hnn_core.Network

class hnn_core.Network(params, add_drives_from_params=False, legacy_mode=False, mesh_shape=(10, 10), pos_dict=None, cell_types=None)

The Network class.

Parameters:

paramsdict

The parameters to use for constructing the network.

add_drives_from_paramsbool, default=False

If True, add drives as defined in the params-dict. NB this is mainly for backward-compatibility with HNN GUI, and will be deprecated in a future release.

legacy_modebool, default=False

Set to True by default to enable matching HNN GUI output when drives are added suitably. Will be deprecated in a future release.

mesh_shapetuple of int (default: (10, 10))

Defines the (n_x, n_y) shape of the grid of pyramidal cells.

pos_dictdict of list of tuple (x, y, z), optional

Dictionary containing the coordinate positions of all cells. Keys are ‘L2_pyramidal’, ‘L5_pyramidal’, ‘L2_basket’, ‘L5_basket’, or any external drive name. If you specify “pos_dict”, you MUST also specify your own “cell_types” argument (see below).

cell_typesdict of dict of (Cell | dict), optional

Dictionary containing names of real cell types in the network (e.g. ‘L2_basket’) as keys and child-dictionaries describing the cell type. If you specify “cell_types”, you MUST also specify your own “pos_dict” argument (see above). The child-dictionary contains two keys: “cell_object” and “cell_metadata”. The value of “cell_object” is the corresponding Cell instance of the cell type being described, and this instance is used as a template for the other cells of its type in the population. The value of “cell_metadata” is a dictionary containing several key-values pairs that describe different aspects of the cell type, described below:

• “morpho_type” : either “basket” or “pyramidal”

• “electro_type” : either “inhibitory” or “excitatory”

• “layer” : either “2” or “5”

• “measure_dipole” : either True or False

• “reference”: optional

Attributes:

cell_typesdict of dict of (Cell | dict)

Dictionary containing names of real cell types in the network (e.g. ‘L2_basket’) as keys and child-dictionaries describing the cell type. The child-dictionary contains two keys: “cell_object” and “cell_metadata”. The value of “cell_object” is the corresponding Cell instance of the cell type being described, and this instance is used as a template for the other cells of its type in the population. The value of “cell_metadata” is a dictionary containing several key-values pairs that describe different aspects of the cell type, described below:

• “morpho_type” : either “basket” or “pyramidal”

• “electro_type” : either “inhibitory” or “excitatory”

• “layer” : either “2” or “5”

• “measure_dipole” : either True or False

• “reference”: optional

gid_rangesdict

A dictionary of unique identifiers of each real and artificial cell in the network. Every cell type is represented by a key read from cell_types, followed by keys read from external_drives. The value of each key is a range of ints, one for each cell in given category. Examples: ‘L2_basket’: range(0, 270), ‘evdist1’: range(272, 542), etc

pos_dictdict of list of tuple (x, y, z)

Dictionary containing the coordinate positions of all cells. Keys are ‘L2_pyramidal’, ‘L5_pyramidal’, ‘L2_basket’, ‘L5_basket’, or any external drive name.

cell_responseCellResponse

An instance of the CellResponse object.

external_drivesdict (keys: drive names) of dict (keys: parameters)

The external driving inputs to the network. Drives are added by defining their spike-time dynamics, and their connectivity to the real cells of the network. Event times are instantiated before simulation, and are stored under the 'events'-key (list of list; first index for trials, second for event time lists for each drive cell).

external_biasesdict of dict (bias parameters for each cell type)

The parameters of bias inputs to cell somata, e.g., tonic current clamp

connectivitylist of dict

List of dictionaries specifying each cell-cell and drive-cell connection

rec_arraysdict

Stores electrode position information and voltages recorded by them for extracellular potential measurements. Multiple electrode arrays may be defined as unique keys. The values of the dictionary are instances of hnn_core.extracellular.ExtracellularArray.

thresholdfloat

Firing threshold of all cells.

delayfloat

Synaptic delay in ms.

