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Generalized Stochastic Spiking Neuron Model and Extended Spike Response Model in Spatial-Temporal Pulse Pattern Detection Task

机译:时空脉冲模式检测任务中的广义随机尖峰神经元模型和扩展尖峰响应模型

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摘要

A generalized model of spiking neuron is proposed as a non-stationary stochastic spike sequences processing unit. The generalized spiking neuron model is described with a conditional spike generation probability distribution and with a state evolution operator. An information theory language is used for the convenient neuron's learning tasks description. The problem of spiking neuron learning with the teacher is solved using information entropy minimization algorithm. A particular implementation of generalized model using stochastic Spike Response Model with alpha-functions set is provided. A task of time delay maintenance between input and output spikes and a task of detecting of a spiking pattern in a noisy stream of pulse signals are considered using extended SRM neuron. It is shown that after the using of the proposed learning method spiking neuron became capable to detect a spatial-temporal pulse pattern and to serve as an adaptive delay unit.
机译:提出了尖峰神经元的广义模型,作为非平稳随机尖峰序列处理单元。用条件尖峰生成概率分布和状态演化算子描述了广义尖峰神经元模型。信息论语言用于方便神经元的学习任务描述。使用信息熵最小化算法解决了与老师的神经元学习突增的问题。提供了使用带有Alpha函数集的随机峰值响应模型的广义模型的特定实现。使用扩展的SRM神经元考虑了输入和输出尖峰之间的时间延迟维护任务以及在脉冲信号的噪声流中检测尖峰模式的任务。结果表明,在使用所提出的学习方法之后,尖峰神经元变得能够检测时空脉冲模式并用作自适应延迟单元。

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