bmtk.simulator.bionet package

Subpackages

Submodules

bmtk.simulator.bionet.biocell module

class bmtk.simulator.bionet.biocell.BioCell(node, population_name, bionetwork)[source]

Bases: Cell

Implemntation of a morphologically and biophysically detailed type cell.

build_morphology()[source]
connections()[source]
get_connection_info()[source]
get_im()[source]

Gather membrane currents from PtrVector into imVec (does not need a loop!)

get_section(sec_id)[source]
get_sections()[source]
get_sections_id()[source]
init_connections()[source]
property morphology

The actual Morphology object instanstiation

property morphology_file

Value that’s stored in SONATA morphology column

print_synapses()[source]
property seg_coords

Coordinates for segments/sections of the morphology, need to make public for ecp, xstim, and other functionality that needs to compute the soma/dendritic coordinates of each cell

set_e_extracellular(vext)[source]
set_im_ptr()[source]

Set PtrVector to point to the _ref_i_membrane_ parameter

set_ptr2e_extracellular()[source]
set_sec_array()[source]

Arrange sections in an array to be access by index

set_spike_detector(spike_threshold)[source]
set_syn_connection(edge_prop, src_node, stim=None, gj_ids=None)[source]
setup_ecp()[source]
setup_xstim(set_nrn_mechanism=True)[source]
class bmtk.simulator.bionet.biocell.BioCellSpontSyn(node, population_name, bionetwork)[source]

Bases: BioCell

Special class that allows certain synapses to spontaneously fire (without spiking) at a specific time.

class bmtk.simulator.bionet.biocell.ConnectionStruct(edge_prop, src_node, syn, connector, is_virtual=False, is_gap_junc=False)[source]

Bases: object

property is_gap_junc
property is_virtual
property source_node
property syn_weight

bmtk.simulator.bionet.bionetwork module

class bmtk.simulator.bionet.bionetwork.BioNetwork[source]

Bases: SimNetwork

add_nodes(node_population)[source]
add_spike_trains(spike_trains, node_set)[source]
build_nodes()[source]
build_recurrent_edges()[source]
build_replay_inputs(spike_trains, edges_path, edge_types_path, source_node_set, target_node_set)[source]
cell_type_maps(model_type)[source]
find_edges(source_nodes=None, target_nodes=None)[source]
get_cell_gid(gid)[source]
get_cell_node_id(population, node_id)[source]
get_disconnected_cell(population, node_id, spike_trains)[source]
get_gj_id(network, src_nid, trg_nid, source_gap)[source]

Returns the gap junction id for the given nodes on a given network.

Parameters:

source_gap – whether to return the id of the gap junction on the source node or the target node.

get_local_cells()[source]
get_node_id(population, node_id)[source]
get_virtual_cells(population, node_id, spike_trains)[source]
property gid_pool
property local_gids
model_type_col = 'model_type'
property py_function_caches
set_spont_syn_activity(precell_filter, timestamps)[source]

bmtk.simulator.bionet.biosimulator module

class bmtk.simulator.bionet.biosimulator.BioSimulator(network, dt, tstop, v_init, celsius, nsteps_block, start_from_state=False)[source]

Bases: Simulator

Includes methods to run and control the simulation

add_mod(module)[source]
attach_se_voltage_clamp(amplitudes, durations, gids, rs)[source]
property biophysical_gids
property cell_var_output
property cell_variables
property celsius
property dt
classmethod from_config(config, network, set_recordings=True)[source]
property h
property local_gids
property n_steps
property nsteps_block
post_fadvance()[source]

Runs after every execution of fadvance (see advance.hoc) Called after every time step to perform computation and save data to memory block or to disk. The initial condition tstep=0 is not being saved

report_load_balance()[source]
run()[source]

Run the simulation: if beginning from a blank state, then will use h.run(), if continuing from the saved state, then will use h.continuerun()

set_spikes_recording()[source]
simulation_time(units='ms')[source]
property spikes_table
property tstop
property v_init

bmtk.simulator.bionet.cell module

class bmtk.simulator.bionet.cell.Cell(node, population_name, network=None)[source]

Bases: object

A abstract base class for any cell object.