Methods

add_bursty_drive(name, *[, tstart, ...])	Add a bursty (rhythmic) external drive to all cells of the network
add_connection(src_gids, target_gids, loc, ...)	Appends connections to connectivity list
add_electrode_array(name, electrode_pos, *)	Specify coordinates of electrode array for extracellular recording.
add_evoked_drive(name, *, mu, sigma, ...[, ...])	Add an 'evoked' external drive to the network
add_poisson_drive(name, *[, tstart, tstop, ...])	Add a Poisson-distributed external drive to the network
add_spike_train_drive(name, *, spike_data, ...)	Add an external drive from explicitly defined spike trains.
add_tonic_bias(amplitude[, bias_name, gid, ...])	Adds a tonic bias input to provided cell types and/or cells
clear_connectivity()	Remove all connections defined in Network.connectivity
clear_drives()	Remove all drives defined in Network.connectivity
copy()	Return a copy of the Network instance
filter_cell_types(**metadata_filters)	Filter cell types based on cell_metadata criteria
get_global_synaptic_gains()	Retrieve gain values for different celltype connections in the Network.
gid_to_type(gid)	Reverse lookup of gid to type.
plot_cells([ax, show, colors, markers])	Plot the cells using Network.pos_dict.
set_cell_positions([inplane_distance, ...])	Reset relative positions of cells arranged in a square grid (Deprecated)
set_global_synaptic_gains([e_e, e_i, i_e, ...])	Change the synaptic gains of the celltypes in the Network.
update_cell_positions([inplane_distance, ...])	Change relative positions of cells arranged in a square grid.
write_configuration(fname[, overwrite])	Writes network configuration to a json file.

to_dict

Notes

net = neymotin_2020_model(params) is the recommended path for creating a network. Instantiating the network as net = Network(params) will produce a network with no cell-to-cell connections. As such, connectivity information contained in params will be ignored.

__repr__()

Return repr(self).

add_bursty_drive(name, *, tstart=0, tstart_std=0, tstop=None, location, burst_rate, burst_std=0, numspikes=2, spike_isi=10, n_drive_cells=1, cell_specific=False, weights_ampa=None, weights_nmda=None, synaptic_delays=0.1, space_constant=100.0, probability=1.0, event_seed=2, conn_seed=3)

Add a bursty (rhythmic) external drive to all cells of the network

Parameters:

namestr

Unique name for the drive

tstartfloat

Start time of the burst trains (default: 0)

tstart_stdfloat

If greater than 0, randomize start time with standard deviation tstart_std (unit: ms). Effectively jitters start time across multiple trials.

tstopfloat

End time of burst trains (defaults to None: tstop is set to the end of the simulation)

locationstr

Target location of synapses. Must be an element of Cell.sect_loc such as ‘proximal’ or ‘distal’, which defines a group of sections, or an existing section such as ‘soma’ or ‘apical_tuft’ (defined in Cell.sections for all targeted cells). The parameter legacy_mode of the Network must be set to False to target specific sections.

burst_ratefloat

The mean rate at which cyclic bursts occur (unit: Hz)

burst_stdfloat

The standard deviation of the burst occurrence on each cycle (unit: ms). Default: 0 ms

numspikesint

The number of spikes in a burst. This is the spikes/burst parameter in the GUI. Default: 2 (doublet)

spike_isifloat

Time between spike events within a cycle (ISI). Default: 10 ms

n_drive_cellsint | ‘n_cells’

The number of drive cells that contribute an independently sampled burst at each cycle. If n_drive_cells=’n_cells’ and cell_specific=True, a drive cell gets assigned to each available simulated cell in the network with 1-to-1 connectivity. Otherwise (default: 1), drive cells are assigned with all-to-all connectivity and provide synchronous input to cells in the network.

cell_specificbool

Whether each artificial drive cell has 1-to-1 (True) or all-to-all (False, default) connection parameters. Note that 1-to-1 connectivity requires that n_drive_cells=’n_cells’, where ‘n_cells’ denotes the number of all available cells that this drive can target in the network.

weights_ampadict or None

Synaptic weights (in uS) of AMPA receptors on each targeted cell type (dict keys). Cell types omitted from the dict are set to zero.

weights_nmdadict or None

Synaptic weights (in uS) of NMDA receptors on each targeted cell type (dict keys). Cell types omitted from the dict are set to zero.

synaptic_delaysdict or float

Synaptic delay (in ms) at the column origin, dispersed laterally as a function of the space_constant. If float, applies to all target cell types. Use dict to create delay->cell mapping.

