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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

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Enabling Large-Scale Simulations With the GENESIS Neuronal Simulator.

Joshua C Crone¹, Manuel M Vindiola¹, Alfred B Yu²

¹Computational and Information Sciences Directorate, Army Research Laboratory, Aberdeen Proving Ground, MD, United States.

Frontiers in Neuroinformatics

|December 6, 2019

Summary

This summary is machine-generated.

This study enhances the GEneral NEural SImulation System (GENESIS) for large-scale neural network simulations. Optimized PGENESIS efficiently simulates complex neuronal models on supercomputers, enabling larger network sizes.

Keywords:

computational neuroscience high performance computing large-scale simulation multi-compartment neuron model multiscale modeling spiking neuronal network

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Area of Science:

Computational Neuroscience
Neuroscience Simulation Software

Background:

Large-scale neural network simulations are crucial for understanding brain function.
Previous benchmarks often used simplified neuronal models.
High-fidelity neuronal models present significant computational challenges.

Purpose of the Study:

To evaluate and improve the computational performance of the GEneral NEural SImulation System (GENESIS) for large-scale neural network simulations.
To enable simulations using complex, multi-compartment neuronal models.
To optimize GENESIS for high-performance computing environments.

Main Methods:

Modified the source code of GENESIS and its parallel implementation, PGENESIS, focusing on memory usage optimization.
Conducted large-scale simulations on supercomputing resources.
Utilized higher fidelity neuronal models with 50-74 compartments per neuron.

Main Results:

PGENESIS demonstrated efficient scaling on supercomputing resources.
Successfully simulated neural networks up to 9 × 10^6 neurons with 18 × 10^9 synapses.
Also simulated networks of 2.2 × 10^6 neurons with 45 × 10^9 synapses.
Optimizations improved memory usage for large-scale simulations.

Conclusions:

The modified PGENESIS enables efficient large-scale neural network simulations with high-fidelity neuronal models.
These advancements significantly expand the capacity for complex brain simulations.
The improvements are integrated into the official PGENESIS 2.4 release.