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Article ; Online: Pitfalls in the interpretation of multielectrode data: on the infeasibility of the neuronal current-source monopoles.

Gratiy, Sergey L / Pettersen, Klas H / Einevoll, Gaute T / Dale, Anders M

2013 Volume 109, Issue 6, Page(s) 1681–1682

MeSH term(s)	Animals ; Electroencephalography/instrumentation ; Electroencephalography/methods ; Humans ; Male ; Models, Neurological ; Neocortex/physiology ; Neurons/physiology ; Synapses/physiology
Language	English
Publishing date	2013-03-18
Publishing country	United States
Document type	Letter ; Comment
ZDB-ID	80161-6
ISSN	1522-1598 ; 0022-3077
ISSN (online)	1522-1598
ISSN	0022-3077
DOI	10.1152/jn.01047.2012
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Article ; Online: Systematic Integration of Structural and Functional Data into Multi-scale Models of Mouse Primary Visual Cortex.

Billeh, Yazan N / Cai, Binghuang / Gratiy, Sergey L / Dai, Kael / Iyer, Ramakrishnan / Gouwens, Nathan W / Abbasi-Asl, Reza / Jia, Xiaoxuan / Siegle, Joshua H / Olsen, Shawn R / Koch, Christof / Mihalas, Stefan / Arkhipov, Anton

Neuron

2020 Volume 106, Issue 3, Page(s) 388–403.e18

Abstract: Structural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into a data-driven, ... ...

Abstract	Structural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into a data-driven, biologically realistic simulation of the awake mouse primary visual cortex. The model was constructed at two levels of granularity, using either biophysically detailed or point neurons. Both variants have identical network connectivity and were compared to each other and to experimental recordings of visual-driven neural activity. While tuning these networks to recapitulate experimental data, we identified rules governing cell-class-specific connectivity and synaptic strengths. These structural constraints constitute hypotheses that can be tested experimentally. Despite their distinct single-cell abstraction, both spatially extended and point models perform similarly at the level of firing rate distributions for the questions we investigated. All data and models are freely available as a resource for the community.
MeSH term(s)	Animals ; Mice ; Models, Neurological ; Neurons/physiology ; Synapses/physiology ; Systems Integration ; Visual Cortex/cytology ; Visual Cortex/physiology
Language	English
Publishing date	2020-03-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	808167-0
ISSN	1097-4199 ; 0896-6273
ISSN (online)	1097-4199
ISSN	0896-6273
DOI	10.1016/j.neuron.2020.01.040
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Article ; Online: Brain Modeling ToolKit: An open source software suite for multiscale modeling of brain circuits.

Dai, Kael / Gratiy, Sergey L / Billeh, Yazan N / Xu, Richard / Cai, Binghuang / Cain, Nicholas / Rimehaug, Atle E / Stasik, Alexander J / Einevoll, Gaute T / Mihalas, Stefan / Koch, Christof / Arkhipov, Anton

PLoS computational biology

2020 Volume 16, Issue 11, Page(s) e1008386

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 ... ...

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.
MeSH term(s)	Action Potentials ; Biophysical Phenomena ; Brain/physiology ; Brain Mapping/methods ; Computational Biology ; Humans ; Nerve Net ; Software
Language	English
Publishing date	2020-11-30
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1008386
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Article ; Online: From Maxwell's equations to the theory of current-source density analysis.

Gratiy, Sergey L / Halnes, Geir / Denman, Daniel / Hawrylycz, Michael J / Koch, Christof / Einevoll, Gaute T / Anastassiou, Costas A

The European journal of neuroscience

2017 Volume 45, Issue 8, Page(s) 1013–1023

Abstract: Despite the widespread use of current-source density (CSD) analysis of extracellular potential recordings in the brain, the physical mechanisms responsible for the generation of the signal are still debated. While the extracellular potential is thought ... ...

Abstract	Despite the widespread use of current-source density (CSD) analysis of extracellular potential recordings in the brain, the physical mechanisms responsible for the generation of the signal are still debated. While the extracellular potential is thought to be exclusively generated by the transmembrane currents, recent studies suggest that extracellular diffusive, advective and displacement currents-traditionally neglected-may also contribute considerably toward extracellular potential recordings. Here, we first justify the application of the electro-quasistatic approximation of Maxwell's equations to describe the electromagnetic field of physiological origin. Subsequently, we perform spatial averaging of currents in neural tissue to arrive at the notion of the CSD and derive an equation relating it to the extracellular potential. We show that, in general, the extracellular potential is determined by the CSD of membrane currents as well as the gradients of the putative extracellular diffusion current. The diffusion current can contribute significantly to the extracellular potential at frequencies less than a few Hertz; in which case it must be subtracted to obtain correct CSD estimates. We also show that the advective and displacement currents in the extracellular space are negligible for physiological frequencies while, within cellular membrane, displacement current contributes toward the CSD as a capacitive current. Taken together, these findings elucidate the relationship between electric currents and the extracellular potential in brain tissue and form the necessary foundation for the analysis of extracellular recordings.
MeSH term(s)	Algorithms ; Animals ; Brain/physiology ; Diffusion ; Electricity ; Electrodes, Implanted ; Electromagnetic Fields ; Male ; Membrane Potentials/physiology ; Mice, Inbred C57BL ; Models, Neurological ; Neurons/physiology ; Photic Stimulation ; Synaptic Transmission/physiology ; Visual Perception/physiology
Language	English
Publishing date	2017-03-28
Publishing country	France
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	645180-9
ISSN	1460-9568 ; 0953-816X
ISSN (online)	1460-9568
ISSN	0953-816X
DOI	10.1111/ejn.13534
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Article ; Online: The SONATA data format for efficient description of large-scale network models.

Dai, Kael / Hernando, Juan / Billeh, Yazan N / Gratiy, Sergey L / Planas, Judit / Davison, Andrew P / Dura-Bernal, Salvador / Gleeson, Padraig / Devresse, Adrien / Dichter, Benjamin K / Gevaert, Michael / King, James G / Van Geit, Werner A H / Povolotsky, Arseny V / Muller, Eilif / Courcol, Jean-Denis / Arkhipov, Anton

PLoS computational biology

2020 Volume 16, Issue 2, Page(s) e1007696

Abstract: Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and ... ...

Abstract	Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.
MeSH term(s)	Algorithms ; Brain/physiology ; Brain Mapping ; Computational Biology/methods ; Computer Simulation ; Databases, Factual ; Humans ; Models, Neurological ; Neurons/physiology ; Neurosciences ; Programming Languages ; Reproducibility of Results ; Software
Language	English
Publishing date	2020-02-24
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1007696
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Article ; Online: Brain Modeling ToolKit

Kael Dai / Sergey L Gratiy / Yazan N Billeh / Richard Xu / Binghuang Cai / Nicholas Cain / Atle E Rimehaug / Alexander J Stasik / Gaute T Einevoll / Stefan Mihalas / Christof Koch / Anton Arkhipov

PLoS Computational Biology, Vol 16, Iss 11, p e

An open source software suite for multiscale modeling of brain circuits.

2020 Volume 1008386

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.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2020-11-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: On the estimation of population-specific synaptic currents from laminar multielectrode recordings.

Gratiy, Sergey L / Devor, Anna / Einevoll, Gaute T / Dale, Anders M

Frontiers in neuroinformatics

2011 Volume 5, Page(s) 32

Abstract: Multielectrode array recordings of extracellular electrical field potentials along the depth axis of the cerebral cortex are gaining popularity as an approach for investigating the activity of cortical neuronal circuits. The low-frequency band of ... ...

Abstract	Multielectrode array recordings of extracellular electrical field potentials along the depth axis of the cerebral cortex are gaining popularity as an approach for investigating the activity of cortical neuronal circuits. The low-frequency band of extracellular potential, i.e., the local field potential (LFP), is assumed to reflect synaptic activity and can be used to extract the laminar current source density (CSD) profile. However, physiological interpretation of the CSD profile is uncertain because it does not disambiguate synaptic inputs from passive return currents and does not identify population-specific contributions to the signal. These limitations prevent interpretation of the CSD in terms of synaptic functional connectivity in the columnar microcircuit. Here we present a novel anatomically informed model for decomposing the LFP signal into population-specific contributions and for estimating the corresponding activated synaptic projections. This involves a linear forward model, which predicts the population-specific laminar LFP in response to synaptic inputs applied at different positions along each population and a linear inverse model, which reconstructs laminar profiles of synaptic inputs from laminar LFP data based on the forward model. Assuming spatially smooth synaptic inputs within individual populations, the model decomposes the columnar LFP into population-specific contributions and estimates the corresponding laminar profiles of synaptic input as a function of time. It should be noted that constant synaptic currents at all positions along a neuronal population cannot be reconstructed, as this does not result in a change in extracellular potential. However, constraining the solution using a priori knowledge of the spatial distribution of synaptic connectivity provides the further advantage of estimating the strength of active synaptic projections from the columnar LFP profile thus fully specifying synaptic inputs.
Language	English
Publishing date	2011-12-20
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2452979-5
ISSN	1662-5196 ; 1662-5196
ISSN (online)	1662-5196
ISSN	1662-5196
DOI	10.3389/fninf.2011.00032
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Article ; Online: BioNet: A Python interface to NEURON for modeling large-scale networks.

Gratiy, Sergey L / Billeh, Yazan N / Dai, Kael / Mitelut, Catalin / Feng, David / Gouwens, Nathan W / Cain, Nicholas / Koch, Christof / Anastassiou, Costas A / Arkhipov, Anton

PloS one

2018 Volume 13, Issue 8, Page(s) e0201630

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 ... ...

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.
MeSH term(s)	Animals ; Mice ; Models, Neurological ; Nerve Net/physiology ; Neurons/physiology ; Photic Stimulation ; Software ; Synapses/physiology
Language	English
Publishing date	2018-08-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0201630
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Article ; Online: BioNet

Sergey L Gratiy / Yazan N Billeh / Kael Dai / Catalin Mitelut / David Feng / Nathan W Gouwens / Nicholas Cain / Christof Koch / Costas A Anastassiou / Anton Arkhipov

PLoS ONE, Vol 13, Iss 8, p e

A Python interface to NEURON for modeling large-scale networks.

2018 Volume 0201630

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.
Keywords	Medicine ; R ; Science ; Q
Subject code	612
Language	English
Publishing date	2018-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: The SONATA data format for efficient description of large-scale network models.

Kael Dai / Juan Hernando / Yazan N Billeh / Sergey L Gratiy / Judit Planas / Andrew P Davison / Salvador Dura-Bernal / Padraig Gleeson / Adrien Devresse / Benjamin K Dichter / Michael Gevaert / James G King / Werner A H Van Geit / Arseny V Povolotsky / Eilif Muller / Jean-Denis Courcol / Anton Arkhipov

PLoS Computational Biology, Vol 16, Iss 2, p e

2020 Volume 1007696

Abstract	Increasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2020-02-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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