神经网络模型中一种新的随机STDP规则
A new stochastic STDP Rule in a neural Network Model
摘要：
突触可塑性被认为与记忆有关，在过去的几十年里得到了广泛的研究。可塑性模型的一个例子是流行的尖峰时间依赖可塑性（STDP）。大量的STDP模型文献主要基于确定性规则，而涉及的生物机制主要是随机性的。此外，只有很少的数学研究考虑到精确的尖峰时间的可塑性。在这篇文章中，我们的目标是提出一个新的随机STDP规则与离散的突触权重，允许一个完整的网络动态的数学分析的假设下，分离的时间尺度。这个模型试图回答需要了解的权重动态和神经元之间的相互作用。
英文摘要：
Thought to be responsible for memory, synaptic plasticity has been widely studied in the past few decades. One example of plasticity models is the popular Spike Timing Dependent Plasticity (STDP). The huge litterature of STDP models are mainly based deterministic rules whereas the biological mechanisms involved are mainly stochastic ones. Moreover, there exist only few mathematical studies on plasticity taking into account the precise spikes timings. In this article, we aim at proposing a new stochastic STDP rule with discrete synaptic weights which allows a mathematical analysis of the full network dynamics under the hypothesis of separation of timescales. This model attempts to answer the need for understanding the interplay between the weights dynamics and the neurons ones.
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