canns.task.tracking¶

Classes¶

`PopulationCoding1D`	Population coding task for 1D continuous attractor networks.
`PopulationCoding2D`	Population coding task for 2D continuous attractor networks.
`SmoothTracking1D`	Smooth tracking task for 1D continuous attractor networks.
`SmoothTracking2D`	Smooth tracking task for 2D continuous attractor networks.
`TemplateMatching1D`	Template matching task for 1D continuous attractor networks.
`TemplateMatching2D`	Template matching task for 2D continuous attractor networks.

Module Contents¶

class canns.task.tracking.PopulationCoding1D(cann_instance, before_duration, after_duration, Iext, duration, time_step=0.1)[source]¶

Bases: PopulationCoding

Population coding task for 1D continuous attractor networks.

A stimulus is presented for a specific duration, preceded and followed by periods of no stimulation.

Workflow:: Setup -> Create a 1D CANN and the task. Execute -> Call get_data(). Result -> Use task.data as the input sequence.

Examples

>>> import brainpy.math as bm
>>> from canns.models.basic import CANN1D
>>> from canns.task.tracking import PopulationCoding1D
>>>
>>> bm.set_dt(0.1)
>>> model = CANN1D(num=64)
>>> task = PopulationCoding1D(
...     cann_instance=model,
...     before_duration=1.0,
...     after_duration=1.0,
...     Iext=0.0,
...     duration=2.0,
...     time_step=bm.get_dt(),
... )
>>> task.get_data()
>>> task.data.shape[0] == task.total_steps
True

Initializes the Population Coding task.

Parameters:

cann_instance (BaseCANN1D) – An instance of the 1D CANN model.
before_duration (float | Quantity) – Duration of the initial period with no stimulus.
after_duration (float | Quantity) – Duration of the final period with no stimulus.
Iext (float | Quantity) – The position of the external input during the stimulation period.
duration (float | Quantity) – The duration of the stimulation period.
time_step (float | Quantity, optional) – The simulation time step. Defaults to 0.1.

after_duration[source]¶

before_duration[source]¶

class canns.task.tracking.PopulationCoding2D(cann_instance, before_duration, after_duration, Iext, duration, time_step=0.1)[source]¶

Bases: PopulationCoding

Population coding task for 2D continuous attractor networks.

A 2D stimulus is presented for a duration with pre- and post-silence.

Workflow:: Setup -> Create a 2D CANN and the task. Execute -> Call get_data(). Result -> Use task.data as the input sequence.

Examples

>>> import brainpy.math as bm
>>> from canns.models.basic import CANN2D
>>> from canns.task.tracking import PopulationCoding2D
>>>
>>> bm.set_dt(0.1)
>>> model = CANN2D(length=8)
>>> task = PopulationCoding2D(
...     cann_instance=model,
...     before_duration=1.0,
...     after_duration=1.0,
...     Iext=(0.0, 0.0),
...     duration=1.0,
...     time_step=bm.get_dt(),
... )
>>> task.get_data()
>>> task.data.shape[1:] == model.shape
True

Initializes the Population Coding task.

Parameters:

cann_instance (BaseCANN2D) – An instance of the 2D CANN model.
before_duration (float | Quantity) – Duration of the initial period with no stimulus.
after_duration (float | Quantity) – Duration of the final period with no stimulus.
Iext (float | Quantity) – The position of the external input during the stimulation period.
duration (float | Quantity) – The duration of the stimulation period.
time_step (float | Quantity, optional) – The simulation time step. Defaults to 0.1.

after_duration[source]¶

before_duration[source]¶

class canns.task.tracking.SmoothTracking1D(cann_instance, Iext, duration, time_step=0.1)[source]¶

Bases: SmoothTracking

Smooth tracking task for 1D continuous attractor networks.

The external input moves smoothly between key positions.

Workflow:: Setup -> Create a 1D CANN and the task. Execute -> Call get_data(). Result -> task.data contains the smoothly varying stimulus.

Examples

>>> import brainpy.math as bm
>>> from canns.models.basic import CANN1D
>>> from canns.task.tracking import SmoothTracking1D
>>>
>>> bm.set_dt(0.1)
>>> model = CANN1D(num=64)
>>> task = SmoothTracking1D(
...     cann_instance=model,
...     Iext=(0.0, 1.0, 0.5),
...     duration=(0.5, 0.5),
...     time_step=bm.get_dt(),
... )
>>> task.get_data()
>>> task.data.shape[0] == task.total_steps
True

Initializes the Smooth Tracking task.

