(schema:networks_)= # Networks **Network descriptions for NeuroML 2. Describes {ref}`schema:network` elements containing {ref}`schema:population`s ( potentially of type {ref}`schema:populationlist`, and so specifying a list of cell {ref}`schema:location`s ), {ref}`schema:projection`s ( i.e. lists of {ref}`schema:connection`s ) and {ref}`schema:input`s.** --- Original ComponentType definitions: [Networks.xml](https://github.com/NeuroML/NeuroML2/blob/master/NeuroML2CoreTypes//Networks.xml). Schema against which NeuroML based on these should be valid: [NeuroML_v2.3.xsd](https://github.com/NeuroML/NeuroML2/tree/master/Schemas/NeuroML2/NeuroML_v2.3.xsd). Generated on 14/08/24 from [this](https://github.com/NeuroML/NeuroML2/commit/352244cff605cb1ba24fa7c11757dc818fe90fd2) commit. Please file any issues or questions at the [issue tracker here](https://github.com/NeuroML/NeuroML2/issues). --- (schema:network)= ## network extends *{ref}`schema:basestandalone`* Network containing: {ref}`schema:population`s ( potentially of type {ref}`schema:populationlist`, and so specifying a list of cell {ref}`schema:location`s ); {ref}`schema:projection`s ( with lists of {ref}`schema:connection`s ) and/or {ref}`schema:explicitconnection`s; and {ref}`schema:inputlist`s ( with lists of {ref}`schema:input`s ) and/or {ref}`schema:explicitinput`s. Note: often in NeuroML this will be of type {ref}`schema:networkwithtemperature` if there are temperature dependent elements ( e.g. ion channels ). `````{tab-set} ````{tab-item} Children list ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **regions**$ $ {ref}`schema:region` **populations**$ $ {ref}`schema:basepopulation` **projections**$ $ {ref}`schema:projection` **synapticConnections**$ $ {ref}`schema:explicitconnection` **electricalProjection**$ $ {ref}`schema:electricalprojection` **continuousProjection**$ $ {ref}`schema:continuousprojection` **explicitInputs**$ $ {ref}`schema:explicitinput` **inputs**$ $ {ref}`schema:inputlist` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import Network from neuroml.utils import component_factory variable = component_factory( Network, id: 'a NmlId (required)' = None, metaid: 'a MetaId (optional)' = None, notes: 'a string (optional)' = None, properties: 'list of Property(s) (optional)' = None, annotation: 'a Annotation (optional)' = None, type: 'a networkTypes (optional)' = None, temperature: 'a Nml2Quantity_temperature (optional)' = None, neuro_lex_id: 'a NeuroLexId (optional)' = None, spaces: 'list of Space(s) (optional)' = None, regions: 'list of Region(s) (optional)' = None, extracellular_properties: 'list of ExtracellularPropertiesLocal(s) (optional)' = None, populations: 'list of Population(s) (required)' = None, cell_sets: 'list of CellSet(s) (optional)' = None, synaptic_connections: 'list of SynapticConnection(s) (optional)' = None, projections: 'list of Projection(s) (optional)' = None, electrical_projections: 'list of ElectricalProjection(s) (optional)' = None, continuous_projections: 'list of ContinuousProjection(s) (optional)' = None, explicit_inputs: 'list of ExplicitInput(s) (optional)' = None, input_lists: 'list of InputList(s) (optional)' = None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:networkwithtemperature)= ## networkWithTemperature extends {ref}`schema:network` Same as {ref}`schema:network`, but with an explicit **temperature** for temperature dependent elements ( e.g. ion channels ). `````{tab-set} ````{tab-item} Parameters ```{csv-table} :widths: 1, 7, 2 :width: 100 % :delim: $ **temperature**$ ${ref}`schema:dimensions:temperature` ``` ```` ````` (schema:basepopulation)= ## *basePopulation* extends *{ref}`schema:basestandalone`* A population of multiple instances of a specific **component,** which anything which extends {ref}`schema:basecell`. `````{tab-set} ````{tab-item} Component References ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **component**$ $ {ref}`schema:basecell` ``` ```` ````{tab-item} Child list ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **notes**$ $ {ref}`schema:notes` **annotation**$ $ {ref}`schema:annotation` ``` ```` ````{tab-item} Children list ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **property**$ $ {ref}`schema:property` ``` ```` ````` (schema:population)= ## population extends *{ref}`schema:basepopulation`* A population of components, with just one parameter for the **size,** i.e. number of components to create. Note: quite often this is used with type= {ref}`schema:populationlist` which means the size is determined by the number of {ref}`schema:instance`s ( with {ref}`schema:location`s ) in the list. The **size** attribute is still set, and there will be a validation error if this does not match the number in the