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

ABSTRACT: 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 s

SUBMITTER: Olah V 

PROVIDER: S-EPMC9640191 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature