Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits

Abstract

Experimental studies in neuroscience are producing data at a rapidly increasing rate, providing exciting opportunities and formidable challenges to existing theoretical and modeling approaches. To turn massive datasets into predictive quantitative frameworks, the field needs software solutions for systematic integration of data into realistic, multiscale models. Here we describe the Brain Modeling ToolKit (BMTK), a software suite for building models and performing simulations at multiple levels of resolution, from biophysically detailed multi-compartmental, to point-neuron, to population-statistical approaches. Leveraging the SONATA file format and existing software such as NEURON, NEST, and others, BMTK offers a consistent user experience across multiple levels of resolution. It permits highly sophisticated simulations to be set up with little coding required, thus lowering entry barriers to new users. We illustrate successful applications of BMTK to large-scale simulations of a cortical area. BMTK is an open-source package provided as a resource supporting modeling-based discovery in the community.

Scaffold modelling captures the structure-function-dynamics relationship in brain microcircuits

“…This work develops and benchmarks a new framework for multi-scale brain modelling (BSB), which accounts for the richness of network architectures and the variety of dynamic processes generating neural activity at different spatial and temporal scales. Although effective tools for microcircuit modelling appeared recently [BMTK (Billeh et al, 2020;Dai et al, 2020), NetPyNE (Dura-Bernal et al, 2019, Snudda (Hjorth et al, 2020) PyCabnn (Wichert et al, 2020)], their connectivity rules deal well with population-level and probabilistic approaches but a subset of modelling problems remains unsolved, when it comes to dealing with neurons as entities in space with specific morphologies. BSB addresses these needs with a set of tools designed to work with complex network topologies, cell morphologies and many other spatial and n-point problems.…”

“…: "videoS2.html", on this URL. The Allen Institute is developing the Brain Modelling ToolKit (BMTK) (Dai et al, 2020) that has been exploited to reconstruct cerebro-cortical microcircuits (Billeh et al, 2020) and provides an interface with multiple simulators. Another recent tool is NetPyNE (Dura-Bernal et al, 2019), with a graphical and Python interface for NEURON.…”

“…NEURON (Hines and Carnevale, 1997) and NEST (Gewaltig and Diesmann, 2007). There are modelling frameworks with solutions to most common problems (Dai et al, 2020;Dura-Bernal et al, 2019;Gratiy et al, 2018) but there is still a need for toolkits taking into account the location, morphologies and rotation of cells and using these properties to determine connectivity. Here, we introduce the Brain Scaffold Builder (BSB), an advanced framework for neuronal circuit modelling, with specific modules for network reconstruction and simulation.…”

Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era

“…A number of simulation engines have been developed, including general simulators such as NEURON ( Hines and Carnevale, 1997 ), NEST ( Gewaltig and Diesmann, 2007 ), Brian ( Stimberg et al, 2019 ), Nengo ( Bekolay et al, 2014 ), Neurokernel ( Givon and Lazar, 2016 ), DynaSim ( Sherfey et al, 2018 ), and the ones that specialize in multi-scale simulation, for example MOOSE ( Ray and Bhalla, 2008 ), in compartmental models, for example ARBOR ( Akar et al, 2019 ), and in fMRI-scale simulation for example The Virtual Brain ( Sanz Leon et al, 2013 ; Melozzi et al, 2017 ). Other tools improve the accessibility to these simulators by (i) facilitating the creation of large-scale neural networks, for example BMTK ( Dai et al, 2020a ) and NetPyNE ( Dura-Bernal et al, 2019 ), and by (ii) providing a common interface, simplifying the simulation workflow and streamlining parallelization of simulation, for example PyNN ( Davison et al, 2008 ), Arachne ( Aleksin et al, 2017 ), and NeuroManager ( Stockton and Santamaria, 2015 ). To facilitate access and exchange of neurobiological data worldwide, a number of model specification standards have been worked upon in parallel including MorphML ( Crook et al, 2007 ), NeuroML ( Gleeson et al, 2010 ), SpineML ( Tomkins et al, 2016 ), and SONATA ( Dai et al, 2020b ).…”

Brain signal predictions from multi-scale networks using a linearized framework

“…Multicompartment (MC) models have for decades been the go-to tool for geometrically detailed conductance-based neuron models as tailored software solvers are readily available such as NEURON (Carnevale and Hines 2006) and Arbor (Akar et al 2019). For the purpose of computing extracellular electric and magnetic signals, transmembrane currents from the MC neuron simulation is then combined with the appropriate forward model derived using linear volume conductor theory, as incorporated in software interfacing the neural simulator like LFPy (Lindén et al 2014; Hagen et al 2018), NetPyNe (Dura-Bernal et al 2019) and BMTK (Dai et al 2020). For brain tissues, a linear relationship between transmembrane currents and extracellular electric potentials as well as magnetic fields appears well established (Nicholson and Freeman 1975; Nunez and Srinivasan 2006; Logothetis et al 2007; Miceli et al 2017).…”