hnn_core.parallel_backends.JoblibBackend#

class hnn_core.parallel_backends.JoblibBackend(n_jobs=1)[source]#

The JoblibBackend class.

Parameters:

n_jobsint | None: The number of jobs to start in parallel. If None, then 1 trial will be started without parallelism

Attributes:

Methods

simulate(net, tstop, dt, n_trials[, ...])

Simulate the HNN model

simulate(net, tstop, dt, n_trials, postproc=False, baseline_correction=True)[source]#

Simulate the HNN model

Parameters:

netNetwork object: The Network object specifying how cells are connected.
tstopfloat: The simulation stop time (ms).
dtfloat: The integration time step of h.CVode (ms)
n_trialsint: Number of trials to simulate.
postprocbool, default=False: Deprecated. If True, smoothing (dipole_smooth_win) and scaling (dipole_scalefctr) values are read from the Network’s parameter file, and applied to the dipole objects before returning (the default Network parameter file, hnn_core/param/default.json, uses a smoothing value of 30 ms and a scaling factor of 3000). Note that this setting only affects the dipole waveforms, and not somatic voltages, possible extracellular recordings etc. The preferred way is to use the smooth() and scale() methods after the simulation is run instead.
baseline_correctionbool, default=True: Whether to apply the baseline correction after simulation (which correction is used depends on Network._model_variant). Defaults to True, applying the appropriate correction.

Returns: