Hierarchical Gaussian Stimulus Generation¶
This is a stimulus generation class in which stimuli are formed by applying random weights to a basis of Gaussians described by the class properties.
Unique Properties¶
This stimulus generation method has seven properties in addition to those inhereted from the Abstract and Abstract Binned classes. Defaults:
- n_broad = 3 % Number of "wide" Gaussians in the basis
- n_med = 8 % Number of "medium" Gaussians in the basis
- n_narrow = 6 % Number of "narrow" Gaussians in the basis
- broad_std = 8000 % Standard deviation of the "wide" Gaussians
- med_std = 2000 % Standard deviation of the "medium" Gaussians
- narrow_std = 100 % Standard deviation of the "narrow" Gaussians
- scale_fact = 40 % Max power (dB)
generate_stimulus¶
Generate a stimulus by applying random weights to a basis of Gaussians.
OUTPUTS:
stim: self.nfft + 1 x 1 numerical vector,
the stimulus waveform,
Fs: 1x1 numerical scalar,
the sample rate in Hz.
spect: self.nfft / 2 x 1 numerical vector,
the half-spectrum, in dB.
binned_repr: [], empty because this is not a binned class.
w: self.n_broad + self.n_med + self.n_narrow x 1 numerical vector,
the weight vector corresponding to the each curve.
Class Properties Used:
See Also
get_basis¶
Generate a basis vector from the Gaussians stored in the class.
OUTPUTS:
B: self.nfft / 2 x self.n_broad + self.n_med + self.n_narrow numerical array,
Gaussian distributions for each type specified in the class fields
Class Properties Used: