pulse2percept.models.temporal

FadingTemporal

Classes

FadingTemporal(**params)

A generic temporal model for phosphene fading

class pulse2percept.models.temporal.FadingTemporal(**params)

A generic temporal model for phosphene fading

Implements phosphene fading using a leaky integrator:

\[\frac{dB}{dt} = -\frac{A+B}{\tau}\]

where \(A\) is the stimulus amplitude, \(B\) is the perceived brightness, and \(\tau\) is the exponential decay constant (tau).

The model makes the following assumptions:

• Cathodic currents (negative amplitudes) will increase perceived brightness
• Anodic currents (positive amplitudes) will decrease brightness
• Brightness is bounded in \([\theta, \infty]\), where \(\theta\) (thresh_percept) is a nonnegative scalar

Parameters:

• dt (float, optional) – Sampling time step of the simulation (ms)
• tau (float, optional) – Time decay constant for the exponential decay (ms). Larger values lead to slower decay. Brightness should decay to half its peak (“half-life”) after \(\ln(2) \tau\) milliseconds.
• thresh_percept (float, optional) – Below threshold, the percept has brightness zero.
• n_threads (int, optional) – Number of CPU threads to use during parallelization using OpenMP. Defaults to max number of user CPU cores.
• versionadded: (.) – 0.7.1:

build(**build_params)

Build the model

Every model must have a `build method, which is meant to perform all expensive one-time calculations. You must call build before calling predict_percept.

Important

Don’t override this method if you are building your own model. Customize _build instead.

Parameters:build_params (additional parameters to set) – You can overwrite parameters that are listed in get_default_params. Trying to add new class attributes outside of that will cause a FreezeError. Example: model.build(param1=val)

find_threshold(stim, bright_th, amp_range=(0, 999), amp_tol=1, bright_tol=0.1, max_iter=100, t_percept=None)

Find the threshold current for a certain stimulus

Estimates amp_th such that the output of model.predict_percept(stim(amp_th)) is approximately bright_th.

Parameters:

• stim (Stimulus) – The stimulus to use. Stimulus amplitude will be up and down regulated until amp_th is found.
• bright_th (float) – Model output (brightness) that’s considered “at threshold”.
• amp_range ((amp_lo, amp_hi), optional) – Range of amplitudes to search (uA).
• amp_tol (float, optional) – Search will stop if candidate range of amplitudes is within amp_tol
• bright_tol (float, optional) – Search will stop if model brightness is within bright_tol of bright_th
• max_iter (int, optional) – Search will stop after max_iter iterations
• t_percept (float or list of floats, optional) – The time points at which to output a percept (ms). If None, implant.stim.time is used.

Returns:

amp_th (float) – Threshold current (uA), estimated so that the output of model.predict_percept(stim(amp_th)) is within bright_tol of bright_th.

get_default_params()

Return a dictionary of default values for all model parameters

is_built

A flag indicating whether the model has been built

predict_percept(stim, t_percept=None)

Predict the temporal response

Important

Don’t override this method if you are creating your own model. Customize _predict_temporal instead.

Parameters:

• stim (: py: class: ~pulse2percept.stimuli.Stimulus or) – : py: class: ~pulse2percept.models.Percept Either a Stimulus or a Percept object. The temporal model will be applied to each spatial location in the stimulus/percept.
• t_percept (float or list of floats, optional) –

  The time points at which to output a percept (ms). If None, the percept will be output once very 20 ms (50 Hz frame rate).

  Note

  If your stimulus is shorter than 20 ms, you should specify the desired time points manually.

Returns:

percept (Percept) – A Percept object whose data container has dimensions Y x X x T. Will return None if stim is None.

Notes

• If a list of time points is provided for t_percept, the values will automatically be sorted.

set_params(**params)

Set the parameters of this model