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Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

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Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform.

Subhashini Sivagnanam¹, Wyatt Gorman², Donald Doherty³

¹State University of New York DMC, Brooklyn NY; San Diego Supercomputer Center / University California San Diego, La Jolla CA.

PEARC20 : Practice and Experience in Advanced Research Computing 2020 : Catch the Wave : July 27-31, 2020, Portland, or Virtual Conference. Practice and Experience in Advanced Research Computing (Conference) (2020 : Online)

|January 31, 2022

Summary

We optimized complex brain circuit simulations using Google Compute Platform (GCP) and Slurm. This approach efficiently handles large-scale neural modeling, integrating diverse experimental data for precise brain circuit exploration.

Keywords:

Applied computing → Life and medical sciences Brain modeling Computational neuroscience Computing methodologies → Modeling and simulation Google Cloud Platform Large-scale simulations

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Area of Science:

Computational Neuroscience
Neuroscience
High-Performance Computing

Background:

Biophysically detailed modeling is crucial for integrating experimental data and simulating brain circuits with high precision.
Large-scale neural simulations require significant computational resources.

Purpose of the Study:

To describe the setup and utilization of Google Compute Platform (GCP) with Slurm for large-scale biophysically detailed brain circuit simulations.
To present best practices and solutions for challenges encountered during high-performance computing for neuroscience.

Main Methods:

Developed a detailed model of motor cortex circuits, including over 10,000 neurons and 30 million synaptic connections.
Employed parameter exploration using grid search and evolutionary algorithms for model optimization.
Utilized Google Compute Platform (GCP) with the Slurm workload manager for high-throughput simulations.

Main Results:

Successfully configured and executed tens of thousands of large-scale neural simulations on GCP.
Identified and addressed practical challenges associated with cloud-based high-performance computing for neuroscience.
Obtained preliminary results from the simulated motor cortex circuits.

Conclusions:

Google Compute Platform (GCP) with Slurm provides a viable and powerful environment for large-scale, biophysically detailed neural simulations.
The described methodology and solutions facilitate efficient computational neuroscience research.
This work enables advanced exploration of brain circuit dynamics through scalable simulation.