list. `````{tab-set} ````{tab-item} Parameters ```{csv-table} :widths: 1, 7, 2 :width: 100 % :delim: $ **size**$ Number of instances of this Component to create when the population is instantiated $Dimensionless ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import Population from neuroml.utils import component_factory variable = component_factory( Population, id: 'a NmlId (required)' = None, metaid: 'a MetaId (optional)' = None, notes: 'a string (optional)' = None, properties: 'list of Property(s) (optional)' = None, annotation: 'a Annotation (optional)' = None, component: 'a NmlId (required)' = None, size: 'a NonNegativeInteger (optional)' = None, type: 'a populationTypes (optional)' = None, extracellular_properties: 'a NmlId (optional)' = None, neuro_lex_id: 'a NeuroLexId (optional)' = None, layout: 'a Layout (optional)' = None, instances: 'list of Instance(s) (required)' = None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:populationlist)= ## populationList extends *{ref}`schema:basepopulation`* An explicit list of {ref}`schema:instance`s ( with {ref}`schema:location`s ) of components in the population. `````{tab-set} ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **size**$ Note: the size of the populationList to create is set by the number of explicitly defined instances. The size attribute is still set, and there will be a validation error if this does not match the number in the list. ```` ````{tab-item} Children list ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **instances**$ $ {ref}`schema:instance` ``` ```` ````` (schema:instance)= ## instance Specifies a single instance of a component in a {ref}`schema:population` ( placed at {ref}`schema:location` ). `````{tab-set} ````{tab-item} Child list ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **location**$ $ {ref}`schema:location` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import Instance from neuroml.utils import component_factory variable = component_factory( Instance, id: 'a nonNegativeInteger (optional)' = None, i: 'a nonNegativeInteger (optional)' = None, j: 'a nonNegativeInteger (optional)' = None, k: 'a nonNegativeInteger (optional)' = None, location: 'a Location (required)' = None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:location)= ## location Specifies the ( x, y, z ) location of a single {ref}`schema:instance` of a component in a {ref}`schema:population`. `````{tab-set} ````{tab-item} Parameters ```{csv-table} :widths: 1, 7, 2 :width: 100 % :delim: $ **x**$ $Dimensionless **y**$ $Dimensionless **z**$ $Dimensionless ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import Location from neuroml.utils import component_factory variable = component_factory( Location, x: 'a float (required)' = None, y: 'a float (required)' = None, z: 'a float (required)' = None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:region)= ## region Initial attempt to specify 3D region for placing cells. Work in progress... `````{tab-set} ````{tab-item} Child list ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **rectangularExtent**$ $ {ref}`schema:rectangularextent` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import Region from neuroml.utils import component_factory variable = component_factory( Region, id: 'a NmlId (required)' = None, spaces: 'a NmlId (optional)' = None, anytypeobjs_=None, ) ``` ```` ````` (schema:rectangularextent)= ## rectangularExtent For defining a 3D rectangular box. `````{tab-set} ````{tab-item} Parameters ```{csv-table} :widths: 1, 7, 2 :width: 100 % :delim: $ **xLength**$ $Dimensionless **xStart**$ $Dimensionless **yLength**$ $Dimensionless **yStart**$ $Dimensionless **zLength**$ $Dimensionless **zStart**$ $Dimensionless ``` ```` ````` (schema:projection)= ## projection Projection from one population, **presynapticPopulation** to another, **postsynapticPopulation,** through **synapse.** Contains lists of {ref}`schema:connection` or {ref}`schema:connectionwd` elements. `````{tab-set} ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **presynapticPopulation**$ **postsynapticPopulation**$ ```` ````{tab-item} Component References ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **synapse**$ $ {ref}`schema:basesynapse` ``` ```` ````{tab-item} Children list ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **connections**$ $ {ref}`schema:connection` **connectionsWD**$ $ {ref}`schema:connectionwd` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import Projection from neuroml.utils import component_factory variable = component_factory( Projection, id: 'a NmlId (required)' = None, presynaptic_population: 'a NmlId (required)' = None, postsynaptic_population: 'a NmlId (required)' = None, synapse: 'a NmlId (required)' = None, connections: 'list of