Ghosh-Dastidar Samanwoy, Adeli Hojjat
Department of Biomedical Engineering, The Ohio State University, OH 43210, USA.
Int J Neural Syst. 2009 Aug;19(4):295-308. doi: 10.1142/S0129065709002002.
Most current Artificial Neural Network (ANN) models are based on highly simplified brain dynamics. They have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. ANNs have been evolving towards more powerful and more biologically realistic models. In the past decade, Spiking Neural Networks (SNNs) have been developed which comprise of spiking neurons. Information transfer in these neurons mimics the information transfer in biological neurons, i.e., via the precise timing of spikes or a sequence of spikes. To facilitate learning in such networks, new learning algorithms based on varying degrees of biological plausibility have also been developed recently. Addition of the temporal dimension for information encoding in SNNs yields new insight into the dynamics of the human brain and could result in compact representations of large neural networks. As such, SNNs have great potential for solving complicated time-dependent pattern recognition problems because of their inherent dynamic representation. This article presents a state-of-the-art review of the development of spiking neurons and SNNs, and provides insight into their evolution as the third generation neural networks.
当前大多数人工神经网络（ANN）模型都基于高度简化的大脑动力学。它们已被用作强大的计算工具来解决复杂的模式识别、函数估计和分类问题。人工神经网络一直在朝着更强大、更符合生物学现实的模型发展。在过去十年中，已经开发出了由脉冲神经元组成的脉冲神经网络（SNN）。这些神经元中的信息传递模仿了生物神经元中的信息传递，即通过脉冲的精确时间或一系列脉冲。为了便于在这样的网络中学习，最近还开发了基于不同程度生物学合理性的新学习算法。在脉冲神经网络中增加用于信息编码的时间维度，为人类大脑的动力学带来了新的见解，并可能导致大型神经网络的紧凑表示。因此，脉冲神经网络由于其固有的动态表示，在解决复杂的时间相关模式识别问题方面具有巨大潜力。本文对脉冲神经元和脉冲神经网络的发展进行了最新综述，并深入探讨了它们作为第三代神经网络的演变。
