pulse2percept.models.granley2021¶

BiphasicAxonMapModel, BiphasicAxonMapSpatial, [Granley2021]

Functions

cond_jit(fn[, static_argnums]) Conditional decorator for jax jit

Classes

`BiphasicAxonMapModel`(**params)	BiphasicAxonMapModel of [Granley2021] (standalone model)
`BiphasicAxonMapSpatial`(**params)	BiphasicAxonMapModel of [Granley2021] (spatial model)
`DefaultBrightModel`(**params)	Default model to be used for brightness scaling in BiphasicAxonMapModel Implements Eq 4 from [Granley2021] Fit using data from [Nanduri2012] and [Weitz2015]
`DefaultSizeModel`(rho[, engine])	Default model to be used for size (rho) scaling in BiphasicAxonMapModel Implements Eq 5 from [Granley2021] Fit using data from [Nanduri2012] and [Weitz2015]
`DefaultStreakModel`(axlambda[, engine])	Default model to be used for streak length (lambda) scaling in BiphasicAxonMapModel Implements Eq 6 from [Granley2021] Fit using data from [Weitz2015]

class pulse2percept.models.granley2021.BiphasicAxonMapModel(**params)[source]¶

BiphasicAxonMapModel of [Granley2021] (standalone model)

An AxonMapModel where phosphene brightness, size, and streak length scale according to amplitude, frequency, and pulse duration

All stimuli must be BiphasicPulseTrains.

This model is different than other spatial models in that it calculates one representative percept from all time steps of the stimulus.

Brightness, size, and streak length scaling are controlled by the parameters bright_model, size_model, and streak model respectively. By default, these are set to classes that implement Eqs 3-6 from Granley 2021. These models can be individually customized by setting the bright_model, size_model, or streak_model to any python callable with signature f(freq, amp, pdur)

Note

Using this model in combination with a temporal model is not currently supported and will give unexpected results

Parameters:

bright_model (callable, optional) – Model used to modulate percept brightness with amplitude, frequency, and pulse duration
size_model (callable, optional) – Model used to modulate percept size with amplitude, frequency, and pulse duration
streak_model (callable, optional) – Model used to modulate percept streak length with amplitude, frequency, and pulse duration
do_thresholding (boolean) – Use probabilistic sigmoid thresholding, default: False
**params (dict, optional) –
Arguments to be passed to AxonMapSpatial

axlambda: double, optional

Exponential decay constant along the axon(microns).

rho: double, optional

Exponential decay constant away from the axon(microns).

eye: {‘RE’, LE’}, optional

Eye for which to generate the axon map.

xrange : (x_min, x_max), optional

A tuple indicating the range of x values to simulate (in degrees of visual angle). In a right eye, negative x values correspond to the temporal retina, and positive x values to the nasal retina. In a left eye, the opposite is true.

yrange : tuple, (y_min, y_max)

A tuple indicating the range of y values to simulate (in degrees of visual angle). Negative y values correspond to the superior retina, and positive y values to the inferior retina.

xystep : int, double, tuple

Step size for the range of (x,y) values to simulate (in degrees of visual angle). For example, to create a grid with x values [0, 0.5, 1] use x_range=(0, 1) and xystep=0.5.

grid_type : {‘rectangular’, ‘hexagonal’}

Whether to simulate points on a rectangular or hexagonal grid

vfmap : VisualFieldMap, optional

An instance of a VisualFieldMap object that provides retinotopic mappings. By default, Watson2014Map is used.

n_gray : int, optional

The number of gray levels to use. If an integer is given, k-means clustering is used to compress the color space of the percept into n_gray bins. If None, no compression is performed.

noise : float or int, optional

Adds salt-and-pepper noise to each percept frame. An integer will be interpreted as the number of pixels to subject to noise in each frame. A float between 0 and 1 will be interpreted as a ratio of pixels to subject to noise in each frame.

loc_od, loc_od: (x,y), optional

Location of the optic disc in degrees of visual angle. Note that the optic disc in a left eye will be corrected to have a negative x coordinate.

n_axons: int, optional

Number of axons to generate.

axons_range: (min, max), optional

The range of angles(in degrees) at which axons exit the optic disc. This corresponds to the range of $phi_0$ values used in [Jansonius2009].

n_ax_segments: int, optional

Number of segments an axon is made of.

ax_segments_range: (min, max), optional

Lower and upper bounds for the radial position values(polar coords) for each axon.

min_ax_sensitivity: float, optional

Axon segments whose contribution to brightness is smaller than this value will be pruned to improve computational efficiency. Set to a value between 0 and 1.

axon_pickle: str, optional

File name in which to store precomputed axon maps.

ignore_pickle: bool, optional

A flag whether to ignore the pickle file in future calls to model.build().

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 a `FreezeError`. 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 of model.predict_percept(stim(amp_th)) is approximately bright_th.

Parameters:

implant (ProsthesisSystem) – The implant and its 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.

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.

Overrides base predict percept to keep desired time axes

Important

You must call build before calling predict_percept.

Note: The stimuli should use amplitude as a factor of threshold, NOT raw amplitude in microamps

Parameters:	implant (`ProsthesisSystem`) – A valid prosthesis system. A stimulus can be passed via `stim`. 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 whose `data` container has dimensions Y x X x T. Will return None if `implant.stim` is None.

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.granley2021.BiphasicAxonMapSpatial(**params)[source]¶

BiphasicAxonMapModel of [Granley2021] (spatial model)

An AxonMapModel where phosphene brightness, size, and streak length scale according to amplitude, frequency, and pulse duration

All stimuli must be BiphasicPulseTrains.

This model is different than other spatial models in that it calculates one representative percept from all time steps of the stimulus.

Brightness, size, and streak length scaling are controlled by the effects models bright_model, size_model, and streak model respectively. By default, these are set to classes that implement Eqs 3-6 from Granley 2021. These models can be individually customized by setting the bright_model, size_model, or streak_model to any python callable with signature f(freq, amp, pdur)

Note

Using this model in combination with a temporal model is not currently supported and will give unexpected results

Parameters:

bright_model (callable, optional) – Model used to modulate percept brightness with amplitude, frequency, and pulse duration
size_model (callable, optional) – Model used to modulate percept size with amplitude, frequency, and pulse duration
streak_model (callable, optional) – Model used to modulate percept streak length with amplitude, frequency, and pulse duration
**params (optional) –
Additional params for AxonMapModel.

axlambda: double, optional

Exponential decay constant along the axon(microns).

rho: double, optional

Exponential decay constant away from the axon(microns).

eye: {‘RE’, LE’}, optional

Eye for which to generate the axon map.

xrange : (x_min, x_max), optional

A tuple indicating the range of x values to simulate (in degrees of visual angle). In a right eye, negative x values correspond to the temporal retina, and positive x values to the nasal retina. In a left eye, the opposite is true.

yrange : tuple, (y_min, y_max)

A tuple indicating the range of y values to simulate (in degrees of visual angle). Negative y values correspond to the superior retina, and positive y values to the inferior retina.

xystep : int, double, tuple

Step size for the range of (x,y) values to simulate (in degrees of visual angle). For example, to create a grid with x values [0, 0.5, 1] use x_range=(0, 1) and xystep=0.5.

grid_type : {‘rectangular’, ‘hexagonal’}

Whether to simulate points on a rectangular or hexagonal grid

vfmap : VisualFieldMap, optional

An instance of a VisualFieldMap object that provides retinotopic mappings. By default, Watson2014Map is used.

n_gray : int, optional

The number of gray levels to use. If an integer is given, k-means clustering is used to compress the color space of the percept into n_gray bins. If None, no compression is performed.

noise : float or int, optional

Adds salt-and-pepper noise to each percept frame. An integer will be interpreted as the number of pixels to subject to noise in each frame. A float between 0 and 1 will be interpreted as a ratio of pixels to subject to noise in each frame.

loc_od, loc_od: (x,y), optional

Location of the optic disc in degrees of visual angle. Note that the optic disc in a left eye will be corrected to have a negative x coordinate.

n_axons: int, optional

Number of axons to generate.

axons_range: (min, max), optional

The range of angles(in degrees) at which axons exit the optic disc. This corresponds to the range of $phi_0$ values used in [Jansonius2009].

n_ax_segments: int, optional

Number of segments an axon is made of.

ax_segments_range: (min, max), optional

Lower and upper bounds for the radial position values(polar coords) for each axon.

min_ax_sensitivity: float, optional

Axon segments whose contribution to brightness is smaller than this value will be pruned to improve computational efficiency. Set to a value between 0 and 1.

axon_pickle: str, optional

File name in which to store precomputed axon maps.

ignore_pickle: bool, optional

A flag whether to ignore the pickle file in future calls to model.build().

n_threads: int, optional

Number of CPU threads to use during parallelization using OpenMP. Defaults to max number of user CPU cores.

biphasic_axon_map_jax[source]¶

Predicts the spatial response of BiphasicAxonMapModel using Jax

eparams : jnp.array with shape (n_elecs, 3): Brightness, size, and streak length effect on each electrode
x, y : jnp.array with shape (n_elecs): x and y coordinate of each electrode
axon_segments : jnp.array with shape (n_points, n_ax_segments, 3): Closest axon segment to each simulated point, as returned by calc_axon_sensitivities
rho : float: The rho parameter of the axon map model: exponential decay constant (microns) away from the axon.
axlambda : float: The lambda parameter of the axon map model: exponential decay constant (microns) away from the cell body along the axon
thresh_percept : float: Spatial responses smaller than thresh_percept will be set to zero

build(**build_params)[source]¶

Build the model

Performs expensive one-time calculations, such as building the spatial grid used to predict a percept. 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)`

calc_axon_sensitivity(bundles, pad=False)[source]¶

Calculate the sensitivity of each axon segment to electrical current

This function combines the x,y coordinates of each bundle segment with a sensitivity value that depends on the distance of the segment to the cell body and self.axlambda.

The number of bundles must equal the number of points on self.grid`. The function will then assume that the i-th bundle passes through the i-th point on the grid. This is used to determine the bundle segment that is closest to the i-th point on the grid, and to cut off all segments that extend beyond the soma. This effectively transforms a bundle into an axon, where the first axon segment now corresponds with the i-th location of the grid.

After that, each axon segment gets a sensitivity value that depends on the distance of the segment to the soma (with decay rate self.axlambda). This is typically done during the build process, so that the only work left to do during run time is to multiply the sensitivity value with the current applied to each segment.

If pad is True (set when engine is ‘jax’), axons are padded to all have the same length as the longest axon

Parameters: bundles (list of Nx2 arrays) – A list of bundles, where every bundle is an Nx2 array consisting of the x,y coordinates of each axon segment (retinal coords, microns). Note that each bundle will most likely have a different N

Returns: axon_contrib (numpy array with shape (n_points, axon_length, 3)) – An array of axon segments and sensitivity values. Each entry in the array is a Nx3 array, where the first two columns contain the retinal coordinates of each axon segment (microns), and the third column contains the sensitivity of the segment to electrical current. The latter depends on self.axlambda. axon_length is set to the maximum length of any axon after being trimmed due to min_sensitivity

calc_bundle_tangent(xc, yc)[source]¶

Calculates orientation of fiber bundle tangent at (xc, yc)

Parameters:	yc (xc,) – (x, y) retinal location of point at which to calculate bundle orientation in microns.
Returns:	tangent (scalar) – An angle in radians

calc_bundle_tangent_fast(xc, yc, bundles=None)[source]¶

Calculates orientation of fiber bundle tangent at (xc, yc) This function supports multiple queries (xc and yc can be arrays), without requiring growing the axon bundles again for each point (like calc_bundle_tangent). It uses a ckdtree, which will be slower for single points, but significantly faster for multiple points.

