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

被引:6
|
作者
Olah, Viktor J. [1 ]
Pedersen, Nigel P. [2 ]
Rowan, Matthew J. M. [1 ]
机构
[1] Emory Univ, Sch Med, Dept Cell Biol, Atlanta, GA 30322 USA
[2] Emory Univ, Sch Med, Dept Neurol, Atlanta, GA 30322 USA
来源
ELIFE | 2022年 / 11卷
基金
美国国家卫生研究院;
关键词
computational model; artificial neural net; NMDA; cortex; deep learning; None; ARTIFICIAL NEURAL-NETWORKS; 5 PYRAMIDAL NEURONS; RETT-SYNDROME; MOUSE MODEL; ACTION-POTENTIALS; SYNAPTIC PLASTICITY; GAMMA-OSCILLATIONS; DENDRITIC SPIKES; BASAL DENDRITES; SPIKING NEURONS;
D O I
10.7554/eLife.79535
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:34
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