NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail
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{"title"=>"NeuroML: A language for describing data driven models of neurons and networks with a high degree of biological detail", "type"=>"journal", "authors"=>[{"first_name"=>"Padraig", "last_name"=>"Gleeson", "scopus_author_id"=>"16202455700"}, {"first_name"=>"Sharon", "last_name"=>"Crook", "scopus_author_id"=>"18433315700"}, {"first_name"=>"Robert C.", "last_name"=>"Cannon", "scopus_author_id"=>"7201491609"}, {"first_name"=>"Michael L.", "last_name"=>"Hines", "scopus_author_id"=>"35428221400"}, {"first_name"=>"Guy O.", "last_name"=>"Billings", "scopus_author_id"=>"26024997800"}, {"first_name"=>"Matteo", "last_name"=>"Farinella", "scopus_author_id"=>"36245162000"}, {"first_name"=>"Thomas M.", "last_name"=>"Morse", "scopus_author_id"=>"7005748827"}, {"first_name"=>"Andrew P.", "last_name"=>"Davison", "scopus_author_id"=>"8940202300"}, {"first_name"=>"Subhasis", "last_name"=>"Ray", "scopus_author_id"=>"36454995300"}, {"first_name"=>"Upinder S.", "last_name"=>"Bhalla", "scopus_author_id"=>"55962484900"}, {"first_name"=>"Simon R.", "last_name"=>"Barnes", "scopus_author_id"=>"36247230400"}, {"first_name"=>"Yoana D.", "last_name"=>"Dimitrova", "scopus_author_id"=>"36247268400"}, {"first_name"=>"R. Angus", "last_name"=>"Silver", "scopus_author_id"=>"7201936933"}], "year"=>2010, "source"=>"PLoS Computational Biology", "identifiers"=>{"scopus"=>"2-s2.0-77955483472", "doi"=>"10.1371/journal.pcbi.1000815", "isbn"=>"1553-7358 (Electronic)\\n1553-734X (Linking)", "issn"=>"1553734X", "pmid"=>"20585541", "pui"=>"359337536", "sgr"=>"77955483472"}, "id"=>"8131fa94-db60-3acd-9f87-6511b19a0da1", "abstract"=>"Computer modeling is becoming an increasingly valuable tool in the study of the complex interactions underlying the behavior of the brain. Software applications have been developed which make it easier to create models of neural networks as well as detailed models which replicate the electrical activity of individual neurons. The code formats used by each of these applications are generally incompatible however, making it difficult to exchange models and ideas between researchers. Here we present the structure of a neuronal model description language, NeuroML. This provides a way to express these complex models in a common format based on the underlying physiology, allowing them to be mapped to multiple applications. We have tested this language by converting published neuronal models to NeuroML format and comparing their behavior on a number of commonly used simulators. Creating a common, accessible model description format will expose more of the model details to the wider neuroscience community, thus increasing their quality and reliability, as for other Open Source software. NeuroML will also allow a greater “ecosystem” of tools to be developed for building, simulating and analyzing these complex neuronal systems.", "link"=>"http://www.mendeley.com/research/neuroml-language-describing-data-driven-models-neurons-networks-high-degree-biological-detail", "reader_count"=>179, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>13, "Researcher"=>46, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>49, "Student > Postgraduate"=>5, "Student > Master"=>21, "Other"=>12, "Student > Bachelor"=>9, "Lecturer"=>4, "Professor"=>11}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>13, "Researcher"=>46, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>49, "Student > Postgraduate"=>5, "Student > Master"=>21, "Other"=>12, "Student > Bachelor"=>9, "Lecturer"=>4, "Professor"=>11}, "reader_count_by_subject_area"=>{"Engineering"=>17, "Unspecified"=>9, "Environmental Science"=>1, "Mathematics"=>4, "Agricultural and Biological Sciences"=>79, "Medicine and Dentistry"=>2, "Neuroscience"=>16, "Physics and Astronomy"=>10, "Psychology"=>4, "Social Sciences"=>1, "Computer Science"=>35, "Linguistics"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>17}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>16}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>10}, "Psychology"=>{"Psychology"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>79}, "Computer Science"=>{"Computer Science"=>35}, "Linguistics"=>{"Linguistics"=>1}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>9}, "Environmental Science"=>{"Environmental Science"=>1}}, "reader_count_by_country"=>{"Republic of Singapore"=>1, "Hungary"=>1, "United States"=>7, "Japan"=>1, "United Kingdom"=>7, "Belarus"=>1, "Portugal"=>1, "India"=>1, "Canada"=>1, "Latvia"=>1, "South