opencortex core Package¶
This is the main module for user interaction with OpenCortex.
opencortex.core Package¶
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opencortex.core.add_exp_one_syn(nml_doc, id, gbase, erev, tau_decay)¶ Adds an <expOneSynapse> element to the document. See the definition of the behaviour of this here: https://www.neuroml.org/NeuroML2CoreTypes/Synapses.html#expOneSynapse
Returns the class created.
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opencortex.core.add_exp_two_syn(nml_doc, id, gbase, erev, tau_rise, tau_decay)¶ Adds an <expTwoSynapse> element to the document. See the definition of the behaviour of this here: https://www.neuroml.org/NeuroML2CoreTypes/Synapses.html#expTwoSynapse
Returns the class created.
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opencortex.core.add_gap_junction_synapse(nml_doc, id, conductance)¶ Adds a <gapJunction> element to the document. See the definition of the behaviour of this here: https://www.neuroml.org/NeuroML2CoreTypes/Synapses.html#gapJunction
Returns the class created.
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opencortex.core.add_inputs_to_population(net, id, population, input_comp_id, number_per_cell=1, all_cells=False, only_cells=None, segment_ids=[0], fraction_alongs=[0.5], weights=1)¶ Add current input to the specified population. Attributes:
- net
- reference to the network object previously created
- id
- id of the <inputList> to be created
- population
- the <population> to be targeted
- input_comp_id
- id of the component to be used for the input (e.g. added with add_pulse_generator())
- number_per_cell
- how many inputs to apply to each cell of the population. Default 1
- all_cells
- Whether to target all cells. Default False
- only_cells
- Which specific cells to target. List of ids. Default None
- segment_ids
- List of segment ids to place inputs onto on each cell. Either list of 1 value or list of number_per_cell entries. Default [0]
- fraction_alongs
- List of fractions along the specified segments to place inputs onto on each cell. Either list of 1 value or list of number_per_cell entries. Default [0.5]
- weights
- Either a scalar value (all weights set to this for all inputs), or a function to pick a (random) value per input. Default 1
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opencortex.core.add_poisson_firing_synapse(nml_doc, id, average_rate, synapse_id)¶ Adds a <poissonFiringSynapse> element to the document. See the definition of the behaviour of this here: https://www.neuroml.org/NeuroML2CoreTypes/Inputs.html#poissonFiringSynapse
Returns the class created.
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opencortex.core.add_population_in_rectangular_region(net, pop_id, cell_id, size, x_min, y_min, z_min, x_size, y_size, z_size, cell_bodies_overlap=True, store_soma=False, population_dictionary=None, cell_diameter_dict=None, color=None)¶ Method which creates a cell population in the NeuroML2 network and distributes these cells in the rectangular region. Input arguments are:
- net
- reference to the network object previously created
- pop_id
- population id
- cell_id
- cell component id
- size
- size of a population
- x_min
- lower x bound of a rectangular region
- y_min
- lower y bound of a rectangular region
- z_min
- lower z bound of a rectangular region
- x_size
- width of a rectangular region along x axis
- y_size
- width of a rectangular region along y axis
- z_size
- width of a rectangular region along z axis
- cell_bodies_overlap
- boolean value which defines whether cell somata can overlap; default is set to True
- store_soma
- boolean value which specifies whether soma positions have to be returned in the output array; default is set to False
- population_dictionary
- optional argument in the format returned by opencortex.utils.add_populations_in_rectangular_layers; default value is None but it must be specified when cell_bodies_overlap is set to False
- cell_diameter_dict
- optional argument in the format {‘cell_id1’: soma diameter of type ‘float’, ‘cell_id2’: soma diameter of type ‘float’}; default is None but it must be specified when cell_bodies_overlap is set to False
- color
- optional color (which will be put through to annotation in generated NeuroML); RGB format, 3 floats 0->1, e.g. 1 0 0 for red; default is None
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opencortex.core.add_probabilistic_projection(net, prefix, presynaptic_population, postsynaptic_population, synapse_id, connection_probability, delay=0, weight=1)¶ Add a projection between presynaptic_population and postsynaptic_population with probability of connection between each pre & post pair of cells given by connection_probability. Attributes:
- net
- reference to the network object previously created
- prefix
- prefix to use in the id of the projection
- presynaptic_population
- presynaptic population e.g. added via add_population_in_rectangular_region()
- postsynaptic_population
- postsynaptic population e.g. added via add_population_in_rectangular_region()
- synapse_id
- id of synapse previously added, e.g. added with add_exp_two_syn()
- connection_probability
- For each pre syn cell i and post syn cell j, where i!=j, the chance they will be connected is given by this
- delay
- optional delay for each connection, default 0 ms
- weight
- optional weight for each connection, default 1
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opencortex.core.add_pulse_generator(nml_doc, id, delay, duration, amplitude)¶ Adds a <pulseGenerator> element to the document. See the definition of the behaviour of this here: https://www.neuroml.org/NeuroML2CoreTypes/Inputs.html#pulseGenerator
Returns the class created.
