Pairwise analysis can account for network structures arising from spike-timing dependent plasticity.
Pairwise analysis can account for network structures arising from spike-timing dependent plasticity.
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DOI：
10.1371/journal.pcbi.1002906
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发表时间：
2013
影响因子：
4.3
通讯作者：
Abbott LF
中科院分区：
文献类型：
--
作者：
Babadi B;Abbott LF
脉冲时序依赖可塑性(STDP)根据每个突触局部可用的时序信息修改突触强度。尽管如此，它还是在一个递归连接的网络中引发了全球结构。我们通过模拟和分析STDP对神经元成对相互作用的影响来研究这种结构。我们展示了传统的STDP如何作为一种环路消除机制，并将神经元组织成内中枢和外中枢。当抑郁占主导地位时，环路消除会增加，而当增强占主导地位时，环路消除会变成环路生成。STDP具有移位的时间窗口，使得重合的尖峰导致抑郁，增强了循环连接，并作为一种严格的缓冲机制来维持大致恒定的平均放电率。具有相反时间移位的STDP在低速率时用作环路消除器，而在较高速率时用作有效的环路生成器。总的来说，研究神经元的成对相互作用提供了关于STDP可以在大型网络中产生的结构的重要见解。神经网络中的连接结构至少部分反映了构成学习和记忆基础的突触可塑性机制的长期影响。在最广泛的这种机制之一，峰时依赖可塑性(STDP)，突触前和突触后尖峰通过突触的时间顺序决定了它是加强还是减弱。因此，突触仅根据局部信息通过STDP进行修改。然而，STDP可以在一个相互连接的神经网络中产生各种全局连通结构。在这里，我们提供了一个分析框架，可以预测在这样的网络中由STDP产生的全局结构。我们开发的分析技术实际上非常简单，涉及到研究两个相互连接的神经元从其周围网络接收输入。在对各种不同的STDP模型进行分析计算之后，我们通过全网络模拟来测试和验证我们的所有预测。更重要的是，开发的分析工具将允许其他研究人员找出网络中任何其他类型的STDP产生的原因。
Spike timing-dependent plasticity (STDP) modifies synaptic strengths based on timing information available locally at each synapse. Despite this, it induces global structures within a recurrently connected network. We study such structures both through simulations and by analyzing the effects of STDP on pair-wise interactions of neurons. We show how conventional STDP acts as a loop-eliminating mechanism and organizes neurons into in- and out-hubs. Loop-elimination increases when depression dominates and turns into loop-generation when potentiation dominates. STDP with a shifted temporal window such that coincident spikes cause depression enhances recurrent connections and functions as a strict buffering mechanism that maintains a roughly constant average firing rate. STDP with the opposite temporal shift functions as a loop eliminator at low rates and as a potent loop generator at higher rates. In general, studying pairwise interactions of neurons provides important insights about the structures that STDP can produce in large networks. The connectivity structure in neural networks reflects, at least in part, the long-term effects of synaptic plasticity mechanisms that underlie learning and memory. In one of the most widespread such mechanisms, spike-timing dependent plasticity (STDP), the temporal order of pre- and postsynaptic spiking across a synapse determines whether it is strengthened or weakened. Therefore, the synapses are modified solely based on local information through STDP. However, STDP can give rise to a variety of global connectivity structures in an interconnected neural network. Here, we provide an analytical framework that can predict the global structures that arise from STDP in such a network. The analytical technique we develop is actually quite simple, and involves the study of two interconnected neurons receiving inputs from their surrounding network. Following analytical calculations for a variety of different STDP models, we test and verify all our predictions through full network simulations. More importantly, the developed analytical tool will allow other researchers to figure out what arises from any other type of STDP in a network.
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