Korea"=>1, "Belgium"=>2, "Finland"=>1, "France"=>1, "Germany"=>5}, "group_count"=>15}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/844906"], "description"=>"<p>The core elements for expressing networks are <i>population</i> for homogenous groups of cells positioned in 3D, <i>projections</i> for synaptic contacts between (or within) populations and <i>inputs</i> for electrical stimulation to the network. The networks can either be expressed as lists of precise positions, connections and input locations (instance based representation) or as templates for generating these lists (template based representation). A detailed description of each of these elements can be found in Supporting <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815.s001\" target=\"_blank\">Text S1</a>.</p>", "links"=>[], "tags"=>["computational biology/computational neuroscience", "computational biology/systems biology", "neuroscience", "neuroscience/theoretical neuroscience"], "article_id"=>515341, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Elements_in_NetworkML_/515341", "title"=>"Elements in NetworkML.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:29:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/845214"], "description"=>"<p>(A) Voltage traces from a pair of gap junction coupled model cells (300 pS) during 0.19 nA current pulse injected into one of the cells. Blue indicates cell receiving current pulse and red shows gap junction coupled cell simulated in GENESIS. White overlapping dashes indicate the same model in NEURON. Black overlapping dashes indicate the same model in MOOSE. (B) Simulated EPSCs for a single compartment cell receiving synaptic input through an AMPA receptor only synapse at a membrane potential of −80 mV (red) and −20 mV (blue) in GENESIS. Again, the dashed lines indicate the equivalent NEURON (white) and MOOSE (black) simulations. (C) As B but for a single compartment cell receiving synaptic input through an NMDA receptor only synapse. (D) Short–term plasticity (STP) model <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815-Tsodyks1\" target=\"_blank\">[45]</a>: membrane potential of a postsynaptic cell receiving a regular presynaptic spike train for a synaptic connection exhibiting no STP (green, left), facilitation (red, middle) and depression (blue, right) implemented on the NEST (colored) and NEURON (white overlap) simulators.</p>", "links"=>[], "tags"=>["synapses", "implemented"], "article_id"=>515654, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g008", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Models_of_electrical_and_chemical_synapses_implemented_in_NeuroML_/515654", "title"=>"Models of electrical and chemical synapses implemented in NeuroML.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:34:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/420527", "https://ndownloader.figshare.com/files/420637", "https://ndownloader.figshare.com/files/420669", "https://ndownloader.figshare.com/files/420699", "https://ndownloader.figshare.com/files/420772", "https://ndownloader.figshare.com/files/420862", "https://ndownloader.figshare.com/files/420950", "https://ndownloader.figshare.com/files/420980", "https://ndownloader.figshare.com/files/421020", "https://ndownloader.figshare.com/files/421081"], "description"=>"<div><p>Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.</p></div>", "links"=>[], "tags"=>["describing", "driven", "models", "neurons", "networks"], "article_id"=>143025, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1000815.s001", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s002", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s003", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s004", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s005", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s006", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s007", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s008", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s009", "https://dx.doi.org/10.1371/journal.pcbi.1000815.s010"], "stats"=>{"downloads"=>5, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/NeuroML_A_Language_for_Describing_Data_Driven_Models_of_Neurons_and_Networks_with_a_High_Degree_of_Biological_Detail/143025", "title"=>"NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-06-17 00:50:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/844713"], "description"=>"<p>ChannelML allows expression of models of voltage (and ligand) gated conductances which are dispersed across the cell membrane (in <i>channel_type</i> element), conductances which are concentrated at synaptic contacts (in <i>synapse_type</i> element) and basic models of time varying internal ion concentrations (in <i>ion_concentration</i> element). Distributed conductance descriptions contain a number of <i>gate</i> elements, which describe the transitions between conducting and non conducting states of the channels underlying the conductances. A number of synaptic conductance models are allowed including simple double exponential waveforms, AMPA and NMDA receptor mediated synapses, Short Term Plasticity (STP) models, Spike Timing Dependent Plasticity (STDP) models, and electrical synapses. The <i>ion_concentration</i> element can be used for the simple models of exponentially decaying Ca<sup>2+</sup> pools often used in detailed cell models. A detailed description of each of these elements can be found in Supporting <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815.s001\" target=\"_blank\">Text S1</a>.</p>", "links"=>[], "tags"=>["computational biology/computational neuroscience", "computational biology/systems biology", "neuroscience", "neuroscience/theoretical neuroscience"], "article_id"=>515138, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g005", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Elements_in_ChannelML_/515138", "title"=>"Elements in ChannelML.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:25:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/844521"], "description"=>"<p>The main element for expressing a branching neuronal structure in NeuroML is <i>cell</i> which is used for all Levels in NeuroML. The core of the cell description is a set of <i>segment</i> elements which describe the 3D shape of the cell. These can be grouped into <i>cables</i> which represent unbranched neurites of the cell. Metadata present in the cell description can contain details of the creators of the cell model, or the data on which it was based (e.g. a neuronal reconstruction from NeuroMorpho.org). Addition of the <i>biophysics</i> element allows a Level 2 conductance based spiking cell model to be described, and the <i>connectivity</i> element can be used for the allowed synaptic connectivity of a Level 3 cell (e.g. to be used when connecting the cell in a network). A detailed description of each of these elements can be found in Supporting <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815.s001\" target=\"_blank\">Text S1</a>. Only the elements in Level 1 which are normally used in compartmental cell modeling are shown in the figure. Other elements such as <i>freePoints</i>, <i>features</i> etc. could be present in a Level 1 file from a camera lucida reconstruction <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815-Crook1\" target=\"_blank\">[38]</a>.</p>", "links"=>[], "tags"=>["cells", "neuroml", "levels"], "article_id"=>514955, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g004", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Elements_for_representing_cells_in_NeuroML_Levels_1_3_/514955", "title"=>"Elements for representing cells in NeuroML Levels 1-3.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:22:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/843922"], "description"=>"<p>Level 1 incorporates MorphML, which allows descriptions of cell structure ranging from single compartment cells to detailed cells based on morphological reconstructions. Metadata describing the provenance of the data (authors, citations, etc.) can be used at this and subsequent Levels. Level 2 builds on Level 1 to specify the passive properties and the location and densities of active conductances on the cell, and includes ChannelML, for description of the membrane processes that generate the electrophysiological behavior of cells. Level 3 contains NetworkML, allowing networks of these neuronal models and their synaptic connections to be described. MorphML, ChannelML and NetworkML can be used in isolation to describe model components, while a Level X file can include any elements from that and any lower Level.</p>", "links"=>[], "tags"=>["levels", "neuroml", "channelml"], "article_id"=>514361, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_the_three_Levels_of_NeuroML_and_MorphML_ChannelML_and_NetworkML_/514361", "title"=>"Relationship between the three Levels of NeuroML and MorphML, ChannelML and NetworkML.