space_constantfloat

Describes lateral dispersion (from the column origin) of synaptic weights and delays within the simulated column. The constant is measured in the units of inplane_distance of Network. For example, for space_constant=3, the weights and delays are modulated by the factor exp(-(x / (3 * inplane_distance)) ** 2), where x is the physical distance (in um) between the connected cells in the xy plane.

probabilitydict or float (default: 1.0)

Probability of connection between any src-target pair. Use dict to create probability->cell mapping. If float, applies to all target cell types.

event_seedint

Optional initial seed for random number generator (default: 2). Used to generate event times for drive cells.

conn_seedint

Optional initial seed for random number generator (default: 3). Used to randomly remove connections when probability < 1.0.

add_connection(src_gids, target_gids, loc, receptor, weight, delay, lamtha, threshold=None, gain=1.0, allow_autapses=True, probability=1.0, conn_seed=None)

Appends connections to connectivity list

Parameters:

src_gidsstr | int | range | list of int

Identifier for source cells. Passing str arguments (‘evdist1’, ‘L2_pyramidal’, ‘L2_basket’, ‘L5_pyramidal’, ‘L5_basket’, etc.) is equivalent to passing a list of gids for the relevant cell type. source - target connections are made in an all-to-all pattern.

target_gidsstr | int | range | list of int

Identifier for targets of source cells. Passing str arguments (‘L2_pyramidal’, ‘L2_basket’, ‘L5_pyramidal’, ‘L5_basket’) is equivalent to passing a list of gids for the relevant cell type. source - target connections are made in an all-to-all pattern.

locstr

Target location of synapses. Must be an element of Cell.sect_loc such as ‘proximal’ or ‘distal’, which defines a group of sections, or an existing section such as ‘soma’ or ‘apical_tuft’ (defined in Cell.sections for all targeted cells). The parameter legacy_mode of the Network must be set to False to target specific sections.

receptorstr

Synaptic receptor of connection. Must be one of: ‘ampa’, ‘nmda’, ‘gabaa’, or ‘gabab’.

weightfloat

Synaptic weight on target cell.

delayfloat

Synaptic delay in ms.

lamthafloat

Space constant.

thresholdfloat, default=None

Firing threshold of cells for connection. If None (the default), inherit the threshold from the Network object.

gainfloat, default=1.0

Multiplicative factor for synaptic weight.

allow_autapsesbool, default=True

If True, allow connecting neuron to itself.

probabilityfloat, default=1.0

Probability of connection between any src-target pair. Defaults to 1.0 producing an all-to-all pattern.

conn_seedint, default=None

Optional initial seed for random number generator (default: None). Used to randomly remove connections when probability < 1.0.

Notes

Connections are stored in net.connectivity[idx]['gid_pairs'], a dictionary indexed by src gids with the format: {src_gid: [target_gids, …], …} where each src_gid indexes a list of all its targets.

add_electrode_array(name, electrode_pos, *, conductivity=0.3, method='psa', min_distance=0.5)

Specify coordinates of electrode array for extracellular recording.

Parameters:

namestr

Unique name of the array.

electrode_postuple | list of tuple

Coordinates specifying the position for extracellular electrodes in the form of (x, y, z) (in um).

conductivityfloat

Extracellular conductivity, in S/m, of the assumed infinite, homogeneous volume conductor that the cell and electrode are in.

methodstr

Approximation to use. 'psa' (point source approximation) treats each segment junction as a point extracellular current source. 'lsa' (line source approximation) treats each segment as a line source of current, which extends from the previous to the next segment center point: /—x—/, where x is the current segment flanked by /.

min_distancefloat (default: 0.5; unit: um)

To avoid numerical errors in calculating potentials, apply a minimum distance limit between the electrode contacts and the active neuronal membrane elements that act as sources of current. The default value of 0.5 um corresponds to 1 um diameter dendrites.

add_evoked_drive(name, *, mu, sigma, numspikes, location, n_drive_cells='n_cells', cell_specific=True, weights_ampa=None, weights_nmda=None, space_constant=3.0, synaptic_delays=0.1, probability=1.0, event_seed=2, conn_seed=3)

Add an ‘evoked’ external drive to the network

Parameters:

namestr

Unique name for the drive

mufloat

Mean of Gaussian event time distribution

sigmafloat

Standard deviation of event time distribution

numspikesint

Number of spikes at each target cell

locationstr

Target location of synapses. Must be an element of Cell.sect_loc such as ‘proximal’ or ‘distal’, which defines a group of sections, or an existing section such as ‘soma’ or ‘apical_tuft’ (defined in Cell.sections for all targeted cells). The parameter legacy_mode of the Network must be set to False to target specific sections.

n_drive_cellsint | ‘n_cells’

The number of drive cells that each contribute an independently sampled synaptic spike to the network according to the Gaussian time distribution (mu, sigma). If n_drive_cells=’n_cells’ (default) and cell_specific=True, a drive cell gets assigned to each available simulated cell in the network with 1-to-1 connectivity. Otherwise, drive cells are assigned with all-to-all connectivity. If you wish to synchronize the timing of this evoked drive across the network in a given trial with one spike, set n_drive_cells=1 and cell_specific=False.

cell_specificbool

Whether each artificial drive cell has 1-to-1 (True, default) or all-to-all (False) connection parameters. Note that 1-to-1 connectivity requires that n_drive_cells=’n_cells’, where ‘n_cells’ denotes the number of all available cells that this drive can target in the network.

weights_ampadict or None

Synaptic weights (in uS) of AMPA receptors on each targeted cell type (dict keys). Cell types omitted from the dict are set to zero.

weights_nmdadict or None

Synaptic weights (in uS) of NMDA receptors on each targeted cell type (dict keys). Cell types omitted from the dict are set to zero.

synaptic_delaysdict or float

Synaptic delay (in ms) at the column origin, dispersed laterally as a function of the space_constant. If float, applies to all target cell types. Use dict to create delay->cell mapping.

space_constantfloat

Describes lateral dispersion (from the column origin) of synaptic weights and delays within the simulated column. The constant is measured in the units of inplane_distance of Network. For example, for space_constant=3, the weights are modulated by the factor exp(-(x / (3 * inplane_distance)) ** 2), where x is the physical distance (in um) between the connected cells in the xy plane (delays are modulated by the inverse of this factor).

probabilitydict or float (default: 1.0)

Probability of connection between any src-target pair. Use dict to create probability->cell mapping. If float, applies to all target cell types

event_seedint

Optional initial seed for random number generator (default: 2). Used to generate event times for drive cells. Not fixed across trials (see Notes)

conn_seedint

Optional initial seed for random number generator (default: 3). Used to randomly remove connections when probability < 1.0. Fixed across trials (see Notes)

Notes

Random seeding behavior across trials is different for event_seed and conn_seed (n_trials > 1 in simulate_dipole(…, n_trials):

• event_seed

  Across trials, the random seed is incremented such that the exact spike times are different

• conn_seed

  The random seed does not change across trials. This means for probability < 1.0, the random subset of gids targeted is the same.

add_poisson_drive(name, *, tstart=0, tstop=None, rate_constant, location, n_drive_cells='n_cells', cell_specific=True, weights_ampa=None, weights_nmda=None, space_constant=100.0, synaptic_delays=0.1, probability=1.0, event_seed=2, conn_seed=3)

Add a Poisson-distributed external drive to the network

Parameters:

namestr

Unique name for the drive

tstartfloat

Start time of Poisson-distributed spike train (default: 0)

tstopfloat

End time of the spike train (defaults to None: tstop is set to the end of the simulation)

rate_constantfloat or dict of floats

Rate constant (lambda > 0) of the renewal-process generating the samples. If a float is provided, the same rate constant is applied to each target cell type. Cell type-specific values may be provided as a dictionary, in which a key must be present for each cell type with non-zero AMPA or NMDA weights.

locationstr

Target location of synapses. Must be an element of Cell.sect_loc such as ‘proximal’ or ‘distal’, which defines a group of sections, or an existing section such as ‘soma’ or ‘apical_tuft’ (defined in Cell.sections for all targeted cells). The parameter legacy_mode of the Network must be set to False to target specific sections.

n_drive_cellsint | ‘n_cells’

