具有用深度构建的分区域工作机制和具有时间特性的Spiking发放机制是生物大脑系统中常见的信息表示和处理方式。目前对Deep Learning的研究，多数是借鉴于生物视觉认知系统的分区机制，而缺少对神经元信息表示方法的模仿，不能充分的表示时间特征，从而导致很难用以解决具有高度时序特性的数据问题。本课题在分析神经科学、机器学习最新研究成果的基础上，研究具有高度时序表征能力的Deep Learning模型。具体而言，是在现有Deep Learning分层模型的基础上，综合考虑神经系统的Spiking信息表示方式、神经元的Spiking时间编码机制、生物认知的STDP学习规则，研究探索既能高度模仿大脑深度层次结构、又具有良好的时间特征表示能力的时空结合的Deep Learning新模型和算法。从而为Deep Learning在高维时序大数据处理问题中的应用提供理论保障。

Deep Learning models are widely studied in machine learning and have achieved a lot of success. However, these models have few advantages to solve the real-world time-series-dependent problems since they still employee the information representation mechanism in traditional neural networks. Researches on hippocampus electrophysiology experiments reveal that biological neurons transmit information by the spike emitting mechanism, which motivates the temporal coding approach to learning rather than the pure deep architecture. Recently, models and learning methods developed for deep learning still ignore the spike emitting mechanism and are not adept in solving the real-world time-series-dependent problems. This project attempts to make up this defect of deep learning models basing on some recent achievements on neuroscience and spiking neural networks. More specifically, we analyze the latest deep learning models and the temporal-coding mechanism of spiking neural networks to study biologically plausible deep learning models. With experimentally supported learning rules and spike emitting mechanism of spiking neural networks, we explore a new deep learning model with efficient training algorithms, which can solve time-series-dependent problems in a biological way, and represent both the spatial and temporal features of time-series data. This research can be applied to more complex engineering problems and contribute to understanding the learning process of cognitive science, computational neuroscience and machine learning in a high degree.