title: A new supervised learning algorithm for spiking neurons.
abstract: The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.
authors.affiliation: Institute of Intelligence Science and Technology, Hohai University, Nanjing, Jiangsu 211100, China. xuyanhehai@163.com
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.forename: Xiaoqin
authors.initials: X
authors.lastname: Zeng
authors.forename: Shuiming
authors.initials: S
authors.lastname: Zhong
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D012691:Serial Learning / Q000502:physiology*; D013569:Synapses / Q000502:physiology