The number of drive cells that each contribute an independently sampled synaptic spike to the network according to a Poisson process. If n_drive_cells=’n_cells’ (default) and cell_specific=True, a drive cell gets assigned to each available simulated cell in the network with 1-to-1 connectivity. Otherwise, drive cells are assigned with all-to-all connectivity. If you wish to synchronize the timing of Poisson drive across the network in a given trial, set n_drive_cells=1 and cell_specific=False.

cell_specificbool

Whether each artificial drive cell has 1-to-1 (True, default) or all-to-all (False) connection parameters. Note that 1-to-1 connectivity requires that n_drive_cells=’n_cells’, where ‘n_cells’ denotes the number of all available cells that this drive can target in the network.

weights_ampadict or None

Synaptic weights (in uS) of AMPA receptors on each targeted cell type (dict keys). Cell types omitted from the dict are set to zero.

weights_nmdadict or None

Synaptic weights (in uS) of NMDA receptors on each targeted cell type (dict keys). Cell types omitted from the dict are set to zero.

synaptic_delaysdict or float

Synaptic delay (in ms) at the column origin, dispersed laterally as a function of the space_constant. If float, applies to all target cell types. Use dict to create delay->cell mapping.

space_constantfloat

Describes lateral dispersion (from the column origin) of synaptic weights and delays within the simulated column. The constant is measured in the units of inplane_distance of Network. For example, for space_constant=3, the weights and delays are modulated by the factor exp(-(x / (3 * inplane_distance)) ** 2), where x is the physical distance (in um) between the connected cells in the xy plane.

probabilitydict or float (default: 1.0)

Probability of connection between any src-target pair. Use dict to create probability->cell mapping. If float, applies to all target cell types.

event_seedint

Optional initial seed for random number generator (default: 2). Used to generate event times for drive cells.

conn_seedint

Optional initial seed for random number generator (default: 3). Used to randomly remove connections when probability < 1.0.

add_spike_train_drive(name, *, spike_data, location, weights_ampa=None, weights_nmda=None, synaptic_delays=0.1, space_constant=3.0, probability=1.0, conn_seed=3)

Add an external drive from explicitly defined spike trains.

This method enables the target network to receive spike trains from a source network (e.g., another HNN simulation) or external data, driving activity in the target network’s cells.

Parameters:

namestr

Unique name for the drive (e.g., ‘drive_from_NetA’).

spike_datadict or list of tuple

Spike train data from the source network (or external source) in one of three formats:

• Format 1 (dictionary): Keys are unique identifiers (str) for source

cells and values are lists of spike times in milliseconds. The keys can be any string that helps identify the source. Example: ` {"NetA_L2_pyramidal_GID0": [10.2, 25.3], "NetA_L5_pyramidal_GID1": [15.1, 30.4]} `

• Format 2 (tuples): A list of (time, gid) tuples, where each tuple

contains a spike time (float, in ms) and a GID (int). The GIDs can be any integers that uniquely identify different source cells. Example: ` [(10.2, 0), (25.3, 1), (15.1, 0), (30.4, 1)] `

• Format 3: String path (or glob pattern) to spike files that can be loaded

with read_spikes(). Example: “path/to/spk_*.txt”

Note: The GIDs in both formats refer to the source cells in the originating network (or external data). These are arbitrary identifiers that will be remapped internally to sequential drive cell IDs (0 to n-1) in the target network. Different GIDs should be used for different source cells.

locationstr

Target location of synapses in the target network. Must be ‘proximal’, ‘distal’, or ‘soma’, or a specific section name (when legacy_mode=False).

weights_ampadict or None

Synaptic weights (in uS) of AMPA receptors for each targeted cell type (dict keys). Cell types omitted are set to zero.

weights_nmdadict or None

Synaptic weights (in uS) of NMDA receptors for each targeted cell type (dict keys). Cell types omitted are set to zero.

synaptic_delaysdict or float

Synaptic delay (in ms) at the column origin, dispersed laterally as a function of the space_constant. If float, applies to all target cell types. Use dict to create delay->cell mapping.

space_constantfloat

Lateral dispersion constant (in units of inplane_distance) for synaptic weights and delays within the target network. Default: 3.0

probabilityfloat or dict

Connection probability between source and target cells. Default: 1.0 (all-to-all).

conn_seedint

Optional seed for random number generator for connectivity (default: None).

cell_specificbool

If True, enables cell-specific connectivity (e.g., for 1-to-1 mapping). Default: False.

n_drive_cellsstr or int

Number of drive cells. Use ‘n_cells’ for 1-to-1 mapping with target cell types, or an integer for a fixed number. Default: None (inferred from spike_data).

add_tonic_bias(amplitude, bias_name='tonic', gid=None, section='soma', t0=0, tstop=None, cell_type=None)

Adds a tonic bias input to provided cell types and/or cells

Parameters:

amplitudedict | int | float

Required parameter. All amplitudes should be given in units of nA.