A base class for implementation of a cell-type objects like biophysical cells, LIF cells, etc. Do not instantiate a Cell object directly. Cell classes act as wrapper around HOC cell object with extra functionality for setting positions, synapses, and other parameters depending on the desired cell class.

get_connection_info()[source]
property gid
property group_id
property hobj
init_connections()[source]
property netcons
property network_name
property node
property node_id
scale_weights(factor)[source]
set_soma_position()[source]
set_syn_connections(edge_prop, src_node, stim=None)[source]
property soma_position

bmtk.simulator.bionet.config module

class bmtk.simulator.bionet.config.Config(*args, **kwargs)[source]

Bases: SimulationConfig

build_env()[source]

Creates the folder(s) set in ‘output’ section, sets up logging and copies over the configuration

create_output_dir()[source]
load_nrn_modules()[source]

bmtk.simulator.bionet.gids module

class bmtk.simulator.bionet.gids.GidPool[source]

Bases: object

add_pool(name, n_nodes)[source]
get_gid(name, node_id)[source]
get_pool_id(gid)[source]
class bmtk.simulator.bionet.gids.PopulationID(node_id, population)

Bases: tuple

node_id

Alias for field number 0

population

Alias for field number 1

bmtk.simulator.bionet.iclamp module

class bmtk.simulator.bionet.iclamp.FileIClamp(amplitudes, dt)[source]

Bases: object

attach_current(cell)[source]
class bmtk.simulator.bionet.iclamp.IClamp(amplitude, delay, duration, section_name='soma', section_index=0, section_dist=0.5)[source]

Bases: object

attach_current(cell)[source]

bmtk.simulator.bionet.io_tools module

class bmtk.simulator.bionet.io_tools.NEURONIOUtils[source]

Bases: IOUtils

barrier()[source]

MPI Barrier call

bmtk.simulator.bionet.morphology module

class bmtk.simulator.bionet.morphology.Morphology(hobj, rng_seed=None, swc_path=None)[source]

Bases: object

choose_sections(section_names, distance_range, n_sections=1, cache=True)[source]

Similar to find_sections, but will only N=n_section number of sections_ids/x values randomly selected (may return less if there aren’t as many sections

Parameters:

  • section_names – ‘soma’, ‘dend’, ‘apic’, ‘axon’

  • distance_range – [float, float]: distance range of sections from the soma, in um.

  • n_sections – int: maximum number of sections to select

  • cache – caches the segments + probs so that next time the same section_names/distance_range values are called it will return the same values without recalculating. default True.

Returns:

[int], [float]: A list of all section_ids and a list of all segment_x values (as defined by NEURON) that meet the given critera.

find_sections(section_names, distance_range, cache=True)[source]

Retrieves a list of sections ids and section x’s given a section name/type (eg axon, soma, apic, dend) and the distance from the soma.

Parameters:

  • section_names – A list of sections to target, ‘soma’, ‘dend’, ‘apic’, ‘axon’

  • distance_range – [float, float]: distance range of sections from the soma, in um.

  • cache – caches the segments + probs so that next time the same section_names/distance_range values are called it will return the same values without recalculating. default True.

Returns:

[float], [float]: A list of all section_ids and a list of all segment_x values (as defined by NEURON) that meet the given critera.

get_coords(sec_id, sec_x)[source]
get_section(section_idx)[source]
get_swc_id(sec_id, sec_x)[source]
classmethod load(hobj=None, morphology_file=None, rng_seed=None, cache_seg_props=True)[source]

Factory method, perfered way to create a Morphology object

Parameters:

  • hobj

  • morphology_file

  • rng_seed

  • cache_seg_props

Returns:

move_and_rotate(soma_coords=None, rotation_angles=None, inplace=False)[source]
property n_sections
property nseg
sec_type_swc = {'apic': 4, 'apical': 4, 'axon': 2, 'axonal': 2, 'basal': 3, 'dend': 3, 'soma': 1, 'somatic': 1}
property sections
property seg_coords

