Scorcioni Ruggero, Hamilton David J, Ascoli Giorgio A
Center for Neural Informatics, Structure, and Plasticity (CN3), Krasnow Institute for Advanced Study, George Mason University, Mail Stop 2A1, Fairfax, VA 22030-4444, USA.
Neural Netw. 2008 Oct;21(8):1153-63. doi: 10.1016/j.neunet.2008.05.006. Epub 2008 Jun 4.
The mammalian hippocampus is involved in spatial representation and memory storage and retrieval, and much research is ongoing to elucidate the cellular and system-level mechanisms underlying these cognitive functions. Modeling may be useful to link network-level activity patterns to the relevant features of hippocampal anatomy and electrophysiology. Investigating the effects of circuit connectivity requires simulations of a number of neurons close to real scale. To this end, we construct a model of the hippocampus with 16 distinct neuronal classes (including both local and projection cells) and 200,000 individual neurons. The number of neurons in each class and their interconnectivity are drawn from rat anatomy. Here we analyze the emergent network activity and how it is affected by reducing either the size or the connectivity diversity of the model. When the model is run with a simple variation of the McCulloch-Pitts formalism, self-sustaining non-repetitive activity patterns consistently emerge. Specific firing threshold values are narrowly constrained for each cell class upon multiple runs with different stochastic wiring and initial conditions, yet these values do not directly affect network stability. Analysis of the model at different network sizes demonstrates that a scale reduction of one order of magnitude drastically alters network dynamics, including the variability of the output range, the distribution of firing frequencies, and the duration of self-sustained activity. Moreover, comparing the model to a control condition with an equivalent number of (excitatory/inhibitory balanced) synapses, but removing all class-specific information (i.e. collapsing the network to homogeneous random connectivity) has surprisingly similar effects to downsizing the total number of neurons. The reduced-scale model is also compared directly with integrate-and-fire simulations, which capture considerably more physiological detail at the single-cell level, but still fail to reproduce the full behavioral complexity of the large-scale model. Thus network size, cell class diversity, and connectivity details may all be critical to generate self-sustained non-repetitive activity patterns.
哺乳动物的海马体参与空间表征以及记忆的存储和提取，目前有许多研究正在进行，以阐明这些认知功能背后的细胞和系统层面的机制。建模可能有助于将网络层面的活动模式与海马体解剖结构和电生理学的相关特征联系起来。研究电路连接性的影响需要对接近实际规模的大量神经元进行模拟。为此，我们构建了一个包含16种不同神经元类型（包括局部神经元和投射神经元）以及200,000个单个神经元的海马体模型。每个类型的神经元数量及其相互连接性均取自大鼠的解剖结构。在这里，我们分析了涌现的网络活动，以及模型规模或连接性多样性的降低如何对其产生影响。当该模型使用麦卡洛克 - 皮茨形式主义的简单变体运行时，会持续出现自持性非重复活动模式。在多次使用不同的随机布线和初始条件运行时，每个细胞类型的特定放电阈值被严格限制，但这些值并不会直接影响网络稳定性。对不同网络规模的模型进行分析表明，规模缩小一个数量级会极大地改变网络动态，包括输出范围的变异性、放电频率的分布以及自持活动的持续时间。此外，将该模型与具有同等数量（兴奋性/抑制性平衡）突触的对照条件进行比较，但去除所有特定类型信息（即将网络简化为均匀随机连接），其效果与缩小神经元总数惊人地相似。规模缩小后的模型还直接与积分发放模拟进行了比较，后者在单细胞层面捕捉到了更多的生理细节，但仍然无法重现大规模模型的全部行为复杂性。因此，网络规模、细胞类型多样性和连接性细节可能对于产生自持性非重复活动模式都至关重要。
