A Forgetting Memristive Spiking Neural Network for Pavlov Experiment
A Forgetting Memristive Spiking Neural Network for Pavlov Experiment
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DOI：
10.1142/s0218127418500803
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发表时间：
2018-06
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影响因子：
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通讯作者：
Ling Chen;Chuandong Li;Yiran Chen
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文献类型：
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作者：
Ling Chen;Chuandong Li;Yiran Chen
在本文中，我们设计了一个忆阻脉冲神经网络（MSNN）执行一个功能齐全的巴甫洛夫实验。采用具有遗忘效应的忆阻器实现突触，而Izhikevich神经元用于产生紧张性尖峰和紧张性爆发信号。考虑到突触前神经元之间的激活时间差，自然形成了非对称线性尖峰时间依赖可塑性（STDP）。我们的设计实现了关联，纠正和遗忘过程中没有学习规则控制模块。此外，如果两个神经元的激活时间足够接近，则联合会增强，否则，所有条件反射联合都会减弱。
In this paper, we designed a memristive spiking neural network (MSNN) to perform a fully functional Pavlov experiment. A memristor with forgetting effect is adopted to implement synapses while Izhikevich neurons are used for generating tonic spiking and tonic bursting signals. An asymmetric linear spiking time dependent plasticity (STDP) is naturally formed by taking into account the activation time difference between pre-synaptic neurons. Our design realizes associative, correcting, and forgetting processes without learning rule control modules. Moreover, an association will be enhanced if the activation time of two neurons is close enough, otherwise, all conditioned reflex associations will be weakened.