Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons

Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavio...

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Bibliographic Details
Published in:eLife 2022-11, Vol.11
Main Authors: Oláh, Viktor J, Pedersen, Nigel P, Rowan, Matthew J M
Format: Article
Language:English
Subjects:
Action potential
Action Potentials - physiology
Analysis
Animals
artificial neural net
Brain
Channel gating
computational model
Computational neuroscience
Computer Simulation
cortex
Datasets
deep learning
Mammals
Models, Neurological
Nelson, Ed
Neocortex - physiology
Neural networks
Neurons
Neurons - physiology
Neuroscience
NMDA
Rett syndrome
Simulation
Tools and Resources
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Summary:Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources.
ISSN:2050-084X
2050-084X
DOI:10.7554/ELIFE.79535