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opencortex.core.add_single_cell_population(net, pop_id, cell_id, x=0, y=0, z=0, color=None)¶ Add a population with id pop_id containing a single instance of cell cell_id. Optionally specify (x,`y`,`z`) and the population color.
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opencortex.core.add_spike_source_poisson(nml_doc, id, start, duration, rate)¶ Adds a <SpikeSourcePoisson> element to the document. See the definition of the behaviour of this here: https://www.neuroml.org/NeuroML2CoreTypes/PyNN.html#SpikeSourcePoisson
Returns the class created.
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opencortex.core.add_targeted_electrical_projection(nml_doc, net, prefix, presynaptic_population, postsynaptic_population, targeting_mode, synapse_list, number_conns_per_cell, pre_segment_group, post_segment_group)¶ Adds (electrical, gap junction mediated) projection from presynaptic_population to postsynaptic_population, specifically limiting connections presynaptically to pre_segment_group and postsynaptically to post_segment_group.
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opencortex.core.add_targeted_inputs_to_population(net, id, population, input_comp_id, segment_group, number_per_cell=1, all_cells=False, only_cells=None, weights=1)¶ Add current input to the specified population. Attributes:
- net
- reference to the network object previously created
- id
- id of the <inputList> to be created
- population
- the <population> to be targeted
- input_comp_id
- id of the component to be used for the input (e.g. added with add_pulse_generator())
- segment_group
- which segment group on the target cells to limit input locations to
- number_per_cell
- How many inputs to apply to each cell of the population. Default 1
- all_cells
- whether to target all cells. Default False
- only_cells
- which specific cells to target. List of ids. Default None
- weights
- Either a scalar value (all weights set to this for all inputs), or a function to pick a (random) value per input. Default 1
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opencortex.core.add_targeted_projection(net, prefix, presynaptic_population, postsynaptic_population, targeting_mode, synapse_list, number_conns_per_cell, pre_segment_group=None, post_segment_group=None, delays_dict=None, weights_dict=None)¶ Adds (chemical, event based) projection from presynaptic_population to postsynaptic_population, specifically limiting connections presynaptically to pre_segment_group and postsynaptically to post_segment_group.
- net
- reference to the network object previously created
- prefix
- prefix to use in the id of the projection
- presynaptic_population
- presynaptic population e.g. added via add_population_in_rectangular_region()
- postsynaptic_population
- postsynaptic population e.g. added via add_population_in_rectangular_region()
- targeting_mode
- a string that specifies the targeting mode: ‘convergent’ or ‘divergent’
- synapse_list
- the list of synapse ids that correspond to the individual receptor components on the physical synapse, e.g. the first element is the id of the AMPA synapse and the second element is the id of the NMDA synapse; these synapse components will be mapped onto the same location of the target segment
- number_conns_per_cell
- number of connections to make on each cell in the postsynaptic population (when targeting_mode=’convergent’) or from each cell in the presynaptic population (when targeting_mode=’divergent’)
- pre_segment_group
- which segment_group to target connennections from on the presynaptic population, e.g. axon_group. This can be left out or set to None if the presynaptic component has no morphology
- post_segment_group
- which segment_group to target connennections from on the postsynaptic population, e.g. dendrite_group. This can be left out or set to None if the postsynaptic component has no morphology
- delays_dict
- optional dictionary that specifies the delays (in ms) for individual synapse components, e.g. {‘NMDA’:5.0} or {‘AMPA’:3.0,’NMDA’:5}
- weights_dict
- optional dictionary that specifies the weights for individual synapse components, e.g. {‘NMDA’:1} or {‘NMDA’:1,’AMPA’:2}
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opencortex.core.add_transient_poisson_firing_synapse(nml_doc, id, average_rate, delay, duration, synapse_id)¶ Adds a <transientPoissonFiringSynapse> element to the document. See the definition of the behaviour of this here: https://www.neuroml.org/NeuroML2CoreTypes/Inputs.html#transientPoissonFiringSynapse
Returns the class created.