Get the coordinates of all segments of the cells. Each segment is defined by three values, the beginning of the segment (seg_coords.p0), the middle of the segment(seg_coords.p05), and the end (seg_coords.p1).

property seg_props
property segments
set_segment_dl(dl)[source]

Define number of segments in a cell

property soma_position
property swc_map
bmtk.simulator.bionet.morphology.rotation_matrix(axis, theta)[source]

Return the rotation matrix associated with counterclockwise rotation about the given axis by theta radians.

bmtk.simulator.bionet.nml_reader module

class bmtk.simulator.bionet.nml_reader.ChannelDensity(nml_element)[source]

Bases: NMLElement

static element_tag()[source]
class bmtk.simulator.bionet.nml_reader.ChannelDensityNernst(nml_element)[source]

Bases: ChannelDensity

static element_tag()[source]
class bmtk.simulator.bionet.nml_reader.ConcentrationModel(nml_element)[source]

Bases: NMLElement

static element_tag()[source]
class bmtk.simulator.bionet.nml_reader.NMLElement(nml_element)[source]

Bases: object

static element_tag()[source]
class bmtk.simulator.bionet.nml_reader.NMLTree(nml_path)[source]

Bases: object

static common_name(elem)[source]
element_registry = {'{http://www.neuroml.org/schema/neuroml2}channelDensity': <class 'bmtk.simulator.bionet.nml_reader.ChannelDensity'>, '{http://www.neuroml.org/schema/neuroml2}channelDensityNernst': <class 'bmtk.simulator.bionet.nml_reader.ChannelDensityNernst'>, '{http://www.neuroml.org/schema/neuroml2}concentrationModel': <class 'bmtk.simulator.bionet.nml_reader.ConcentrationModel'>, '{http://www.neuroml.org/schema/neuroml2}resistivity': <class 'bmtk.simulator.bionet.nml_reader.Resistivity'>, '{http://www.neuroml.org/schema/neuroml2}specificCapacitance': <class 'bmtk.simulator.bionet.nml_reader.SpecificCapacitance'>}
nml_ns = '{http://www.neuroml.org/schema/neuroml2}'
classmethod ns_name(name)[source]
static parse_value(value)[source]
classmethod register_module(element_cls)[source]
class bmtk.simulator.bionet.nml_reader.Resistivity(nml_element)[source]

Bases: NMLElement

static element_tag()[source]
class bmtk.simulator.bionet.nml_reader.SpecificCapacitance(nml_element)[source]

Bases: NMLElement

static element_tag()[source]

bmtk.simulator.bionet.nrn module

bmtk.simulator.bionet.nrn.clear_gids()[source]
bmtk.simulator.bionet.nrn.load_neuron_modules(mechanisms_dir, templates_dir, default_templates=True)[source]
Parameters:

  • mechanisms_dir

  • templates_dir

  • default_templates

bmtk.simulator.bionet.nrn.load_templates(template_dir)[source]

Load all templates to be available in the hoc namespace for instantiating cells

bmtk.simulator.bionet.nrn.quit_execution()[source]
bmtk.simulator.bionet.nrn.reset()[source]

bmtk.simulator.bionet.pointprocesscell module

class bmtk.simulator.bionet.pointprocesscell.ConnectionStruct(edge_prop, src_node, nc, is_virtual=False)[source]

Bases: object

property is_virtual
property source_node
property syn_weight
class bmtk.simulator.bionet.pointprocesscell.PointProcessCell(node, population_name, bionetwork)[source]

Bases: Cell

Implimentation of a Leaky Integrate-and-file neuron type cell.

connections()[source]
get_connection_info()[source]
print_synapses()[source]
set_im_ptr()[source]
set_spike_detector()[source]
set_syn_connection(edge_prop, src_node, stim=None)[source]
class bmtk.simulator.bionet.pointprocesscell.PointProcessCellSpontSyns(node, population_name, bionetwork)[source]

Bases: PointProcessCell

Special class that allows certain synapses to spontaneously fire (without spiking) at a specific time.

