API Documentation¶

Simulation (`hnn_core`):¶

`simulate_dipole`(net, tstop[, dt, n_trials, …])	Simulate a dipole given the experiment parameters.
`Network`(params[, add_drives_from_params, …])	The Network class.
`Cell`(name, pos, sections, synapses, …[, gid])	Create a cell object.
`CellResponse`([spike_times, spike_gids, …])	The CellResponse class.
`pick_connection`(net[, src_gids, …])	Returns indices of connections that match search parameters.

Network Models (`hnn_core`):¶

`jones_2009_model`([params, …])	Instantiate the Jones et al. 2009 model.
`law_2021_model`()	Instantiate the beta modulated ERP network model.
`calcium_model`([params, add_drives_from_params])	Instantiate the Jones 2009 model with improved calcium dynamics.

Optimization (`hnn_core.optimization`):¶

optimize_evoked(params, target_dpl, initial_dpl)

Optimize drives to generate evoked response.

Dipole (`hnn_core.dipole`):¶

`Dipole`(times, data[, nave])	Dipole class.
`average_dipoles`(dpls)	Compute dipole averages over a list of Dipole objects.

ExtracellularArray (`hnn_core.extracellular`):¶

ExtracellularArray(positions, *[, …])

Class for recording extracellular potential fields with electrode array

Visualization (`hnn_core.viz`):¶

`plot_dipole`(dpl[, tmin, tmax, ax, layer, …])	Simple layer-specific plot function.
`plot_spikes_hist`(cell_response[, ax, …])	Plot the histogram of spiking activity across trials.
`plot_spikes_raster`(cell_response[, ax, show])	Plot the aggregate spiking activity according to cell type.
`plot_cells`(net[, ax, show])	Plot the cells using Network.pos_dict.
`plot_cell_morphology`(cell, ax[, show])	Plot the cell morphology.
`plot_psd`(dpl, *[, fmin, fmax, tmin, tmax, …])	Plot power spectral density (PSD) of dipole time course
`plot_tfr_morlet`(dpl, freqs, *[, n_cycles, …])	Plot Morlet time-frequency representation of dipole time course
`plot_cell_connectivity`(net, conn_idx[, …])	Plot synaptic weight of connections.
`plot_connectivity_matrix`(net, conn_idx[, …])	Plot connectivity matrix with color bar for synaptic weights

Parallel backends (`hnn_core.parallel_backends`):¶

`MPIBackend`([n_procs, mpi_cmd])	The MPIBackend class.
`JoblibBackend`([n_jobs])	The JoblibBackend class.

Input and Output:¶

`read_params`(params_fname)	Read param values from a file (.json or .param).
`read_dipole`(fname)	Read dipole values from a file and create a Dipole instance.
`read_spikes`(fname[, gid_ranges])	Read spiking activity from a collection of spike trial files.