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:12:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/845402"], "description"=>"<p>(A) Single compartment cell model containing all 22 active conductances present in the detailed cell models (Supporting <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815.s008\" target=\"_blank\">Table S2</a>), together with a passive conductance and a decaying calcium pool. Left plot shows the membrane potential response to a 80 pA current injection on NEURON (black), GENESIS (red) and MOOSE (green). Right plot shows the behavior on NEURON of the activation variables for the anomalous rectifier (thick black line), L-type Ca<sup>2+</sup> (red) and persistent Na<sup>+</sup> conductances (green) and the inactivation variable of the fast Na<sup>+</sup> conductance (blue). White curve overlays show the corresponding GENESIS traces, and dashed lines show MOOSE traces. (B–E) 3D representations of four cell models from Traub et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815-Traub1\" target=\"_blank\">[15]</a> implemented in NeuroML, color indicates the density of fast sodium conductances on the cell membrane (red: high - yellow: low). Graphs show somatic membrane potential during current injections for: (B) regular spiking (RS) Layer 2/3 pyramidal cell; (C) superficial low threshold spiking (LTS) interneuron; (D) intrinsically bursting (IB) Layer 5 pyramidal cell; (E) nucleus reticularis thalami (nRT) cell (trace colors as for left panel of A). See Supporting <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815.s004\" target=\"_blank\">Figure S3</a> for further details of these and the 6 other electrically distinct thalamic and cortical cell models converted to NeuroML.</p>", "links"=>[], "tags"=>["neuroml-based", "cortical", "thalamic", "models", "genesis", "moose"], "article_id"=>515832, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g009", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_behavior_of_NeuroML_based_cortical_and_thalamic_cell_models_run_on_NEURON_GENESIS_and_MOOSE_simulators_/515832", "title"=>"Comparison of the behavior of NeuroML-based cortical and thalamic cell models run on NEURON, GENESIS and MOOSE simulators.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:37:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/844268"], "description"=>"<p>(A) A ChannelML file containing a Hodgkin-Huxley type K<sup>+</sup> conductance model, with four instances of a gating mechanism with open and closed states, and the rates of transitions between them. Supporting <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815.s001\" target=\"_blank\">Text S1</a> contains a description of each of the elements contained in this file, and section 10.2 of that document outlines in more detail the equations behind a channel model expressed in ChannelML. (B) A section of a HTML page automatically generated from the ChannelML using an XML Stylesheet (XSL) file. (C) Top: plots of the forward (alpha, black) and reverse (beta, red) transition rates. Bottom: the time constant (tau) of the transition (black) and steady state of the gating variable (inf, red). These views of the contents of the ChannelML file can be generated automatically (e.g. by neuroConstruct) for any valid file.</p>", "links"=>[], "tags"=>["channelml", "mappings"], "article_id"=>514699, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g003", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_XML_structure_of_a_ChannelML_file_and_mappings_to_text_and_graphs_/514699", "title"=>"XML structure of a ChannelML file and mappings to text and graphs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:18:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/845626"], "description"=>"<p>The network is based on the larger network described in Cunningham et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815-Cunningham1\" target=\"_blank\">[16]</a>, and uses five of the cortical cell models converted to NeuroML from Traub et al. <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815-Traub1\" target=\"_blank\">[15]</a>. (A) 20 regular spiking pyramidal cells (RS, blue), 6 fast rhythmic bursting pyramidal cells (FRB, black), 10 low threshold spiking interneurons (LTS, red), 10 axo-axonic interneurons (yellow) and 10 basket cells (brown) placed at random in a cylindrical region. The network contained electrical connections between the cells within each population, along with 4300 excitatory connections of 10 types within and between populations and 3800 inhibitory connections of 12 types (Supporting <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#pcbi.1000815.s010\" target=\"_blank\">Table S4</a>), but these are not shown. (B) Somatic membrane potential traces from 2 each of RS, FRB and LTS cells (with colors as in (A)) for simulations run on NEURON (top), GENESIS (middle) and MOOSE (bottom). Simulation time step was 0.001 ms.</p>", "links"=>[], "tags"=>["neuroml-based", "types", "connected", "synaptic", "connections", "genesis", "moose"], "article_id"=>516054, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g010", "stats"=>{"downloads"=>1, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_behavior_of_a_NeuroML_based_Layer_2_3_network_model_with_5_cell_types_connected_with_both_electrical_and_chemical_synaptic_connections_run_on_NEURON_GENESIS_and_MOOSE_simulators_/516054", "title"=>"Comparison of the behavior of a NeuroML-based Layer 2/3 network model with 5 cell types connected with both electrical and chemical synaptic connections run on NEURON, GENESIS and MOOSE simulators.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:40:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/845076"], "description"=>"<p>(A) Top: cell morphology visualized in neuroConstruct with color scale showing the density of h-type (HCN) channels (yellow lower, red higher). Bottom: voltage traces (in response to a current pulse input at the soma) at 5 different locations in the cell after execution on NEURON (gray), GENESIS (red), MOOSE (blue) and PSICS (green). (B) Voltage map of same cell executed on the NEURON simulator (top) and membrane potential traces (bottom) for the axon (black), soma (yellow) and 3 locations (green, blue, red) at increasing distances along the dendritic tree. (C) Recompartmentalized morphology visualized and run in GENESIS (top) with membrane potential traces (bottom, colors as for panel (B)). (D) Cell morphology visualized in PSICS using the ICING application (<a href=\"http://psics.org/icing\" target=\"_blank\">http://psics.org/icing</a>, top). Inset shows a small section of dendrite and the locations of the individual ion channels. Membrane potential traces obtained with PSICS below, with colors as for panel (B). MOOSE does not have a native graphical interface at present. The simulation time step in all cases was 0.002 ms, and spatial discretisation is described in <a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000815#s4\" target=\"_blank\">Materials and Methods</a>.</p>", "links"=>[], "tags"=>["pyramidal", "non-uniform", "conductances", "migliore", "et"], "article_id"=>515507, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g007", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_CA1_pyramidal_cell_model_with_non_uniform_active_conductances_based_on_Migliore_et_al_2_/515507", "title"=>"CA1 pyramidal cell model with non-uniform active conductances (based on Migliore et al.[2]).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:31:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/845791"], "description"=>"<p>The latest support for NeuroML in these and other computational neuroscience tools can be found at <a href=\"http://www.neuroml.org/tool_support\" target=\"_blank\">http://www.neuroml.org/tool_support</a>.</p><p>*Simulator mappings of PyNN which have been tested to date: NEURON, NEST.</p>", "links"=>[], "tags"=>["supported", "neuroml"], "article_id"=>516228, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.t001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_supported_NeuroML_features_in_applications_/516228", "title"=>"Summary of supported NeuroML features in applications.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-06-17 01:43:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/844094"], "description"=>"<p>Experimental neuroscience data is measured at different scales describing subcellular, cellular and network properties and NeuroML provides a framework to describe models developed using this data at all of these levels. Once models are defined in NeuroML they can either be directly imported into a simulator or translated via a metasimulator like neuroConstruct. Optimization of such data-driven models involves an iterative process of experimentation, creation of models, comparison with data and refinement of models, and suggestions for new experiments based on modeling results.</p>", "links"=>[], "tags"=>["components"], "article_id"=>514529, "categories"=>["Neuroscience", "Medicine"], "users"=>["Padraig Gleeson", "Sharon Crook", "Robert C. Cannon", "Michael L. Hines", "Guy O. Billings", "Matteo Farinella", "Thomas M. Morse", "Andrew P. Davison", "Subhasis Ray", "Upinder S. Bhalla", "Simon R. Barnes", "Yoana D. Dimitrova", "R. Angus Silver"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1000815.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relationship_between_experimental_data_and_model_components_expressed_in_NeuroML_/514529", "title"=>"Relationship between experimental data and model components expressed in NeuroML.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-06-17 01:15:29"}

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Relative Metric

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