Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.
Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses.
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DOI：
10.1371/journal.pcbi.1004458
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发表时间：
2015-08
影响因子：
4.3
通讯作者：
Doiron B
中科院分区：
文献类型：
--
作者：
Ocker GK;Litwin-Kumar A;Doiron B
与简单的随机网络模型相比，皮层网络的突触连接具有某些布线图案的过度表达。这种结构的形成，部分是由突触可塑性，促进或抑制神经元之间的连接，这取决于他们的联合尖峰活动。通常，理论研究集中在前馈输入如何驱动可塑性以创建这种网络结构。我们研究了循环网络中自组织结构的互补性情景，其中尖峰时间依赖的可塑性由自发动力学驱动。我们开发了一个自洽的理论，结合快速尖峰协方差与突触权重的缓慢演变的网络结构的演变。通过网络动力学的有限尺寸扩展，我们得到一组低维的非线性微分方程的两个突触连接图案的演变。有了这个理论在手，我们探讨了可塑性规则的形式如何驱动皮层网络中微电路的演变。当增强和抑制处于近似平衡时，突触动力学依赖于加权发散、收敛和链基序。对于添加剂，赫布STDP这些基序相互作用在突触动力学中产生不稳定性，促进或抑制初始网络结构。我们的工作提供了一个一致的理论框架，研究如何在经常性网络尖峰活动与突触可塑性相互作用，以确定网络结构。哺乳动物大脑的连通性表现出各种空间尺度的结构，从宽的（大脑区域连接到哪个）到极细的（突触在单个神经元的形态上形成）。最近在新皮层的实验工作强调了微电路水平上的结构：小群体神经元之间的不同连接模式要么比偶然预期的更丰富，要么更少。系统神经科学的一个核心问题是这种结构是如何出现的。试图回答这个问题是混淆了网络结构和尖峰活动的相互作用。突触连接影响尖峰统计数据，而单个突触具有高度可塑性，并且根据突触前和突触后神经元的活动而变得更强或更弱。我们提出了一个自洽的理论如何活动依赖性突触可塑性导致神经元微电路的出现。我们用这个理论来说明如何的可塑性规则的形式可以管理不同的连接模式的促进或抑制。我们的工作为理解皮层回路（而不仅仅是单个突触）如何对网络的外部和内部输入做出反应提供了基础。
The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure. The connectivity of mammalian brains exhibits structure at a wide variety of spatial scales, from the broad (which brain areas connect to which) to the extremely fine (where synapses form on the morphology of individual neurons). Recent experimental work in the neocortex has highlighted structure at the level of microcircuits: different patterns of connectivity between small groups of neurons are either more or less abundant than would be expected by chance. A central question in systems neuroscience is how this structure emerges. Attempts to answer this question are confounded by the mutual interaction of network structure and spiking activity. Synaptic connections influence spiking statistics, while individual synapses are highly plastic and become stronger or weaker depending on the activity of the pre- and postsynaptic neurons. We present a self-consistent theory for how activity-dependent synaptic plasticity leads to the emergence of neuronal microcircuits. We use this theory to show how the form of the plasticity rule can govern the promotion or suppression of different connectivity patterns. Our work provides a foundation for understanding how cortical circuits, and not just individual synapses, are malleable in response to inputs both external and internal to a network.