Connection(s) (optional)' = None, connection_wds: 'list of ConnectionWD(s) (optional)' = None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml --> ``` ```{code-block} xml ``` ```` ````` (schema:explicitconnection)= ## explicitConnection Explicit event connection between components. `````{tab-set} ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **targetPort**$ ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **from**$ **to**$ ```` ````` (schema:connection)= ## connection Event connection directly between named components, which gets processed via a new instance of a **synapse** component which is created on the target component. Normally contained inside a {ref}`schema:projection` element. `````{tab-set} ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **destination**$ **preFractionAlong**$ **postFractionAlong**$ **preSegmentId**$ **postSegmentId**$ ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **preCellId**$ **postCellId**$ ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import Connection from neuroml.utils import component_factory variable = component_factory( Connection, id: 'a NonNegativeInteger (required)' = None, neuro_lex_id: 'a NeuroLexId (optional)' = None, pre_cell_id: 'a Nml2PopulationReferencePath (required)' = None, pre_segment_id: 'a NonNegativeInteger (optional)' = '0', pre_fraction_along: 'a ZeroToOne (optional)' = '0.5', post_cell_id: 'a Nml2PopulationReferencePath (required)' = None, post_segment_id: 'a NonNegativeInteger (optional)' = '0', post_fraction_along: 'a ZeroToOne (optional)' = '0.5', ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:synapticconnection)= ## synapticConnection extends {ref}`schema:explicitconnection` Explicit event connection between named components, which gets processed via a new instance of a **synapse** component which is created on the target component. `````{tab-set} ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **destination**$ ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **from**$ **to**$ ```` ````{tab-item} Component References ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **synapse**$ $ {ref}`schema:basesynapse` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import SynapticConnection from neuroml.utils import component_factory variable = component_factory( SynapticConnection, neuro_lex_id: 'a NeuroLexId (optional)' = None, from_: 'a Nml2PopulationReferencePath (required)' = None, to: 'a Nml2PopulationReferencePath (required)' = None, synapse: 'a NmlId (required)' = None, destination: 'a NmlId (optional)' = None, ) ``` ```` ````` (schema:synapticconnectionwd)= ## synapticConnectionWD extends {ref}`schema:synapticconnection` Explicit event connection between named components, which gets processed via a new instance of a **synapse** component which is created on the target component, includes setting of **weight** and **delay** for the synaptic connection. `````{tab-set} ````{tab-item} Parameters ```{csv-table} :widths: 1, 7, 2 :width: 100 % :delim: $ **delay**$ ${ref}`schema:dimensions:time` **weight**$ $Dimensionless ``` ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **from**$ **to**$ ```` ````` (schema:connectionwd)= ## connectionWD extends {ref}`schema:connection` Event connection between named components, which gets processed via a new instance of a synapse component which is created on the target component, includes setting of **weight** and **delay** for the synaptic connection. `````{tab-set} ````{tab-item} Parameters ```{csv-table} :widths: 1, 7, 2 :width: 100 % :delim: $ **delay**$ ${ref}`schema:dimensions:time` **weight**$ $Dimensionless ``` ```` ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **destination**$ **preFractionAlong**$ **postFractionAlong**$ **preSegmentId**$ **postSegmentId**$ ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **preCellId**$ **postCellId**$ ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import ConnectionWD from neuroml.utils import component_factory variable = component_factory( ConnectionWD, id: 'a NonNegativeInteger (required)' = None, neuro_lex_id: 'a NeuroLexId (optional)' = None, pre_cell_id: 'a Nml2PopulationReferencePath (required)' = None, pre_segment_id: 'a NonNegativeInteger (optional)' = '0', pre_fraction_along: 'a ZeroToOne (optional)' = '0.5', post_cell_id: 'a Nml2PopulationReferencePath (required)' = None, post_segment_id: 'a NonNegativeInteger (optional)' = '0', post_fraction_along: 'a ZeroToOne (optional)' = '0.5', weight: 'a float (required)' = None, delay: 'a Nml2Quantity_time (required)' = None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:electricalconnection)= ## electricalConnection To enable connections between populations through gap junctions. `````{tab-set} ````{tab-item} Component References ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **synapse**$ $ {ref}`schema:gapjunction` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import ElectricalConnection from neuroml.utils import component_factory variable = component_factory( ElectricalConnection, id: 'a NonNegativeInteger (required)' = None, neuro_lex_id: 'a NeuroLexId (optional)' = None, pre_cell: 'a string (required)' = None, pre_segment: 'a NonNegativeInteger (optional)' = '0', pre_fraction_along: 'a ZeroToOne (optional)' = '0.5', post_cell: 'a string (required)' = None, post_segment: 'a NonNegativeInteger (optional)' = '0', post_fraction_along: 'a ZeroToOne (optional)' = '0.5', synapse: 'a NmlId (required)' = None, extensiontype_=None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```` ````` (schema:electricalconnectioninstance)= ## electricalConnectionInstance To enable connections between populations through gap junctions. Populations need to be of type {ref}`schema:populationlist` and contain {ref}`schema:instance` and {ref}`schema:location` elements. `````{tab-set} ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **preFractionAlong**$ **postFractionAlong**$ **preSegment**$ **postSegment**$ ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **preCell**$ **postCell**$ ```` ````{tab-item} Component References ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **synapse**$ $ {ref}`schema:gapjunction` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import ElectricalConnectionInstance from neuroml.utils import component_factory variable = component_factory( ElectricalConnectionInstance, id: 'a NonNegativeInteger (required)' = None, neuro_lex_id: 'a NeuroLexId (optional)' = None, pre_cell: 'a string (required)' = None, pre_segment: 'a NonNegativeInteger (optional)' = '0', pre_fraction_along: 'a ZeroToOne (optional)' = '0.5', post_cell: 'a string (required)' = None, post_segment: 'a NonNegativeInteger (optional)' = '0', post_fraction_along: 'a ZeroToOne (optional)' = '0.5', synapse: 'a NmlId (required)' = None, extensiontype_=None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```` ````` (schema:electricalconnectioninstancew)= ## electricalConnectionInstanceW extends {ref}`schema:electricalconnectioninstance` To enable connections between populations through gap junctions. Populations need to be of type {ref}`schema:populationlist` and contain {ref}`schema:instance` and {ref}`schema:location` elements. Includes setting of **weight** for the connection. `````{tab-set} ````{tab-item} Parameters ```{csv-table} :widths: 1, 7, 2 :width: 100 % :delim: $ **weight**$ $Dimensionless ``` ```` ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **preFractionAlong**$ **postFractionAlong**$ **preSegment**$ **postSegment**$ ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **preCell**$ **postCell**$ ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import ElectricalConnectionInstanceW from neuroml.utils import component_factory variable = component_factory( ElectricalConnectionInstanceW, id: 'a NonNegativeInteger (required)' = None, neuro_lex_id: 'a NeuroLexId (optional)' = None, pre_cell: 'a string (required)' = None, pre_segment: 'a NonNegativeInteger (optional)' = '0', pre_fraction_along: 'a ZeroToOne (optional)' = '0.5', post_cell: 'a string (required)' = None, post_segment: 'a NonNegativeInteger (optional)' = '0', post_fraction_along: 'a ZeroToOne (optional)' = '0.5', synapse: 'a NmlId (required)' = None, weight: 'a float (required)' = None, ) ``` ```` ````` (schema:electricalprojection)= ## electricalProjection A projection between **presynapticPopulation** to another **postsynapticPopulation** through gap junctions. `````{tab-set} ````{tab-item} Component References ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **presynapticPopulation**$ $ {ref}`schema:population` **postsynapticPopulation**$ $ {ref}`schema:population` ``` ```` ````{tab-item} Children list ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **connections**$ $ {ref}`schema:electricalconnection` **connectionInstances**$ $ {ref}`schema:electricalconnectioninstance` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import ElectricalProjection from neuroml.utils import component_factory variable = component_factory( ElectricalProjection, id: 'a NmlId (required)' = None, presynaptic_population: 'a NmlId (required)' = None, postsynaptic_population: 'a NmlId (required)' = None, electrical_connections: 'list of ElectricalConnection(s) (optional)' = None, electrical_connection_instances: 'list of ElectricalConnectionInstance(s) (optional)' = None, electrical_connection_instance_ws: 'list of ElectricalConnectionInstanceW(s) (optional)' = None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:continuousconnection)= ## continuousConnection