src.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 src.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. In this task, a stimulus is presented for a specific duration, preceded and followed by periods of no stimulation, to test the network’s ability to form and maintain a memory bump.

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 src.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. In this task, a stimulus is presented for a specific duration, preceded and followed by periods of no stimulation, to test the network’s ability to form and maintain a memory bump.

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

Bases: SmoothTracking

Smooth tracking task for 1D continuous attractor networks. This task provides an external input that moves smoothly over time, testing the network’s ability to track a continuously changing stimulus.

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

Bases: SmoothTracking

Smooth tracking task for 2D continuous attractor networks. This task provides an external input that moves smoothly over time, testing the network’s ability to track a continuously changing stimulus.

Initializes the Smooth Tracking task.

Parameters:

cann_instance (BaseCANN2D) – An instance of the 2D 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 src.canns.task.tracking.TemplateMatching1D(cann_instance, Iext, duration, time_step=0.1)[source]¶

Bases: TemplateMatching

Template matching task for 1D continuous attractor networks. This task presents a stimulus with added noise to test the network’s ability to denoise the input and settle on the correct underlying pattern (template).

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

Bases: TemplateMatching

Template matching task for 2D continuous attractor networks. This task presents a stimulus with added noise to test the network’s ability to denoise the input and settle on the correct underlying pattern (template).

Initializes the Template Matching task.

Parameters:

cann_instance (BaseCANN2D) – An instance of the 2D 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.