Implementation of topographically constrained connectivity for a large-scale biologically realistic model of the hippocampus.
Implementation of topographically constrained connectivity for a large-scale biologically realistic model of the hippocampus.
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DOI：
10.1109/embc.2012.6346190
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Berger TW
中科院分区：
文献类型：
--
作者：
Yu GJ;Robinson BS;Hendrickson PJ;Song D;Berger TW
为了了解记忆在大脑中是如何工作的，海马体因其在长期记忆编码中的作用而受到高度研究。我们已经确定了四个特征，将有助于编码过程：神经元的形态，它们的生物物理学，突触可塑性，以及连接输入和海马内的神经元的地形。为了研究长期记忆是如何编码的，我们正在构建一个大规模的大鼠海马体生物现实模型。这项工作的重点是地形如何有助于海马体的输出。通常，大脑被构造成具有拓扑结构，使得由输入神经元群体形成的突触连接在空间上跨接收群体组织。我们模型的第一步是构建内嗅皮层输入如何连接到海马齿状回。我们已经从地形数据中获得了现实的约束条件，以连接两个细胞群。这些限制是如何应用的细节。我们证明了空间连接对模拟的输出有重大影响，结果强调了在大脑神经网络模型中仔细定义空间连接以生成相关时空模式的重要性。
In order to understand how memory works in the brain, the hippocampus is highly studied because of its role in the encoding of long-term memories. We have identified four characteristics that would contribute to the encoding process: the morphology of the neurons, their biophysics, synaptic plasticity, and the topography connecting the input to and the neurons within the hippocampus. To investigate how long-term memory is encoded, we are constructing a large-scale biologically realistic model of the rat hippocampus. This work focuses on how topography contributes to the output of the hippocampus. Generally, the brain is structured with topography such that the synaptic connections formed by an input neuron population are organized spatially across the receiving population. The first step in our model was to construct how entorhinal cortex inputs connect to the dentate gyrus of the hippocampus. We have derived realistic constraints from topographical data to connect the two cell populations. The details on how these constraints were applied are presented. We demonstrate that the spatial connectivity has a major impact on the output of the simulation, and the results emphasize the importance of carefully defining spatial connectivity in neural network models of the brain in order to generate relevant spatiotemporal patterns.