An instance of a connection in a {ref}`schema:continuousprojection` between **presynapticPopulation** to another **postsynapticPopulation** through a **preComponent** at the start and **postComponent** at the end. Can be used for analog synapses. `````{tab-set} ````{tab-item} Component References ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **preComponent**$ $ {ref}`schema:basegradedsynapse` **postComponent**$ $ {ref}`schema:basegradedsynapse` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import ContinuousConnection from neuroml.utils import component_factory variable = component_factory( ContinuousConnection, id: 'a NonNegativeInteger (required)' = None, neuro_lex_id: 'a NeuroLexId (optional)' = None, pre_cell: 'a string (required)' = None, pre_segment: 'a NonNegativeInteger (optional)' = '0', pre_fraction_along: 'a ZeroToOne (optional)' = '0.5', post_cell: 'a string (required)' = None, post_segment: 'a NonNegativeInteger (optional)' = '0', post_fraction_along: 'a ZeroToOne (optional)' = '0.5', pre_component: 'a NmlId (required)' = None, post_component: 'a NmlId (required)' = None, extensiontype_=None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:continuousconnectioninstance)= ## continuousConnectionInstance An instance of a connection in a {ref}`schema:continuousprojection` between **presynapticPopulation** to another **postsynapticPopulation** through a **preComponent** at the start and **postComponent** at the end. Populations need to be of type {ref}`schema:populationlist` and contain {ref}`schema:instance` and {ref}`schema:location` elements. Can be used for analog synapses. `````{tab-set} ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **preFractionAlong**$ **postFractionAlong**$ **preSegment**$ **postSegment**$ ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **preCell**$ **postCell**$ ```` ````{tab-item} Component References ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **preComponent**$ $ {ref}`schema:basegradedsynapse` **postComponent**$ $ {ref}`schema:basegradedsynapse` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import ContinuousConnectionInstance from neuroml.utils import component_factory variable = component_factory( ContinuousConnectionInstance, id: 'a NonNegativeInteger (required)' = None, neuro_lex_id: 'a NeuroLexId (optional)' = None, pre_cell: 'a string (required)' = None, pre_segment: 'a NonNegativeInteger (optional)' = '0', pre_fraction_along: 'a ZeroToOne (optional)' = '0.5', post_cell: 'a string (required)' = None, post_segment: 'a NonNegativeInteger (optional)' = '0', post_fraction_along: 'a ZeroToOne (optional)' = '0.5', pre_component: 'a NmlId (required)' = None, post_component: 'a NmlId (required)' = None, extensiontype_=None, ) ``` ```` ````` (schema:continuousconnectioninstancew)= ## continuousConnectionInstanceW extends {ref}`schema:continuousconnectioninstance` An instance of a connection in a {ref}`schema:continuousprojection` between **presynapticPopulation** to another **postsynapticPopulation** through a **preComponent** at the start and **postComponent** at the end. Populations need to be of type {ref}`schema:populationlist` and contain {ref}`schema:instance` and {ref}`schema:location` elements. Can be used for analog synapses. Includes setting of **weight** for the connection. `````{tab-set} ````{tab-item} Parameters ```{csv-table} :widths: 1, 7, 2 :width: 100 % :delim: $ **weight**$ $Dimensionless ``` ```` ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **preFractionAlong**$ **postFractionAlong**$ **preSegment**$ **postSegment**$ ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **preCell**$ **postCell**$ ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import ContinuousConnectionInstanceW from neuroml.utils import component_factory variable = component_factory( ContinuousConnectionInstanceW, id: 'a NonNegativeInteger (required)' = None, neuro_lex_id: 'a NeuroLexId (optional)' = None, pre_cell: 'a string (required)' = None, pre_segment: 'a NonNegativeInteger (optional)' = '0', pre_fraction_along: 'a ZeroToOne (optional)' = '0.5', post_cell: 'a string (required)' = None, post_segment: 'a NonNegativeInteger (optional)' = '0', post_fraction_along: 'a ZeroToOne (optional)' = '0.5', pre_component: 'a NmlId (required)' = None, post_component: 'a NmlId (required)' = None, weight: 'a float (required)' = None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```` ````` (schema:continuousprojection)= ## continuousProjection A projection between **presynapticPopulation** and **postsynapticPopulation** through components **preComponent** at the start and **postComponent** at the end of a {ref}`schema:continuousconnection` or {ref}`schema:continuousconnectioninstance`. Can be used for analog