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首页> 外文期刊>Journal of Computational Neuroscience >Network bursting using experimentally constrained single compartment C A3 hippocampal neuron models with adaptation
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Network bursting using experimentally constrained single compartment C A3 hippocampal neuron models with adaptation

机译：使用实验性约束的单节C A3海马神经元模型进行网络爆发

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摘要

The hippocampus is a brain structure critical for memory functioning. Its network dynamics include several patterns such as sharp waves that are generated in the CA3 region. To understand how population outputs are generated, models need to consider aspects of network size, cellular and synaptic characteristics and context, which are necessarily 'balanced' in appropriate ways to produce particular outputs. Thick slice hippocampal preparations spontaneously produce sharp waves that are initiated in CA3 regions and depend on the right balance of glutamatergic activities. As a step toward developing network models that can explain important balances in the generation of hippocampal output, we develop models of CA3 pyramidal cells. Our models are single compartment in nature, use an Izhikevich-type structure and involve parameter values that are specifically designed to encompass CA3 intrinsic properties. Importantly, they incorporate spike frequency adaptation characteristics that are directly comparable to those measured experimentally. Excitatory networks using these model cells are able to produce bursting suggesting that the amount of spike frequency adaptation expressed in the biological cells is an essential contributor to network bursting, and as such, may be important for sharp wave generation. The network bursting mechanism is numerically dissected showing the critical balance between adaptation and excitatory drive. The compact nature of our models allows large network simulations to be efficiently computed. This, together with the linkage of our models to cellular characteristics, will allow us to develop an understanding of population output of CA3 hippocampus with direct biological comparisons.
机译：海马是记忆功能至关重要的大脑结构。它的网络动态性包括多种模式，例如在CA3区域中生成的尖波。为了理解人口产出是如何产生的，模型需要考虑网络规模，细胞和突触特征以及环境等方面，这些方面必须以适当的方式“平衡”以产生特定的产出。海马厚片制剂可自发产生尖峰波，这些尖波始于CA3区，并取决于谷氨酸能活动的正确平衡。作为开发可解释海马输出产生中重要平衡的网络模型的一步，我们开发了CA3锥体细胞模型。我们的模型本质上是单隔室的，使用Izhikevich型结构，并包含专门设计为包含CA3固有属性的参数值。重要的是，它们结合了尖峰频率适应特性，可与实验测量的特性直接媲美。使用这些模型细胞的兴奋性网络能够产生爆发，这表明生物细胞中表达的尖峰频率适应量是网络爆发的重要因素，因此，对于尖锐波的产生可能很重要。对网络爆发机制进行了数字分解，显示了适应性和兴奋性驱动之间的关键平衡。我们模型的紧凑性质允许有效地计算大型网络仿真。这以及我们的模型与细胞特征的联系，将使我们能够通过直接的生物学比较来了解CA3海马的种群输出。

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