BioNet: A Python interface to NEURON for modeling large-scale networks

Abstract

There is a significant interest in the neuroscience community in the development of large-scale network models that would integrate diverse sets of experimental data to help elucidate mechanisms underlying neuronal activity and computations. Although powerful numerical simulators (e.g., NEURON, NEST) exist, data-driven large-scale modeling remains challenging due to difficulties involved in setting up and running network simulations. We developed a high-level application programming interface (API) in Python that facilitates building large-scale biophysically detailed networks and simulating them with NEURON on parallel computer architecture. This tool, termed “BioNet”, is designed to support a modular workflow whereby the description of a constructed model is saved as files that could be subsequently loaded for further refinement and/or simulation. The API supports both NEURON’s built-in as well as user-defined models of cells and synapses. It is capable of simulating a variety of observables directly supported by NEURON (e.g., spikes, membrane voltage, intracellular [Ca++]), as well as plugging in modules for computing additional observables (e.g. extracellular potential). The high-level API platform obviates the time-consuming development of custom code for implementing individual models, and enables easy model sharing via standardized files. This tool will help refocus neuroscientists on addressing outstanding scientific questions rather than developing narrow-purpose modeling code.

Chandelier cell anatomy and function reveal a variably distributed but common signal

“…For simulating the single neuron models under synaptic inputs, we have used Brain Modeling Toolkit (bmtk) (Gratiy et al, 2018) with NEURON 7.5 simulation environment. In this study we have used all-active models for 4 cells with ids : 477127614, 571306690, 584254833, 382982932 from the Allen Cell-Types database.…”

The Scientific Case for Brain Simulations

“…For these electrical, magnetic and optical measures the `measurement physics' seems well established, that is, mathematical models for the biophysical link between electrical activity in neurons and what is measured by such recordings have been developed, see references in caption of Figure 1. Simulation tools such as LFPy (lfpy.github.io) and BIONET (alleninstitute.github.io/bmtk/bionet.html) for prediction of such electrical and magnetic signals from simulated network activity, both using biophysically-detailed multicompartment models (Lindén et al, 2014;Gratiy et al, 2018;Hagen et al, 2018) and point-neuron models of the integrate-and-fire type (Hagen et al, 2016), are now publically available. For functional magnetic resonance imaging (fMRI) the biophysical link between activity in individual neurons and the recorded BOLD signal is not yet established (but see Uhlirova et al (2016b,a)), and a mechanistic forward-modeling procedure linking microscopic brain activity to the measurements is not yet available.…”

Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex

“…Our models are represented with a standardized data format SONATA (Dai et. al 2019, github.com/AllenInstitute/sonata) via the Brain Modeling ToolKit (BMTK, github.com/AllenInstitute/bmtk; (Gratiy et al, 2018)) and the open source software NEURON (Hines and Carnevale, 1997) and NEST (Gewaltig and Diesmann, 2007). The SONATA format is also supported by other modeling tools, including RTNeuron (Hernando et al, 2013), NeuroML (Gleeson, Steuber and Silver, 2007), PyNN (Davison et al, 2009), andNetPyNE (Dura-Bernal et al, 2019).…”

“…Our models use the Brain Modeling ToolKit (BMTK, github.com/AllenInstitute/bmtk) (Gratiy et al, 2018) that facilitates building large-scale networks with both NEURON (Hines and Carnevale, 1997) and NEST (Gewaltig and Diesmann, 2007). The model architecture and its outputs (e.g., transmembrane voltage across dendrites and soma, spiking activity) are saved using the standardized SONATA format (github.com/AllenInstitute/sonata, (Dai et al, 2019)).…”