synapses. `````{tab-set} ````{tab-item} Component References ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **presynapticPopulation**$ $ {ref}`schema:population` **postsynapticPopulation**$ $ {ref}`schema:population` ``` ```` ````{tab-item} Children list ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **connections**$ $ {ref}`schema:continuousconnection` **connectionInstances**$ $ {ref}`schema:continuousconnectioninstance` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import ContinuousProjection from neuroml.utils import component_factory variable = component_factory( ContinuousProjection, id: 'a NmlId (required)' = None, presynaptic_population: 'a NmlId (required)' = None, postsynaptic_population: 'a NmlId (required)' = None, continuous_connections: 'list of ContinuousConnection(s) (optional)' = None, continuous_connection_instances: 'list of ContinuousConnectionInstance(s) (optional)' = None, continuous_connection_instance_ws: 'list of ContinuousConnectionInstanceW(s) (optional)' = None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:explicitinput)= ## explicitInput An explicit input ( anything which extends {ref}`schema:basepointcurrent` ) to a target cell in a population. `````{tab-set} ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **destination**$ **sourcePort**$ **targetPort**$ ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **target**$ ```` ````{tab-item} Component References ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **input**$ $ {ref}`schema:basepointcurrent` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import ExplicitInput from neuroml.utils import component_factory variable = component_factory( ExplicitInput, target: 'a Nml2PopulationReferencePath (required)' = None, input: 'a NmlId (required)' = None, destination: 'a NmlId (optional)' = None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:inputlist)= ## inputList An explicit list of {ref}`schema:input`s to a **population.**. `````{tab-set} ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **population**$ ```` ````{tab-item} Component References ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **component**$ $ {ref}`schema:basepointcurrent` ``` ```` ````{tab-item} Children list ```{csv-table} :widths: 1, 7, 2 :width: 100% :delim: $ **inputs**$ $ {ref}`schema:input` ``` ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import InputList from neuroml.utils import component_factory variable = component_factory( InputList, id: 'a NonNegativeInteger (required)' = None, populations: 'a NmlId (required)' = None, component: 'a NmlId (required)' = None, input: 'list of Input(s) (optional)' = None, input_ws: 'list of InputW(s) (optional)' = None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:input)= ## input Specifies a single input to a **target,** optionally giving the **segmentId** ( default 0 ) and **fractionAlong** the segment ( default 0.5 ). `````{tab-set} ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **segmentId**$ Optional specification of the segment to target, default 0 **fractionAlong**$ Optional specification of the fraction along the specified segment, default 0.5 **destination**$ ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **target**$ ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import Input from neuroml.utils import component_factory variable = component_factory( Input, id: 'a NonNegativeInteger (required)' = None, target: 'a Nml2PopulationReferencePath (required)' = None, destination: 'a NmlId (required)' = None, segment_id: 'a NonNegativeInteger (optional)' = None, fraction_along: 'a ZeroToOne (optional)' = None, extensiontype_=None, ) ``` ```` ````{tab-item} Usage: XML ```{code-block} xml ``` ```{code-block} xml ``` ```{code-block} xml ``` ```` ````` (schema:inputw)= ## inputW extends {ref}`schema:input` Specifies input lists. Can set **weight** to scale individual inputs. `````{tab-set} ````{tab-item} Parameters ```{csv-table} :widths: 1, 7, 2 :width: 100 % :delim: $ **weight**$ $Dimensionless ``` ```` ````{tab-item} Text fields ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **destination**$ ```` ````{tab-item} Paths ```{csv-table} :widths: 1, 7 :width: 100% :delim: $ **target**$ ```` ````{tab-item} Schema ```{code-block} xml ``` ```` ````{tab-item} Usage: Python *Go to the libNeuroML documentation* ```{code-block} python from neuroml import InputW from neuroml.utils import component_factory variable = component_factory( InputW, id: 'a NonNegativeInteger (required)' = None, target: 'a Nml2PopulationReferencePath (required)' = None, destination: 'a NmlId (required)' = None, segment_id: 'a NonNegativeInteger (optional)' = None, fraction_along: 'a ZeroToOne (optional)' = None, weight: 'a float (required)' = None, ) ``` ```` `````