Predicting Synaptic Connectivity for Large-Scale Microcircuit Simulations Using Snudda

J J Johannes Hjorth,Alexander Kozlov,Jeanette Hellgren Kotaleski

doi:10.1007/s12021-021-09531-w

Abstract

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.

Highlights

Neuroscientists are producing data at an ever growing rate, and sharing the data in public databases
An important goal is that the model building process should be transparent and possible to reproduce by other labs, and the model should be extendable when new data accumulate
The pipeline is developed for setting up large-scale simulations of subcortical nuclei, such as striatum

Summary

Introduction

Neuroscientists are producing data at an ever growing rate, and sharing the data in public databases. Our approach offers novel contributions in the following aspects: (i) we design and present a complete, free and open source toolchain for building and simulating anatomically constrained biologically detailed neural networks including morphology-based neuron touch detection; (ii) we illustrate the use of this platform on the example of the striatal microcircuit, implemented at a very detailed level and accuracy; (iii) we include all tools and parameters in the source code repository, enabling other labs to reproduce as well as reconstruct our striatal model with new data when it becomes available.

Results

Conclusion