Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

Abstract

The primary somatosensory cortex (S1) of mammals is critically important in the perception of touch and related sensorimotor behaviors. In 2015 the Blue Brain Project developed a groundbreaking rat S1 microcircuit simulation with over 31,000 neurons with 207 morpho-electrical neuron types, and 37 million synapses, incorporating anatomical and physiological information from a wide range of experimental studies. We have implemented this highly-detailed and complex model S1 model in NetPyNE, using the data available in the Neocortical Microcircuit Collaboration Portal. NetPyNE provides a Python high-level interface to NEURON and allows defining complicated multiscale models using an intuitive declarative standardized language. It also facilitates running parallel simulations, automates the optimization and exploration of parameters using supercomputers, and provides a wide range of built-in analysis functions. This will make the S1 model more accessible and simpler to scale, modify and extend in order to explore research questions or interconnect to other existing models. Despite some implementation differences, the NetPyNE model closely reproduced the original cell morphologies, electrophysiological responses, spatial distribution for all 207 cell types; and the connectivity properties of the 1941 pathways, including synaptic dynamics and short-term plasticity (STP). The NetPyNE S1 simulations produced reasonable physiological rates and activity patterns across all populations, and, when STP was included, produced a 1 Hz oscillation comparable to that of the original model. We also extended the model by adding thalamic circuits, including 6 distinct thalamic populations with intrathalamic, thalamocortical and corticothalamic connectivity derived from experimental data. Overall, our work provides a widely accessible, data-driven and biophysically-detailed model of the somatosensory thalamocortical circuits that can be utilized as a community tool for researchers to study neural dynamics, function and disease.

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

“…In recent years, several large-scale and biophysically detailed models have been developed (Markram et al, 2015 ; Casali et al, 2019 ; Billeh et al, 2020 ; Hjorth et al, 2020 ; Borges et al, 2022 ; Dura-Bernal et al, 2022a , b ). Some of these models are no longer only models of networks and their signal processing but are actually biophysical models of the tissue itself (Markram et al, 2015 ), capturing a diverse set of properties of the modeled brain region, also referred to as “digital twins.” Such models have proved useful to bridge anatomy and physiology across multiple spatial and time scales (Reimann et al, 2017 ; Newton et al, 2021 ).…”

“…NetPyNE (Dura-Bernal et al, 2019 ) is a high-level declarative NEURON wrapper used to develop a wide range of neural circuit models (Metzner et al, 2020 ; Anwar et al, 2021 ; Bryson et al, 2021 ; Pimentel et al, 2021 ; Ranieri et al, 2021 ; Romaro et al, 2021 ; Volk et al, 2021 ; Borges et al, 2022 ; Dura-Bernal et al, 2022a , b ; Medlock et al, 2022 ) 4 , and also as a resource for teaching neurobiology and computational neuroscience.…”

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

“…In recent years, several large-scale and biophysically detailed models have been developed (Markram et al, 2015; Billeh et al, 2020; Casali et al, 2019; Hjorth et al, 2020; Dura-Bernal et al, 2022a,b; Borges et al, 2022). Some of these models are no longer only models of networks and their signal processing but are actually biophysical models of the tissue itself (Markram et al, 2015), capturing a diverse set of properties of the modeled brain region, also referred to as “digital twins”.…”

“…NetPyNE (Dura-Bernal et al, 2019) is a high-level declarative NEURON wrapper used to develop a wide range of neural circuit models (Bryson et al, 2021; Pimentel et al, 2021; Volk et al, 2021; Ranieri et al, 2021; Metzner et al, 2020; Anwar et al, 2021; Sekiguchi et al, 2021; Dura-Bernal et al, 2022a,b; Borges et al, 2022; Romaro et al, 2021) 4 , and also as a resource for teaching neurobiology and computational neuroscience.…”

“…When running on a GPU, however, one must use the special executable to launch simulations due to limitations of the NVIDIA compiler toolchain when using OpenACC together with shared libraries. (Bryson et al, 2021;Pimentel et al, 2021;Volk et al, 2021;Ranieri et al, 2021;Metzner et al, 2020;Anwar et al, 2021;Sekiguchi et al, 2021;Dura-Bernal et al, 2022a,b;Borges et al, 2022;Romaro et al, 2021) 4 , and also as a resource for teaching neurobiology and computational neuroscience.…”