most of those are still correlation-based and difficult to solve real-world problems. This project attempts to research the learning algorithms for spiking neural networks based on some recent achievements of neuroscience and cognitive science by using machine learning ideas. More specifically, we analyze the temporal-coding mechanism of spiking neural networks, study biologically plausible spiking learning algorithms with experimentally supported learning rules (Correlation-Based) and recognized cognitive behaviors(Target-Driven), and deliver a series of efficient learning algorithms for spiking neural networks that are able to learn tasks in a goal-oriented, target-driven way, and not tied to a specific task but able to learn arbitrary input-output relationships. The research can contribute to understanding the learning process of cognitive science, computational neuroscience and machine learning in a high degree.