• If given as a dictionary, keys should be cell type names (as in Network.cell_types) and values should be the amplitude of the tonic input for that cell type as a float. An example of a valid dictionary input is:

  {'L2_pyramidal': 1.0, 'L5_pyramidal': 2.0}

• If given as an int or float, you must also provide the gid argument. In this case, the amplitude is applied to all cells indicated by the gid argument.

bias_namestr, default=”tonic”

The name of the bias.

gidint | list | dict, optional

Optionally specify gid(s) of cells to which the tonic bias should be applied. This must be specified if amplitude is an int or float. May be given as:

• a single gid (int) or a list of gids (list of int). The tonic bias is connected to the provided gids, regardless of which cell type they belong to.

• a dictionary mapping cell type to gid(s). If given as a dictionary, keys should be cell type names (as in Network.cell_types). Values should be a single gid (int), a list of gids (list of int), or the string 'all'. Each gid must belong to the cell type it is mapped to or else an error will be raised. Examples of valid dictionary inputs are:

  {'L5_pyramidal': 35}

  {'L2_pyramidal': 5, 'L5_pyramidal': 35}

  {'L2_pyramidal': [5, 6], 'L5_pyramidal': 35, 'L5_basket': 'all'}

sectionstr, default=”soma”

Name of cell section the bias should be applied to. See Network.cell_types[cell_type].sections.keys()

t0float, default=0

The start time of tonic input (in ms), defaulting to the beginning of simulation. This value will be applied to all the tonic biases created by this function call, regardless of if multiple cell types are specified using either amplitude or gid.

tstopfloat, optional

The end time of tonic input (in ms), defaulting to the end of the simulation. This value will be applied to all the tonic biases created by this function call, regardless of if multiple cell types are specified using either amplitude or gid.

cell_typestr, optional

DEPRECATED. The name of the cell type to add a tonic input. Valid inputs are those listed in Network.cell_types. When supplied, the amplitude keyword must be provided as a float. This argument will be removed in future releases.

Examples

Apply the same amplitude to all cells of two cell types:

>>> net.add_tonic_bias(amplitude={'L2_pyramidal': 1.0, 'L5_pyramidal': 2.0})

Apply a single amplitude to a single cell, specified by providing that cell’s GID to gid as an int:

>>> net.add_tonic_bias(amplitude=1.0, gid=5)

Apply a single amplitude to several cells, specified by gid as a list of int, regardless of the cell type(s) they belong to (this will NOT check whether the gids belong to the same cell type):

>>> net.add_tonic_bias(amplitude=1.0, gid=[5, 6, 35])

Apply a single amplitude to several cells, specified by gid as a dictionary of per-cell-type GIDs (this WILL check whether the gids belong to the cell type they are mapped to):

>>> net.add_tonic_bias(
...     amplitude=1.0,
...     gid={'L2_pyramidal': [5, 6], 'L5_pyramidal': 35},
... )

Apply per-cell-type amplitudes, restricting each to specific gids using a dictionary for gid:

>>> net.add_tonic_bias(
...     amplitude={'L2_pyramidal': 1.0, 'L5_pyramidal': 2.0},
...     gid={'L2_pyramidal': [5, 6], 'L5_pyramidal': 35},
... )

Apply per-cell-type amplitudes, using the string 'all' in the gid dictionary to apply the bias to every cell of that type:

>>> net.add_tonic_bias(
...     amplitude={'L2_pyramidal': 1.0, 'L5_pyramidal': 2.0},
...     gid={'L2_pyramidal': 'all', 'L5_pyramidal': 35},
... )

clear_connectivity()

Remove all connections defined in Network.connectivity

clear_drives()

Remove all drives defined in Network.connectivity

copy()

Return a copy of the Network instance

The returned copy retains the intrinsic connectivity between cells, as well as those of any external drives or biases added to the network. The parameters of drive dynamics are also retained, but the instantiated events of the drives are cleared. This allows iterating over the values defining drive dynamics, without the need to re-define connectivity. Extracellular recording arrays are retained in the network, but cleared of existing data.