Parameters:	yc (xc,) – (x, y) retinal location of point at which to calculate bundle orientation in microns.
Returns:	tangent (array of floats) – Angles in radians

find_closest_axon(bundles, xret=None, yret=None, return_index=False)[source]¶

Finds the closest axon segment for a point on the retina

This function will search a number of nerve fiber bundles (bundles) and return the bundle that is closest to a particular point (or list of points) on the retinal surface (xret, yret).

Parameters:

bundles (list of Nx2 arrays) – A list of bundles, where every bundle is an Nx2 array consisting of the x,y coordinates of each axon segment (retinal coords, microns). Note that each bundle will most likely have a different N
yret (xret,) – The x,y location on the retina (in microns, where the fovea is the origin) for which to find the closests axon.
return_index (bool, optional) – If True, the function will also return the index into bundles that represents the closest axon

Returns:

axon (Nx2 array or list of Nx2 arrays) – For each point in (xret, yret), returns an Nx2 array that represents the closest axon to that point. Each row in the array contains the x,y retinal coordinates (microns) of a particular axon segment.
idx_axon (scalar or list of scalars, optional) – If return_index is True, also returns the index in bundles of the closest axon (or list of closest axons).

find_threshold(implant, bright_th, amp_range=(0, 999), amp_tol=1, bright_tol=0.1, max_iter=100)[source]¶

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:

implant (ProsthesisSystem) – The implant and its 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

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()[source]¶: Return a dictionary of default values for all model parameters

grow_axon_bundles(n_bundles=None, prune=True)[source]¶

Grow a number of axon bundles

This method generates the trajectory of a number of nerve fiber bundles based on the mathematical model described in [Beyeler2019], which is based on [Jansonius2009].

Bundles originate at the optic nerve head with initial angle phi0. The method generates n_bundles axon bundles whose phi0 values are linearly sampled from self.axons_range (polar coords). Each axon will consist of self.n_ax_segments segments that span self.ax_segments_range distance from the optic nerve head (polar coords).

Parameters:	n_bundles (int, optional) – Number of axon bundles to generate. If None, `self.n_axons` is used prune (bool, optional) – If set to True, will remove axon segments that are outside the simulated area `self.xrange`, `self.yrange` for the sake of computational efficiency.
Returns:	bundles (list of Nx2 arrays) – A list of bundles, where every bundle is an Nx2 array consisting of the x,y coordinates of each axon segment (retinal coords, microns). Note that each bundle will most likely have a different N

is_built¶: A flag indicating whether the model has been built

plot(use_dva=False, style='hull', annotate=True, autoscale=True, ax=None, figsize=None)[source]¶

Plot the axon map

Parameters:

use_dva (bool, optional) – Uses degrees of visual angle (dva) if True, else retinal coordinates (microns)
style ({'hull', 'scatter', 'cell'}, optional) –
Grid plotting style:
- ’hull’: Show the convex hull of the grid (that is, the outline of the smallest convex set that contains all grid points).
- ’scatter’: Scatter plot all grid points
- ’cell’: Show the outline of each grid cell as a polygon. Note that this can be costly for a high-resolution grid.
annotate (bool, optional) – Flag whether to label the four retinal quadrants
autoscale (bool, optional) – Whether to adjust the x,y limits of the plot
ax (matplotlib.axes._subplots.AxesSubplot, optional) – A Matplotlib axes object. If None, will either use the current axes (if exists) or create a new Axes object
figsize ((float, float), optional) – Desired (width, height) of the figure in inches

predict_one_point_jax(axon, eparams, x, y, rho)[source]¶: Predicts the brightness contribution from each axon segment for each pixel

predict_percept(implant, t_percept=None)[source]¶

Predicts the spatial response Override base predict percept to have desired timesteps and remove unneccesary computation

Parameters:	implant (`ProsthesisSystem`) – A valid prosthesis system. A stimulus can be passed via `stim`. 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 whose `data` container has dimensions Y x X x 1. Will return None if `implant.stim` is None.

predict_percept_batched(implant, stims, t_percept=None)[source]¶

Batched version of predict_percept Only supported with jax engine

This is significantly faster if you do not batch ALL of your percepts, but rather, split them into chunks (128 - 256 percepts each) and repeatedly call that. This is because jax has to compile on the first call, so repeated calls is much faster.