set_syn_connection(edge_prop, src_node, stim=None)[source]

bmtk.simulator.bionet.pointsomacell module

class bmtk.simulator.bionet.pointsomacell.PointSomaCell[source]

Bases: Cell

Used to represent single compartment cells with neural mechanisms

bmtk.simulator.bionet.pyfunction_cache module

bmtk.simulator.bionet.seclamp module

class bmtk.simulator.bionet.seclamp.SEClamp(amplitudes, durations, rs=None)[source]

Bases: object

attach_current(cell)[source]

bmtk.simulator.bionet.sonata_adaptors module

class bmtk.simulator.bionet.sonata_adaptors.BioEdge(sonata_edge, edge_adaptor)[source]

Bases: SonataBaseEdge

property afferent_section_id
property afferent_section_pos
load_synapses(section_x, section_id)[source]
class bmtk.simulator.bionet.sonata_adaptors.BioEdgeAdaptor(network)[source]

Bases: EdgeAdaptor

get_edge(sonata_edge)[source]
classmethod patch_adaptor(adaptor, edge_group)[source]
class bmtk.simulator.bionet.sonata_adaptors.BioNode(node, prop_adaptor)[source]

Bases: SonataBaseNode

load_cell()[source]
property morphology_file
property position
property rotation_angle_xaxis
property rotation_angle_yaxis
property rotation_angle_zaxis
property rotations
property rotations_quaternion
class bmtk.simulator.bionet.sonata_adaptors.BioNodeAdaptor(network)[source]

Bases: NodeAdaptor

get_node(sonata_node)[source]
classmethod patch_adaptor(adaptor, node_group, network)[source]
bmtk.simulator.bionet.sonata_adaptors.aff_sec_id(self, edge)[source]
bmtk.simulator.bionet.sonata_adaptors.aff_sec_id_old(self, edge)[source]
bmtk.simulator.bionet.sonata_adaptors.aff_sec_pos(self, edge)[source]
bmtk.simulator.bionet.sonata_adaptors.aff_sec_pos_old(self, edge)[source]
bmtk.simulator.bionet.sonata_adaptors.position(self, node)[source]
bmtk.simulator.bionet.sonata_adaptors.position_x(self, node)[source]
bmtk.simulator.bionet.sonata_adaptors.position_xy(self, node)[source]
bmtk.simulator.bionet.sonata_adaptors.position_xyz(self, node)[source]
bmtk.simulator.bionet.sonata_adaptors.positions(self, node)[source]
bmtk.simulator.bionet.sonata_adaptors.positions_default(self, node)[source]
bmtk.simulator.bionet.sonata_adaptors.ret_none_function(self, edge)[source]
bmtk.simulator.bionet.sonata_adaptors.rotation_angle_default(self, node)[source]
bmtk.simulator.bionet.sonata_adaptors.rotation_angle_x(self, node)[source]
bmtk.simulator.bionet.sonata_adaptors.rotation_angle_y(self, node)[source]
bmtk.simulator.bionet.sonata_adaptors.rotation_angle_z(self, node)[source]
bmtk.simulator.bionet.sonata_adaptors.rotations(self, node)[source]
bmtk.simulator.bionet.sonata_adaptors.value_none(self, node)[source]

bmtk.simulator.bionet.utils module

bmtk.simulator.bionet.utils.edge_converter_csv(output_dir, csv_file)[source]

urrently being used by BioNetwork.write_connections(), need to refactor

Parameters:

  • output_dir

  • csv_file

Returns:

bmtk.simulator.bionet.utils.rotation_matrix(axis, theta)[source]

Return the rotation matrix associated with counterclockwise rotation about the given axis by theta radians.

bmtk.simulator.bionet.virtualcell module

class bmtk.simulator.bionet.virtualcell.VirtualCell(node, population, spike_train_dataset)[source]

Bases: object

Representation of a Virtual/External node

property hobj
property node_id
set_stim(stim_prop, spike_train)[source]

Gets the spike trains for each individual cell.

Module contents