hnn_core.CellResponse¶

class hnn_core.CellResponse(spike_times=None, spike_gids=None, spike_types=None, times=None, cell_type_names=None)[source]¶

The CellResponse class.

Parameters

spike_timeslist (n_trials,) of list (n_spikes,) of float, shape | None: Each element of the outer list is a trial. The inner list contains the time stamps of spikes.
spike_gidslist (n_trials,) of list (n_spikes,) of float, shape | None: Each element of the outer list is a trial. The inner list contains the cell IDs of neurons that spiked.
spike_typeslist (n_trials,) of list (n_spikes,) of float, shape | None: Each element of the outer list is a trial. The inner list contains the type of spike (e.g., evprox1 or L2_pyramidal) that occured at the corresonding time stamp. Each gid corresponds to a type via Network().gid_ranges.
timesnumpy array | None: Array of time points for samples in continuous data. This includes vsoma and isoma.
cell_type_nameslist: List of unique cell type names that are explicitly modeled in the network

Attributes

spike_timeslist (n_trials,) of list (n_spikes,) of float, shape: Each element of the outer list is a trial. The inner list contains the time stamps of spikes.
spike_gidslist (n_trials,) of list (n_spikes,) of float, shape: Each element of the outer list is a trial. The inner list contains the cell IDs of neurons that spiked.
spike_typeslist (n_trials,) of list (n_spikes,) of float, shape: Each element of the outer list is a trial. The inner list contains the type of spike (e.g., evprox1 or L2_pyramidal) that occured at the corresonding time stamp. Each gid corresponds to a type via Network::gid_ranges.
vseclist (n_trials,) of dict, shape: Each element of the outer list is a trial. Dictionary indexed by gids containing voltages for cell sections.
iseclist (n_trials,) of dict, shape: Each element of the outer list is a trial. Dictionary indexed by gids containing currents for cell sections.
timesarray-like, shape (n_times,): Array of time points for samples in continuous data. This includes vsoma and isoma.

Methods

reset()	Reset all recorded attributes to empty lists.
update_types(gid_ranges)	Update spike types in the current instance of CellResponse.
plot(ax=None, show=True)	Plot and return a matplotlib Figure object showing the aggregate network spiking activity according to cell type.
mean_rates(tstart, tstop, gid_ranges, mean_type=’all’)	Calculate mean firing rate for each cell type. Specify averaging method with mean_type argument.
write(fname)	Write spiking activity to a collection of spike trial files.

__getitem__(gid_item)[source]¶

Returns a CellResponse object with a copied subset filtered by gid.

Parameters

gid_itemint | slice: Subset of gids .

Returns

cell_responseinstance of CellResponse: See below for use cases.

__repr__()[source]¶: Return repr(self).

mean_rates(tstart, tstop, gid_ranges, mean_type='all')[source]¶

Mean spike rates (Hz) by cell type.

Parameters

tstartint | float | None

Value defining the start time of all trials.

tstopint | float | None

Value defining the stop time of all trials.

gid_rangesdict of lists or range objects

Dictionary with keys ‘evprox1’, ‘evdist1’ etc. containing the range of Cell or input IDs of different cell or input types.

mean_typestr

‘all’Average over trials and cells: Returns mean rate for cell types
‘trial’Average over cell types: Returns trial mean rate for cell types
‘cell’Average over individual cells: Returns trial mean rate for individual cells

Returns

spike_ratedict: Dictionary with keys ‘L5_pyramidal’, ‘L5_basket’, etc.

plot_spikes_hist(trial_idx=None, ax=None, spike_types=None, show=True)[source]¶

Plot the histogram of spiking activity across trials.

Parameters

trial_idxint | list of int | None

Index of trials to be plotted. If None, all trials plotted.

axinstance of matplotlib axis | None

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

spike_types: string | list | dictionary | None

String input of a valid spike type is plotted individually.

Ex: 'poisson', 'evdist', 'evprox', …

List of valid string inputs will plot each spike type individually.

Ex: ['poisson', 'evdist']

Dictionary of valid lists will plot list elements as a group.

Ex: {'Evoked': ['evdist', 'evprox'], 'Tonic': ['poisson']}

If None, all input spike types are plotted individually if any are present. Otherwise spikes from all cells are plotted. Valid strings also include leading characters of spike types

Ex: 'ev' is equivalent to ['evdist', 'evprox']

showbool

If True, show the figure.

Returns

figinstance of matplotlib Figure: The matplotlib figure handle.

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

Plot the aggregate spiking activity according to cell type.

Parameters

trial_idxint | list of int | None: Index of trials to be plotted. If None, all trials plotted.
axinstance of matplotlib axis | 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 object.

update_types(gid_ranges)[source]¶

Update spike types in the current instance of CellResponse.

Parameters

gid_rangesdict of lists or range objects: Dictionary with keys ‘evprox1’, ‘evdist1’ etc. containing the range of Cell or input IDs of different cell or input types.

write(fname)[source]¶

Write spiking activity per trial to a collection of files.

Parameters

fnamestr: String format (e.g., ‘spk_%d.txt’ or ‘spk_{0}.txt’) of the path to the output spike file(s). If no string format is provided, the trial index will be automatically appended to the file name.

Examples using `hnn_core.CellResponse`¶

hnn_core.CellResponse¶

Examples using hnn_core.CellResponse¶

Examples using `hnn_core.CellResponse`¶