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 time constants to synaptic connection probabilities. To understand how multiple parameters contribute synergistically to circuit behavior as a whole, neuronal computational models are regularly employed. However, traditional models containing anatomically and biophysically realistic neurons are computationally expensive when scaled to model local circuits. To overcome this limitation, we trained several artificial neural net (ANN) architectures to model the activity of realistic, multicompartmental neurons. We identified a single ANN that accurately predicted both subthreshold and action potential firing, and correctly generalized its responses to previously unobserved synaptic input. 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, our ANN approach allows for rapid, detailed network experiments using inexpensive, readily available computational resources.

Simulations of cortical networks using spatially extended conductance‐based neuronal models

“…Standardized model sharing is becoming more common thanks to the modelling file formats or languages such as NeuroML (Cannon et al, 2014;Gleeson et al, 2010), PyNN (Davison et al, 2009), NSDF (Ray et al, 2016) and SONATA (Dai, Hernando et al, 2020). Additionally, acceleration by orders of magnitude relative to standard CPU simulations has recently been demonstrated using convolutional network approximation of neurons on GPUs (Beniaguev et al, 2021;Oláh et al, 2021).…”