Incorporating anatomically realistic cellular-level connectivity in neural network models of the rat hippocampus
Incorporating anatomically realistic cellular-level connectivity in neural network models of the rat hippocampus
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DOI：
10.1016/j.biosystems.2004.09.024
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发表时间：
2005-01-01
期刊：
影响因子：
1.6
通讯作者：
Atkeson, JC
中科院分区：
文献类型：
--
作者：
Ascoli, GA;Atkeson, JC
神经元类之间的特定连接模式可以在许多脑区域的放电动力学调节中发挥重要作用。然而，大多数神经网络模型都是基于大大简化的连接方案构建的，这些方案不能准确反映生物的复杂性。以大鼠海马为例，我们在这里表明，足够的定量信息是在神经解剖学文献中构建神经网络来自准确的细胞连接模型。基于这种方法的计算模拟有助于直接调查蜂窝连接和网络活动之间的潜在关系。我们定义了一组基本参数来表征细胞连接性，并从已发表的报告中收集大鼠海马的相关值。基于这些数据的初步模拟揭示了前馈抑制神经元的一种新的推定作用。特别地，齿状回中的“mopp”细胞适合于帮助将颗粒细胞的放电率维持在生理水平内，以响应来自内嗅皮层的可能的噪声输入。前馈抑制的稳定效果进一步表明，取决于主细胞和中间神经元的相对阈值之间的特定比例。我们通过一个可公开访问的网络档案（http：//www.krasnow.gmu.edu/L-Neuron）免费分发本研究所基于的连接数据。(C)2004爱思唯尔爱尔兰有限公司保留所有权利。
The specific connectivity patterns among neuronal classes can play an important role in the regulation of firing dynamics in many brain regions. Yet most neural network models are built based on vastly simplified connectivity schemes that do not accurately reflect the biological complexity. Taking the rat hippocampus as an example, we show here that enough quantitative information is available in the neuroanatomical literature to construct neural networks derived from accurate models of cellular connectivity. Computational simulations based on this approach lend themselves to a direct investigation of the potential relationship between cellular connectivity and network activity. We define a set of fundamental parameters to characterize cellular connectivity, and are collecting the related values for the rat hippocampus from published reports. Preliminary simulations based on these data uncovered a novel putative role for feedforward inhibitory neurons. In particular, "mopp" cells in the dentate gyrus are suitable to help maintain the firing rate of granule cells within physiological levels in response to a plausibly noisy input from the entorhinal cortex. The stabilizing effect of feedforward inhibition is further shown to depend on the particular ratio between the relative threshold values of the principal cells and the interneurons. We are freely distributing the connectivity data on which this study is based through a publicly accessible web archive (http://www.krasnow.gmu.edu/L-Neuron). (C) 2004 Elsevier Ireland Ltd. All rights reserved.