Ponulak Filip, Kasinski Andrzej
Institute of Control and Information Engineering, Poznan University of Technology, Poznan, Poland.
Acta Neurobiol Exp (Wars). 2011;71(4):409-33. doi: 10.55782/ane-2011-1862.
The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.
多年来，神经信息由神经元放电率编码的概念一直是神经生物学的主导范式。这种范式也被人工神经网络理论所采用。然而，最近的生理学实验表明，在神经系统的许多部位，神经编码基于单个动作电位的时间。这一发现催生了一类新的神经模型，即脉冲神经网络。在本文中，我们总结了脉冲神经元和脉冲网络的基本特性。具体而言，我们关注基于脉冲的信息编码、突触可塑性和学习的模型。我们还综述了脉冲模型的实际应用。本文旨在为对基于脉冲的神经处理感兴趣的各学科科学家介绍脉冲神经网络。
