An embedded network approach for scale-up of fluctuation-driven systems with preservation of spike information.
An embedded network approach for scale-up of fluctuation-driven systems with preservation of spike information.
复制标题
一种嵌入式网络方法，用于扩展波动驱动系统并保留尖峰信息。
DOI：
10.1073/pnas.0404062101
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发表时间：
2004
期刊：
影响因子：
--
通讯作者：
McLaughlin,DavidW
中科院分区：
文献类型：
--
作者：
Cai,David;Tao,Louis;McLaughlin,DavidW
为了解决大脑皮层大区域建模中的计算“放大”问题，已经调用了许多粗粒化过程来获得对神经元网络动力学的有效描述。然而，由于在空间和时间上的局部平均，这些方法不包含详细的尖峰信息，因此不能用于研究例如通过详细的尖峰定时统计编码的皮层机制。为了保留棘波的高阶统计信息，我们发展了一个混合理论框架，该框架将点神经元的子网络嵌入到动态背景的粗粒度网络中，并与之完全交互。我们使用一个新发展的动力学理论来描述粗粒背景，并结合泊松尖峰重建过程来确保我们的方法既适用于平均驱动的区域，也适用于涨落驱动的区域。实验结果表明，该嵌入式网络方法具有较高的动态精度和数值效率。作为一个例子，我们使用这种嵌入的表示法来构建“反向时间关联”，作为方位调整动力学的环形模型中的尖峰触发平均值。
To address computational “scale-up” issues in modeling large regions of the cortex, many coarse-graining procedures have been invoked to obtain effective descriptions of neuronal network dynamics. However, because of local averaging in space and time, these methods do not contain detailed spike information and, thus, cannot be used to investigate, e.g., cortical mechanisms that are encoded through detailed spike-timing statistics. To retain high-order statistical information of spikes, we develop a hybrid theoretical framework that embeds a subnetwork of point neurons within, and fully interacting with, a coarse-grained network of dynamical background. We use a newly developed kinetic theory for the description of the coarse-grained background, in combination with a Poisson spike reconstruction procedure to ensure that our method applies to the fluctuation-driven regime as well as to the mean-driven regime. This embedded-network approach is verified to be dynamically accurate and numerically efficient. As an example, we use this embedded representation to construct “reverse-time correlations” as spiked-triggered averages in a ring model of orientation-tuning dynamics.