hnn_core.optimization.optimize_evoked¶
- hnn_core.optimization.optimize_evoked(params, target_dpl, initial_dpl, maxiter=50, timing_range_multiplier=3.0, sigma_range_multiplier=50.0, synweight_range_multiplier=500.0, decay_multiplier=1.6, scale_factor=1.0, smooth_window_len=None)[source]¶
Optimize drives to generate evoked response.
- Parameters
- paramsdict
The initial params
- target_dplinstance of Dipole
The target experimental dipole.
- initial_dplinstance of Dipole
The initial dipole to start the optimization.
- maxiterint
The maximum number of simulations to run for optimizing one “chunk”.
- timing_range_multiplierfloat
The scale of timing values to sweep over.
- sigma_range_multiplierfloat
The scale of sigma values to sweep over.
- synweight_range_multiplierfloat
The scale of input synaptic weights to sweep over.
- decay_multiplierfloat
The decay multiplier.
- scale_factorfloat
Scales the simulated dipoles by scale_factor to match target_dpl.
- smooth_window_lenint
The length of the hamming window (in samples) to smooth the simulated dipole waveform in each optimization step.
- Returns
- paramsdict
The optimized params dictionary.
Notes
This optimization protocol utilizes the Constrained Optimization By Linear Approximation (COBYLA) method: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_cobyla.html # noqa