Parameters:	implant (`ProsthesisSystem`) – A valid prosthesis system. stims (list of stimuli) – A percept will be predicted for each stimulus. Each stimulus must be a collection of :py:class:`~pulse2percept.stimuli.BiphasicPulseTrains’ 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:	percepts (list of `Percept`) – A list of Percept objects whose `data` container has dimensions Y x X x 1.

set_params(**params)[source]¶: Set the parameters of this model

class pulse2percept.models.granley2021.DefaultBrightModel(**params)[source]¶

Default model to be used for brightness scaling in BiphasicAxonMapModel Implements Eq 4 from [Granley2021] Fit using data from [Nanduri2012] and [Weitz2015]

do_thresholding : bool, optional: Set to true to enable probabilistic phosphene appearance at near-threshold amplitudes
a0, a1 : float, optional: Linear regression coefficients (slope and intercept) of pulse_duration vs threshold curve (Eq 3). Amplitude factor will be scaled by a0*pdur + a1.
a2, a3, a4: float, optional: Linear regression coefficients for brightness vs amplitude and frequency (Eq 4) F_bright = a2*scaled_amp + a3*freq + a4

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 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)`

get_default_params()[source]¶: Return a dict of user-settable model parameters

is_built¶: A flag indicating whether the model has been built

predict_freq_amp(amp, freq)[source]¶: Eq 4 in paper, A_2*A_tilde + A_3*f + A_4

scale_threshold(pdur)[source]¶

Based on eq 3 in paper, this function produces the factor that amplitude will be scaled by to produce a_tilde. Computes A_0 * t + A_1 (1/threshold) .. note:

This equation has been updated from the original paper, and has been refit
to data from Argus II users from Horsager et al. 2009.

set_params(**params)[source]¶: Set the parameters of this model

class pulse2percept.models.granley2021.DefaultSizeModel(rho, engine='serial', **params)[source]¶

Default model to be used for size (rho) scaling in BiphasicAxonMapModel Implements Eq 5 from [Granley2021] Fit using data from [Nanduri2012] and [Weitz2015]

rho : float32: Rho parameter of BiphasicAxonMapModel (spatial decay rate)
a0, a1 : float, optional: Linear regression coefficients (slope and intercept) of pulse_duration vs threshold curve (Eq 3). Amplitude factor will be scaled by a0*pdur + a1.
a5, a6 : float, optional: Linear regression coefficients for size vs amplitude (Eq 5) F_size = a5*scaled_amp + a6

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 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)`

get_default_params()[source]¶: Return a dict of user-settable model parameters

is_built¶: A flag indicating whether the model has been built

scale_threshold(pdur)[source]¶

Based on eq 3 in paper, this function produces the factor that amplitude will be scaled by to produce a_tilde. Computes A_0 * t + A_1 (1/threshold) .. note:

This equation has been updated from the original paper, and has been refit
to data from Argus II users from Horsager et al. 2009.

set_params(**params)[source]¶: Set the parameters of this model

class pulse2percept.models.granley2021.DefaultStreakModel(axlambda, engine='serial', **params)[source]¶

Default model to be used for streak length (lambda) scaling in BiphasicAxonMapModel Implements Eq 6 from [Granley2021] Fit using data from [Weitz2015]

axlambda : float32: Axlambda parameter of BiphasicAxonMapModel (axonal decay rate)
a7, a8, a9: float, optional: Regression coefficients for streak length vs pulse duration (Eq 6) F_streak = -a7*pdur^a8 + a9

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 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)`

get_default_params()[source]¶: Return a dict of user-settable model parameters

is_built¶: A flag indicating whether the model has been built

set_params(**params)[source]¶: Set the parameters of this model

pulse2percept.models.granley2021.cond_jit(fn, static_argnums=None)[source]¶: Conditional decorator for jax jit