hnn_core.simulate_dipole

hnn_core.simulate_dipole(net, tstop, dt=0.025, n_trials=None, record_vsec=False, record_isec=False, record_ca=False, postproc=False)[source]

Simulate a dipole given the experiment parameters.

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 | None

The number of trials to simulate. If None, the ‘N_trials’ value of the params used to create net is used (must be >0)

record_vsec‘all’ | ‘soma’ | False

Option to record voltages from all sections (‘all’), or just the soma (‘soma’). Default: False.

record_isec‘all’ | ‘soma’ | False

Option to record synaptic currents from all sections (‘all’), or just the soma (‘soma’). Default: False.

record_ca‘all’ | ‘soma’ | False

Option to record calcium concentration from all sections (‘all’), or just the soma (‘soma’). Default: False.

postprocbool

If True, smoothing (dipole_smooth_win) and scaling (dipole_scalefctr) values are read from the parameter file, and applied to the dipole objects before returning. 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 instead. Default: False.

Returns:
dpls: list

List of dipole objects for each trials

Examples using hnn_core.simulate_dipole

01. Simulate Event Related Potentials (ERPs)

01. Simulate Event Related Potentials (ERPs)

02. Simulate Alpha and Beta Rhythms

02. Simulate Alpha and Beta Rhythms

03. Simulate Gamma Rhythms

03. Simulate Gamma Rhythms

04. From MEG sensor-space data to HNN simulation

04. From MEG sensor-space data to HNN simulation

05. Simulate beta modulated ERP

05. Simulate beta modulated ERP

01. Plot firing pattern

01. Plot firing pattern

02. Record extracellular potentials

02. Record extracellular potentials

03. Modifying local connectivity

03. Modifying local connectivity

04. Use MPI backend for parallelization

04. Use MPI backend for parallelization

05. Optimize simulated evoked response parameters

05. Optimize simulated evoked response parameters

06. Animating HNN simulations

06. Animating HNN simulations

08. Optimize simulated rhythmic responses

08. Optimize simulated rhythmic responses