Parameters:

cann_instance (BaseCANN1D) – An instance of the 1D CANN model.
Iext (Sequence[float | Quantity]) – A sequence of keypoint positions for the input.
duration (Sequence[float | Quantity]) – The duration of each segment of smooth movement.
time_step (float | Quantity, optional) – The simulation time step. Defaults to 0.1.

class canns.task.tracking.SmoothTracking2D(cann_instance, Iext, duration, time_step=0.1)[source]¶

Bases: SmoothTracking

Smooth tracking task for 2D continuous attractor networks.

The external 2D input moves smoothly between key positions.

Workflow:: Setup -> Create a 2D CANN and the task. Execute -> Call get_data(). Result -> task.data contains smoothly varying 2D inputs.

Examples

>>> import brainpy.math as bm
>>> from canns.models.basic import CANN2D
>>> from canns.task.tracking import SmoothTracking2D
>>>
>>> bm.set_dt(0.1)
>>> model = CANN2D(length=8)
>>> task = SmoothTracking2D(
...     cann_instance=model,
...     Iext=((0.0, 0.0), (1.0, 1.0), (0.5, 0.5)),
...     duration=(0.5, 0.5),
...     time_step=bm.get_dt(),
... )
>>> task.get_data()
>>> task.data.shape[1:] == model.shape
True

Initializes the Smooth Tracking task.

Parameters:

cann_instance (BaseCANN2D) – An instance of the 2D CANN model.
Iext (Sequence[tuple[float, float] | Quantity]) – A sequence of 2D keypoint positions for the input.
duration (Sequence[float | Quantity]) – The duration of each segment of smooth movement.
time_step (float | Quantity, optional) – The simulation time step. Defaults to 0.1.

class canns.task.tracking.TemplateMatching1D(cann_instance, Iext, duration, time_step=0.1)[source]¶

Bases: TemplateMatching

Template matching task for 1D continuous attractor networks.

A fixed stimulus template is presented with noise at each step, testing the network’s denoising dynamics.

Workflow:: Setup -> Create a 1D CANN and the task. Execute -> Call get_data(). Result -> Use task.data as the noisy input sequence.

Examples

>>> import brainpy.math as bm
>>> from canns.models.basic import CANN1D
>>> from canns.task.tracking import TemplateMatching1D
>>>
>>> bm.set_dt(0.1)
>>> model = CANN1D(num=64)
>>> task = TemplateMatching1D(
...     cann_instance=model,
...     Iext=0.0,
...     duration=1.0,
...     time_step=bm.get_dt(),
... )
>>> task.get_data()
>>> task.data.shape[1] == model.shape[0]
True

Initializes the Template Matching task.

Parameters:

cann_instance (BaseCANN1D) – An instance of the 1D CANN model.
Iext (float | Quantity) – The position of the external input.
duration (float | Quantity) – The duration for which the noisy stimulus is presented.
time_step (float | Quantity, optional) – The simulation time step. Defaults to 0.1.

class canns.task.tracking.TemplateMatching2D(cann_instance, Iext, duration, time_step=0.1)[source]¶

Bases: TemplateMatching

Template matching task for 2D continuous attractor networks.

A 2D template is presented with noise at each step.

Workflow:: Setup -> Create a 2D CANN and the task. Execute -> Call get_data(). Result -> task.data contains noisy 2D inputs.

Examples

>>> import brainpy.math as bm
>>> from canns.models.basic import CANN2D
>>> from canns.task.tracking import TemplateMatching2D
>>>
>>> bm.set_dt(0.1)
>>> model = CANN2D(length=8)
>>> task = TemplateMatching2D(
...     cann_instance=model,
...     Iext=(0.0, 0.0),
...     duration=1.0,
...     time_step=bm.get_dt(),
... )
>>> task.get_data()
>>> task.data.shape[1:] == model.shape
True

Initializes the Template Matching task.

Parameters:

cann_instance (BaseCANN2D) – An instance of the 2D CANN model.
Iext (tuple[float, float] | Quantity) – The 2D position of the external input.
duration (float | Quantity) – The duration for which the noisy stimulus is presented.
time_step (float | Quantity, optional) – The simulation time step. Defaults to 0.1.