Activity dynamics and propagation of synchronous spiking in locally connected random networks
Activity dynamics and propagation of synchronous spiking in locally connected random networks
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DOI：
10.1007/s00422-002-0384-4
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发表时间：
2003-05-01
影响因子：
1.9
通讯作者：
Aertsen, A
中科院分区：
文献类型：
--
作者：
Mehring, C;Hehl, U;Aertsen, A
随机网络模型已经成为研究皮质网络动力学的一种流行工具。在大约一立方毫米的皮质上，包含大约10万个神经元，皮质解剖学显示了一个更现实的结构。在这种局部连接的随机网络中，连接概率随神经元之间的距离呈高斯分布减小。在这里，我们提出了三个主要的结果，从模拟研究的活动动态在这样的网络。首先，对于广泛的参数范围，这些动态表现出异步网络活动的静止状态，具有不规则的单神经元尖峰。这种状态可以用作正在进行的网络活动的现实模型。描述了该状态的参数依赖性和其他状态下网络动力学的性质。其次，对部分神经元的同步兴奋性刺激导致强烈的活动反应，很容易支配网络动力学。第三，由于这种活动响应，发散收敛前馈子网（如在同步链中）的嵌入不会自然地导致子网中同步活动的稳定传播；这与我们之前在这种类型的孤立子网中的发现形成对比。讨论了通过特定的学习规则或子网络的一般化来稳定同步尖峰齐射和网络动态的相互作用的可能机制。
Random network models have been a popular tool for investigating cortical network dynamics. On the scale of roughly a cubic millimeter of cortex, containing about 100,000 neurons, cortical anatomy suggests a more realistic architecture. In this locally connected random network, the connection probability decreases in a Gaussian fashion with the distance between neurons. Here we present three main results from a simulation study of the activity dynamics in such networks. First, for a broad range of parameters these dynamics exhibit a stationary state of asynchronous network activity with irregular single-neuron spiking. This state can be used as a realistic model of ongoing network activity. Parametric dependence of this state and the nature of the network dynamics in other regimes are described. Second, a synchronous excitatory stimulus to a fraction of the neurons results in a strong activity response that easily dominates the network dynamics. And third, due to that activity response an embedding of a divergent-convergent feed-forward subnetwork (as in synfire chains) does not naturally lead to a stable propagation of synchronous activity in the subnetwork; this is in contrast to our earlier findings in isolated subnetworks of that type. Possible mechanisms for stabilizing the interplay of volleys of synchronous spikes and network dynamics by specific learning rules or generalizations of the subnetworks are discussed.