pulse2percept.models.horsager2009¶
Horsager2009Model
, Horsager2009Temporal
[Horsager2009]
Classes
Horsager2009Model (**params) |
[Horsager2009] Standalone model |
Horsager2009Temporal (**params) |
Temporal model of [Horsager2009] |
-
class
pulse2percept.models.horsager2009.
Horsager2009Model
(**params)[source]¶ [Horsager2009] Standalone model
Implements the temporal response model described in [Horsager2009], which assumes that the temporal activation of retinal tissue is the output of a linear-nonlinear model cascade (see Fig.2 in the paper).
Note
Use this class if you want a standalone model. Use
Horsager2009Temporal
if you want to combine the temporal model with a spatial model.Parameters: - dt (float, optional) – Sampling time step (ms)
- tau1 (float, optional) – Time decay constant for the fast leaky integrater.
- tau2 (float, optional) – Time decay constant for the charge accumulation.
- tau3 (float, optional) – Time decay constant for the slow leaky integrator.
- eps (float, optional) – Scaling factor applied to charge accumulation. Common values at threshold: 0.00225, suprathreshold: 0.00873. Power nonlinearity (exponent of the half-wave rectification). Common values at threshold: 3.43, suprathreshold: 0.83.
- 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.
-
build
(**build_params)[source]¶ Build the model
Performs expensive one-time calculations, such as building the spatial grid used to predict a percept.
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 aFreezeError
. Example:model.build(param1=val)
Returns: self
-
find_threshold
(implant, bright_th, amp_range=(0, 999), amp_tol=1, bright_tol=0.1, max_iter=100, t_percept=None)[source]¶ Find the threshold current for a certain stimulus
Estimates
amp_th
such that the output ofmodel.predict_percept(stim(amp_th))
is approximatelybright_th
.Parameters: - implant (
ProsthesisSystem
) – The implant and its stimulus to use. Stimulus amplitude will be up and down regulated untilamp_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
ofbright_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 withinbright_tol
ofbright_th
.- implant (
-
has_space
¶ Returns True if the model has a spatial component
-
has_time
¶ Returns True if the model has a temporal component
-
is_built
¶ Returns True if the
build
model has been called
-
predict_percept
(implant, t_percept=None)[source]¶ Predict a percept
Important
You must call
build
before callingpredict_percept
.Parameters: - implant (
ProsthesisSystem
) – A valid prosthesis system. A stimulus can be passed viastim
. - 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: percept (
Percept
) – A Percept object whosedata
container has dimensions Y x X x T. Will return None ifimplant.stim
is None.- implant (
-
set_params
(params)[source]¶ Set model parameters
This is a convenience function to set parameters that might be part of the spatial model, the temporal model, or both.
Alternatively, you can set the parameter directly, e.g.
model.spatial.verbose = True
.Note
If a parameter exists in both spatial and temporal models(e.g.,
verbose
), both models will be updated.Parameters: params (dict) – A dictionary of parameters to set.
-
class
pulse2percept.models.horsager2009.
Horsager2009Temporal
(**params)[source]¶ Temporal model of [Horsager2009]
Implements the temporal response model described in [Horsager2009], which assumes that the temporal activation of retinal tissue is the output of a linear-nonlinear model cascade (see Fig.2 in the paper).
Note
Use this class if you want to combine the temporal model with a spatial model. Use
Horsager2009Model
if you want a a standalone model.Parameters: - dt (float, optional) – Sampling time step (ms)
- tau1 (float, optional) – Time decay constant for the fast leaky integrater.
- tau2 (float, optional) – Time decay constant for the charge accumulation.
- tau3 (float, optional) – Time decay constant for the slow leaky integrator.
- eps (float, optional) – Scaling factor applied to charge accumulation. Common values at threshold: 2.25, suprathreshold: 8.73.
- beta (float, optional) – Power nonlinearity (exponent of the half-wave rectification). Common values at threshold: 3.43, suprathreshold: 0.83.
- 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.
-
build
(**build_params)[source]¶ Build the model
Every model must have a
`build
method, which is meant to perform all expensive one-time calculations. You must callbuild
before callingpredict_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 aFreezeError
. 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)[source]¶ Find the threshold current for a certain stimulus
Estimates
amp_th
such that the output ofmodel.predict_percept(stim(amp_th))
is approximatelybright_th
.Parameters: - stim (
Stimulus
) – The stimulus to use. Stimulus amplitude will be up and down regulated untilamp_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
ofbright_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 withinbright_tol
ofbright_th
.- stim (
-
is_built
¶ A flag indicating whether the model has been built
-
predict_percept
(stim, t_percept=None)[source]¶ 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 whosedata
container has dimensions Y x X x T. Will return None ifstim
is None.Notes
- If a list of time points is provided for
t_percept
, the values will automatically be sorted.
- stim (: py: class: