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Dynamic Stochastic Synapses as Computational Units

机译:动态随机突触作为计算单位

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摘要

In most neural network models, synapses are treated as static weights that change only with the slow time scales of learning. It is well known, however, that synapses are highly dynamic and show use-dependent plasticity over a wide range of time scales. Moreover, synaptic transmission is an inherently stochastic process: a spike arriving at a presynaptic terminal triggers the release of a vesicle of neurotransmitter from a release site with a probability that can be much less than one. We consider a simple model for dynamic stochastic synapses that can easily be integrated into common models for networks of integrate-andfire neurons (spiking neurons). The parameters of this model have direct interpretations in terms of synaptic physiology. We investigate the consequences of the model for computing with individual spikes and demonstrate through rigorous theoretical results that the computational power of the network is increased through the use of dynamic synapses.
机译:在大多数神经网络模型中,突触被视为静态权重,仅随着学习时间的缓慢而变化。但是,众所周知,突触是高度动态的,并且在很宽的时间范围内都显示出依赖于使用的可塑性。此外,突触传递是一种固有的随机过程:到达突触前末端的尖峰触发神经递质囊泡从释放部位释放,其发生概率可能远小于一个。我们考虑了一个动态随机突触的简单模型,该模型可以轻松地集成到整合和触发神经元网络(加标神经元)的通用模型中。该模型的参数在突触生理学方面具有直接的解释。我们调查了该模型用于计算单个尖峰的结果,并通过严格的理论结果证明了网络的计算能力通过使用动态突触而增加。

著录项

  • 来源
    《Neural computation》 |1999年第4期|903-917|共15页
  • 作者

    Maass W; Zador A;

  • 作者单位

    Institute for Theoretical Computer Science, Technische Universität Graz, A-8010 Graz, Austria;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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