Returns:

net_copyinstance of Network

A copy of the instance with previous simulation results and events of external drives removed.

filter_cell_types(**metadata_filters)

Filter cell types based on cell_metadata criteria

get_global_synaptic_gains()

Retrieve gain values for different celltype connections in the Network.

This function identifies excitatory and inhibitory cells in the Network and retrieves the gain value for each type of synaptic connection:

• excitatory to excitatory (e_e)

• excitatory to inhibitory (e_i)

• inhibitory to excitatory (i_e)

• inhibitory to inhibitory (i_i)

The gain is assumed to be uniform across all instances of each connection type (for example, between AMPA and NMDA, and between L2_pyramidal->L2_pyramidal and L2_pyramidal->L5_pyramidal, etc.). Only the first connection’s gain value is used for each type.

This does not return the synaptic gains of external drives.

Returns:

valuesdict

A dictionary with the connection types (‘e_e’, ‘e_i’, ‘i_e’, ‘i_i’) as keys and their corresponding gain values.

gid_to_type(gid)

Reverse lookup of gid to type.

plot_cells(ax=None, show=True, colors=None, markers=None)

Plot the cells using Network.pos_dict.

Parameters:

axinstance of matplotlib Axes3D | None

An axis object from matplotlib. If None, a new figure is created.

showbool

If True, show the figure.

colorsdict | None

Dictionary mapping cell type names to colors. If None, colors are assigned automatically from the default color cycle.

markersdict | None

Dictionary mapping cell type names to markers. If None, markers are assigned based on morpho_type in cell metadata.

Returns:

figinstance of matplotlib Figure

The matplotlib figure handle.

set_cell_positions(inplane_distance=None, layer_separation=None)

Reset relative positions of cells arranged in a square grid (Deprecated)

This function is deprecated in favor of Network.update_cell_positions. Note that it is possible to change one of the parameters without changing the other.

Parameters:

inplane_distancefloat, optional

The in plane-distance (in um) between pyramidal cell somas in the square grid. Note that this parameter does not affect the amplitude of the dipole moment.

layer_separationfloat, optional

The separation of pyramidal cell soma layers 2/3 and 5. Note that this parameter does not affect the amplitude of the dipole moment.

set_global_synaptic_gains(e_e=None, e_i=None, i_e=None, i_i=None, copy=False)

Change the synaptic gains of the celltypes in the Network.

Parameters:

e_efloat, default=None

Synaptic gain of excitatory to excitatory connections

e_ifloat, default=None

Synaptic gain of excitatory to inhibitory connections

i_efloat, default=None

Synaptic gain of inhibitory to excitatory connections

i_ifloat, default=None

Synaptic gain of inhibitory to inhibitory connections

copybool, default=False

If True, returns a copy of the network. If False, the network is updated in place with a return of None.

Returns:

netinstance of Network

A copy of the instance with updated synaptic gains if copy=True.

Notes

Synaptic gains must be non-negative. The synaptic gains will only be updated if a float value is provided. If None is provided (the default), the synaptic gain will remain unchanged.

This does not change the synaptic gains of external drives.

update_cell_positions(inplane_distance=None, layer_separation=None)

Change relative positions of cells arranged in a square grid.

Note that it is possible to change one of the parameters without changing the other. You must pass at least one argument.

Parameters:

inplane_distancefloat, optional

The in plane-distance (in um) between pyramidal cell somas in the square grid. Note that this parameter does not affect the amplitude of the dipole moment.

layer_separationfloat, optional

The separation of pyramidal cell soma layers 2/3 and 5. Note that this parameter does not affect the amplitude of the dipole moment.

write_configuration(fname, overwrite=True)

Writes network configuration to a json file.

Writes network configurations as a hierarchical json similar to the Network object’s structure. Outputs recorded during simulation such as currents and voltages are not saved due to size.

Parameters:

netNetwork

hnn-core Network object

outputstr, Path, or StringIO

Path or buffer to write outputs

overwritebool

Overwrite file if it exists. Default: True

Returns:

None