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Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

Efficient simulation of neural development using shared memory parallelization.

Erik De Schutter^1,2

¹Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Okinawa, Japan.

Frontiers in Neuroinformatics

|August 7, 2023

Summary

The Neural Development Simulator (NeuroDevSim) models brain development processes like growth and pruning using agent-based methods. This parallel processing software achieves efficient simulation of neural structures on multi-core systems.

Keywords:

growth migration neural development parallel algorithm retraction shared memory

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Area of Science:

Computational neuroscience
Developmental biology
Software engineering

Background:

Simulating complex neural development is computationally intensive.
Existing models may lack efficient parallel processing capabilities.
Understanding neural morphological growth, migration, and pruning is key to developmental neuroscience.

Purpose of the Study:

To introduce NeuroDevSim, a Python module for simulating key aspects of neural development.
To present an agent-based modeling approach for neural growth, migration, and pruning.
To demonstrate efficient parallel processing for large-scale neural simulations.

Main Methods:

Utilizes an agent-based modeling approach with 'fronts' for neural development processes.
Implements a novel shared memory approach for multi-core parallel processing without messaging.
Employs a coding rule to avoid memory conflicts, with a serialized lock broker for collision detection.

Main Results:

NeuroDevSim simulates morphological growth, migration, and pruning of neurons.
Achieves linear strong parallel scaling up to 96 cores, successfully running on 128 cores.
Enables modeling of diverse neural networks, from few complex to thousands of simple neurons.

Conclusions:

NeuroDevSim provides an efficient and scalable platform for simulating neural development.
The shared memory approach offers significant parallel processing advantages.
Facilitates research in computational neuroscience and developmental biology through accessible modeling.