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opencortex.core.add_voltage_clamp_triple(nml_doc, id, delay, duration, conditioning_voltage, testing_voltage, return_voltage, simple_series_resistance, active='1')¶ Adds a <voltageClampTriple> element to the document. See the definition of the behaviour of this here: https://github.com/NeuroML/NeuroML2/blob/development/Schemas/NeuroML2/NeuroML_v2beta5.xsd
Returns the class created.
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opencortex.core.generate_lems_simulation(nml_doc, network, nml_file_name, duration, dt, target_dir='.', include_extra_lems_files=[], gen_plots_for_all_v=True, plot_all_segments=False, gen_plots_for_quantities={}, gen_plots_for_only_populations=[], gen_saves_for_all_v=True, save_all_segments=False, gen_saves_for_only_populations=[], gen_saves_for_quantities={}, gen_spike_saves_for_all_somas=False, gen_spike_saves_for_only_populations=[], gen_spike_saves_for_cells={}, spike_time_format='ID_TIME', report_file_name=None, lems_file_name=None, lems_file_generate_seed=12345, simulation_seed=12345, verbose=False)¶ Generate a LEMS simulation file with which to run simulations of the network. Generated LEMS files can be run with jNeuroML (or converted to simulator specific formats, e.g. NEURON, and run)
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opencortex.core.generate_network(reference, network_seed=1234, temperature='32degC')¶ Generate a network which will contain populations, projections, etc. Arguments:
- reference
- the reference to use as the id for the network
- network_seed
- optional, will be used for random elements of the network, e.g. placement of cells in 3D
- temperature
- optional, will be specified in network and used in temperature dependent elements, e.g. ion channels with Q10. Default: 32degC
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opencortex.core.include_cell_prototype(nml_doc, cell_nml2_path)¶ Add a NeuroML2 file containing a cell definition
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opencortex.core.include_neuroml2_cell(nml_doc, cell_nml2_path, cell_id, channels_also=True)¶ Add a cell with id cell_id which is in cell_nml2_path to the build document, along with all its channels (if channels_also==True)
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opencortex.core.include_neuroml2_cell_and_channels(nml_doc, cell_nml2_path, cell_id)¶ DEPRECATED TODO: remove, due to include_neuroml2_cell Add a cell with id cell_id which is in cell_nml2_path to the build document, along with all its channels
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opencortex.core.include_neuroml2_file(nml_doc, nml2_file_path)¶ Add a NeuroML2 file containing definitions of elements which can be used in the network
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opencortex.core.include_opencortex_cell(nml_doc, reference)¶ Include a cell from the standard set of NeuroML2 cells included with OpenCortex. See https://github.com/OpenSourceBrain/OpenCortex/tree/master/NeuroML2/prototypes.
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opencortex.core.save_network(nml_doc, nml_file_name, validate=True, format='xml', max_memory=None, target_dir='./', use_subfolder=True)¶ Save the contents of the built NeuroML document, including the network to the file specified by nml_file_name, optionally specifying the target_dir
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opencortex.core.simulate_network(lems_file_name, simulator, max_memory='400M', nogui=True, load_saved_data=False, reload_events=False, plot=False, verbose=True, num_processors=1)¶ Run a simulation of the LEMS file lems_file_name using target platform simulator
