hnn_core.Network¶

class hnn_core.Network(params, add_drives_from_params=False, legacy_mode=False, mesh_shape=(10, 10))[source]¶

The Network class.

Parameters:

paramsdict: The parameters to use for constructing the network.
add_drives_from_paramsbool: 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. Default: False
legacy_modebool: 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.

Notes

net = jones_2009_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.

Attributes:

cell_typesdict: Dictionary containing names of real cell types in the network (e.g. ‘L2_basket’) as keys and corresponding Cell instances as values. The Cell instance associated with a given key is used as a template for the other cells of its type in the population.
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: 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_tonic_bias`(*[, cell_type, t0, tstop])	Attaches parameters of tonic bias input for given cell types
`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
`gid_to_type`(gid)	Reverse lookup of gid to type.
`plot_cells`([ax, show])	Plot the cells using Network.pos_dict.
`set_cell_positions`(*[, inplane_distance, ...])	Set relative positions of cells arranged in a square grid
`write_configuration`(fname[, overwrite])	Writes network configuration to a json file.

to_dict

__repr__()[source]¶: 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)[source]¶

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, allow_autapses=True, probability=1.0, conn_seed=None)[source]¶

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.
allow_autapsesbool: If True, allow connecting neuron to itself.
probabilityfloat: Probability of connection between any src-target pair. Defaults to 1.0 producing an all-to-all pattern.
conn_seedint: 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)[source]¶

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)[source]¶

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 leading 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)[source]¶

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_tonic_bias(*, cell_type=None, amplitude, t0=0, tstop=None)[source]¶

Attaches parameters of tonic bias input for given cell types

Parameters:

cell_typesstr | None: The name of the cell type to add a tonic input. When supplied, a float value must be provided with the amplitude keyword. Valid inputs are those listed in net.cell_types.
amplitude: dict | float: A dictionary of cell type keys (str) to amplitude values (float). Valid inputs for cell types are those listed in net.cell_types. If cell_types is not None, amplitude should be a float indicating the amplitude of the tonic input for the specified cell type.
t0float: The start time of tonic input (in ms). Default: 0 (beginning of simulation). This value will be applied to all the tonic biases if multiple are specified with the amplitude keyword.
tstopfloat: The end time of tonic input (in ms). Default: end of simulation. This value will be applied to all the tonic biases if multiple are specified with the amplitude keyword.

clear_connectivity()[source]¶: Remove all connections defined in Network.connectivity

clear_drives()[source]¶: Remove all drives defined in Network.connectivity

copy()[source]¶

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.

gid_to_type(gid)[source]¶: Reverse lookup of gid to type.

plot_cells(ax=None, show=True)[source]¶

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.

Returns:

figinstance of matplotlib Figure: The matplotlib figure handle.

set_cell_positions(*, inplane_distance=None, layer_separation=None)[source]¶

Set relative positions of cells arranged in a square grid

Note that it is possible to change only a subset of the parameters (the default value of each is None, which implies no change).

Parameters:

inplane_distancefloat: 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: 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)[source]¶

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: