Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex

TL;DR

This paper presents an open-source, large-scale spiking model of macaque monkey cortex, demonstrating its usage, scalability, and providing guidance for building computational neuroscience models from data to visualization.

Contribution

It introduces a comprehensive, well-documented large-scale cortical model, detailing its implementation, scaling, and workflow organization for future neuroscience modeling efforts.

Findings

Model relates cortical connectivity to resting-state dynamics

Demonstrates model's runtime scalability

Provides workflow guidance for large-scale neural modeling

Abstract

We are entering an age of `big' computational neuroscience, in which neural network models are increasing in size and in numbers of underlying data sets. Consolidating the zoo of models into large-scale models simultaneously consistent with a wide range of data is only possible through the effort of large teams, which can be spread across multiple research institutions. To ensure that computational neuroscientists can build on each other's work, it is important to make models publicly available as well-documented code. This chapter describes such an open-source model, which relates the connectivity structure of all vision-related cortical areas of the macaque monkey with their resting-state dynamics. We give a brief overview of how to use the executable model specification, which employs NEST as simulation engine, and show its runtime scaling. The solutions found serve as an example for organizing the workflow of future models from the raw experimental data to the visualization of the results, expose the challenges, and give guidance for the construction of ICT infrastructure for neuroscience.