# Computational Models¶

The `models`

module provides a number of published
and verified computational models that can be used to predict neural responses
or visual percepts resulting from electrical stimulation.

A `Model`

object consists of:

- a
`SpatialModel`

, describing how electrical stimulation affects the neural tissue or elicited phosphene*in different spatial locations of the visual field*, and/or - a
`TemporalModel`

, describing how the response of the neural tissue or elicited phosphene evolves*over time*.

pulse2percept provides the following computational models:

Reference | Model | Type |

[Horsager2009] | `Horsager2009Model` |
temporal |

[Horsager2009] | `Horsager2009Temporal` |
temporal |

[Nanduri2012] | `Nanduri2012Model` |
spatial + temporal |

[Nanduri2012] | `Nanduri2012Spatial` |
spatial |

[Nanduri2012] | `Nanduri2012Temporal` |
temporal |

[Beyeler2019] | `AxonMapModel` |
spatial |

[Beyeler2019] | `ScoreboardModel` |
spatial |

Note

Spatial and temporal models can be mix-and-matched to create new models. See Creating your own model.

## Basic usage¶

All models follow the same basic work flow:

**Initialize**the model with the desired model parameters.**Build**the model to perform one-time heavy computations such as building the axon map in`AxonMapModel`

.**Predict a percept**by passing an implant that contains a stimulus. The model will return a`Percept`

object that acts as a data container with labeled axes.

Here is how to run the `ScoreboardModel`

:

```
# Initialize the model:
In [1]: from pulse2percept.models import ScoreboardModel
In [2]: model = ScoreboardModel(rho=200)
# Build the model:
In [3]: model.build()
Out[3]:
ScoreboardModel(engine='serial', grid_type='rectangular',
n_jobs=1, rho=200, scheduler='threading',
spatial=ScoreboardSpatial, temporal=None,
thresh_percept=0, verbose=True,
xrange=(-15, 15), xystep=0.25,
yrange=(-15, 15))
# Predict the percept resulting from stimulating Electrode
# A8 in Argus II with 30 uA:
In [4]: from pulse2percept.implants import ArgusII
In [5]: percept = model.predict_percept(ArgusII(stim={'A8': 30}))
```

## Building your own model¶

To build your own model, you can mix and match spatial and temporal models at will.

For example, to create a model that combines the scoreboard model described in [Beyeler2019] with the temporal model cascade described in [Nanduri2012], use the following:

```
# Instantiate:
model = Model(spatial=ScoreboardSpatial(),
temporal=Nanduri2012Temporal())
# Build:
model.build()
# etc.
```

To create a more advanced model, you will need to subclass the appropriate base
class. For example, to create a new spatial model, you will need to subclass
`SpatialModel`

and provide implementations for
the following methods:

`dva2ret`

: a means to convert from degrees of visual angle (dva) to retinal coordinates (microns).`ret2dva`

: a means to convert from retinal coordinates to dva.`_predict_spatial`

: a method that accepts an`ElectrodeArray`

as well as a`Stimulus`

and computes the brightness at all spatial coordinates of`self.grid`

, returned as a 2D NumPy array (space x time).

In addition, you can customize the following methods:

`__init__`

: the constructor can be used to define additional parameters (note that you cannot add parameters on-the-fly)`get_default_params`

: all settable model parameters must be listed by this method`_build`

(optional): a way to add one-time computations to the build process

A full working example:

```
class MySpatialModel(SpatialModel):
def __init__(self, **params):
"""Constructor"""
# Make sure to call the parent's (SpatialModel's constructor):
super(MySpatialModel, self).__init__(self, **params)
# You can set additional parameters here (e.g., stuff you will
# need later on in ``_build``). You will not be able to add
# parameters outside the constructor or ``get_default_params``.
self.n_fib = 100
def get_default_params(self):
"""Return a dictionary of settable model parameters"""
# Get all parameters already set by the parent (SpatialModel):
params = super(MySpatialModel, self).get_default_params()
# Add our own:
params.update(myparam=1)
# Return the combined dictionary:
return params
def dva2ret(self, dva):
"""Convert degrees of visual angle (dva) into retinal coords (um)"""
return 280.0 * dva
def ret2dva(self, ret):
"""Convert retinal corods (um) to degrees of visual angle (dva)"""
return ret / 280.0
def _build(self):
"""Perform heavy computations during the build process"""
# Perform some expensive computation using parameters you
# initialized in the constructor:
self.heavy = some_heavy_comp(self.n_fib)
def _predict_spatial(self, earray, stim):
"""Calculate the spatial response at different time points"""
resp = np.zeros(self.grid.size, stim.time.size)
for idx_t, t in enumerate(stim.time):
for idx_xy, (x, y) in enumerate(self.grid):
# Response at (x,y,t) is the sum of x,y coordinates and
# all the stimuli at time t (an arbitrary, silly choice):
resp[idx_xy, idx_t] = x + y + np.sum(stim[:, t])
return resp
```

Similarly, a new temporal model needs to subclass from
`TemporalModel`

and provide a
`_predict_temporal`

method:

```
class MyTemporalModel(TemporalModel):
def _predict_temporal(self, stim, t_percept):
"""Calculates the temporal response at different time points"""
# Response at (x,y,t) is the stimulus at (x,y,t). Use stim's smart
# indexing to do automatic interpolation:
return stim[:, t_percept]
```

## Stand-alone models vs. spatial/temporal model components¶

In general, you will want to work with `Model`

objects, which provide all the necessary glue between a spatial and/or a
temporal model component. Objects are named accordingly:

- An object named
***Model**is based on`Model`

- An object named
***Spatial**is based on`SpatialModel`

- An object named
***Temporal**is based on`TemporalModel`

However, nobody stops you from instantiating a spatial or temporal model directly:

```
# Option 1 (preferred): Work with Model objects:
from pulse2percept.models import Model, Nanduri2012Temporal
model = Model(temporal=Nanduri2012Temporal())
model.build()
model.predict_percept(implant)
# Option 2: Work directly with a temporal model:
model = Nanduri2012Temporal()
model.build()
model.predict_percept(implant.stim)
```

The differences between the two are subtle:

- As you can see from the example above, a temporal model will expect a
`Stimulus`

object in its`predict_percept`

method (because it has no notion of space). It will return a 2-D NumPy array (space x time). - In contrast, the stand-alone model will expect a
`ProsthesisSystem`

object (which provides a notion of space and itself contains a`Stimulus`

), and will return a`Percept`

object.

## Getting and setting parameters¶

A `Model`

will hide the complexity that some
parameters exist only in the spatial or temporal model component.

Consider the following model:

```
In [6]: from pulse2percept.models import (Model, ScoreboardSpatial,
...: Nanduri2012Temporal)
...:
In [7]: model = Model(spatial=ScoreboardSpatial(),
...: temporal=Nanduri2012Temporal())
...:
# Set `rho` param of the scoreboard model (works even though it's really
# `model.spatial.rho`):
In [8]: model.rho = 123
# Print the simulation time step of the Nanduri model (works even though
# it's really `model.temporal.dt`):
In [9]: print(model.dt)
0.005
```

Although `rho`

exists only in the scoreboard model, and `dt`

exists only
in the temporal model, you can get and set them as if they were part of the
main model.

Warning

If a parameter exists in both spatial and temporal models (e.g.,
`thresh_percept`

), then calling `model.thresh_percept = 0`

will update
both the spatial and temporal model.

Alternatively, use `model.spatial.thresh_percept = 0`

or
`model.temporal.thresh_percept = 0`

.