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Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda
Neuroinformatics ( IF 3 ) Pub Date : 2021-07-19 , DOI: 10.1007/s12021-021-09531-w
J J Johannes Hjorth 1 , Jeanette Hellgren Kotaleski 1, 2 , Alexander Kozlov 1, 2
Affiliation  

Simulation of large-scale networks of neurons is an important approach to understanding and interpreting experimental data from healthy and diseased brains. Owing to the rapid development of simulation software and the accumulation of quantitative data of different neuronal types, it is possible to predict both computational and dynamical properties of local microcircuits in a ‘bottom-up’ manner. Simulated data from these models can be compared with experiments and ‘top-down’ modelling approaches, successively bridging the scales. Here we describe an open source pipeline, using the software Snudda, for predicting microcircuit connectivity and for setting up simulations using the NEURON simulation environment in a reproducible way. We also illustrate how to further ‘curate’ data on single neuron morphologies acquired from public databases. This model building pipeline was used to set up a first version of a full-scale cellular level model of mouse dorsal striatum. Model components from that work are here used to illustrate the different steps that are needed when modelling subcortical nuclei, such as the basal ganglia.



中文翻译:

使用 Snudda 预测大规模微电路仿真的突触连接

大规模神经元网络的模拟是理解和解释来自健康和患病大脑的实验数据的重要方法。由于模拟软件的快速发展和不同神经元类型的定量数据的积累,有可能以“自下而上”的方式预测局部微电路的计算和动力学特性。来自这些模型的模拟数据可以与实验和“自上而下”的建模方法进行比较,从而逐步缩小规模。在这里,我们描述了一个开源管道,使用软件 Snudda,用于预测微电路连接性并使用 NEURON 模拟环境以可重复的方式设置模拟。我们还说明了如何进一步“整理”从公共数据库获取的单个神经元形态的数据。该模型构建管道用于建立小鼠背侧纹状体全尺寸细胞水平模型的第一个版本。该工作中的模型组件在这里用于说明在对皮质下核(例如基底神经节)进行建模时所需的不同步骤。

更新日期:2021-07-20
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