aind-behavior-curriculum 0.0.28

Generated from aind-library-template

aind-behavior-curriculum

A core problem in mice training is accurately keeping track of each mouse's training stage and accurately setting the corresponding rig parameters. As the number of behavior studies, research assistants, and mice increase, manual tracking and parameter input is prone to human error. This library provides a flexible framework for defining mice curriculum enabling mouse training to be automated.

Installation

pip install aind-behavior-curriculum

Documentation

Understanding a Curriculum

A Curriculum is structured as a graph of training Stages. Each Stage is associated with a Task, which is a set of rig parameters. Stages are connected by Stage Transitions, which are directed edges associated with a trigger condition.

Stages and Stage Transitions form the nodes and edges of a Curriculum graph, respectively. With this structure alone, a user can define a basic curriculum with the flexibility of defining skip connections and regressions. For nodes with multiple ongoing edges, edges are labelled by priority, set by the user.

An example curriculum consisting of purely stages and stage transitions. This Curriculum consists of a skip connection between Stage 'StageA' and Stage 'Graduated'. Stage Transitions are triggered on a parameter 't2' and the skip transition is ordered before the transition going to Stage StageB.

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This library also supports Curriculum hypergraphs.

Conceptually, a user may want to change the rig parameters associated with a stage, but this set of rig parameters would be unnatural to classify as a new training stage altogether. In this situation, the user may define a graph of Policies and Policy Transitions within a Stage. A Policy, changes the task parameters of a Stage, as described above. A Policy Transition acts just like a Stage Transition, and defines transitions between Policies on a trigger condition. Like Stage Transitions, Policy Transitions can connect any two arbitrary Policies and are ordered by priority set by the user.

An example Curriculum consisting of Stage and Policy graphs. Left: The high level policy graph. Right: Internal policy graphs.

Policies are more nuanced than Stages.

Yellow Policies in the example indicate 'Start Policies'. To initialize the rig parameters of a Stage, the user must specify which Policy/Policies in the Stage policy graph to start with.

Unlike Stages, a mouse can occupy multiple active Policies within a Stage. As described later, the Trainer will record the net combination of rig parameters.

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Any hypergraph is supported!

Here are some examples of the possibilities. The high-level stage graph are shown to the left and the inidividual policy graphs are shown to the right.

A 'Tree' Curriculum

A 'Train Track' Curriculum

A 'Policy Triangle' Curriculum

A 'Stage Triangle' Curriculum

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Understanding the Trainer

The Trainer is responsible for recording where a mouse is in its associated curriculum hypergraph. The Trainer contains 4 primary functions:

1. Registration: This is the entry point where the mice enter the system. Here, the user provides the Trainer with a mouse and associates the mouse with a curriculum, a start stage, and start policies as a starting place for evaluation.

2. Evaluation: For each registered mouse, the Trainer looks at the mouse's current position in its hypergraph curriculum. The Trainer collects all the current outgoing transitions and checks which evaluate to True. The Trainer determines the updated hypergraph position and associated Task parameters according to the following simple rules:

   • Trainer takes the outgoing Stage Transition with the highest priority. If multiple Stage Transitions evaluate to True, then the Stage Transition with the highest priority is chosen. Priority is set by the user.
   • Trainer takes the outgoing Policy Transition with the highest priority. If multiple Policy Transitions evaluate to True, then the Policy Transition with the highest priority is chosen. Priority is set by the user.
   • Stage Transitions override Policy Transitions. If a Stage Transition and Policy Transition both evaluate to True, the Trainer jumps directly to the next Stage .
   • If no transitions are True, the mouse stays in place.
   • For multiple active Policies that evaluate to True, Trainer sets the current Task parameters to the net combination of incident Policies.

3. Mouse Override: This allows the user to update a mouse's position manually to any position in its curriculum. Future evaluation occurs from this new position. Due to this feature, it is possible to design a Curriculum of 'floating stages' and 'floating policies'.

4. Mouse Eject: This allows the user to remove a mouse from its curriculum entirely. The position of the mouse is recorded as 'None' and stays at 'None' on future evaluation unless the mouse is overrides back onto curriculum.

Every Trainer function keeps a record of mouse history in SubjectHistory which can be referenced or exported for rig automation and further analysis.

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Building a Curriculum

For examples of how to build a Curriculum, please reference examples/example_project and examples/example_project_2 within the project files and their associated diagrams, examples/example_project/diagrams and examples/example_project_2/diagrams.

Tips for building your own Curriculum:

• Focus on one graph at a time. Define all the Tasks/Stages/Stage Transitions associated with the higher level graph, and then move onto defining the Policies/Policy Transitions associated with each Stage.

• Metrics contains all the variables that trigger conditions associated with Stage Transitions and Policy Transitions. Progressively add to Metrics as needed.

• Keep Stage Transitions and Policy Transitions simple. A typical transition will only trigger on one metric variable. This makes transitions much easier to name.

• Validate Stage Transition and Policy Transition priority with the Curriculum.export_digram(...) utility, which labels edges with its rank. Use Curriculum.set_stage_transition_priority(...) and Stage.set_policy_transition_priority(...) to reorder priority.

Common mistakes:

• Every Stage needs a set of start policies, see Curriculum.set_start_policies(...). If a stage with no policies is desired, use curriculum_utils.create_empty_stage(...). This is a common pattern for the final stage of a Curriculum, so the library also offers a prebuilt final stage curriculum_utils.GRADUATED.

• The callables in Policy and Policy Transition/Stage Transition have different input signatures. Please reference Policy.validate_rule(...) and PolicyTransition.validate_rule(...)/StageTransition.validate_rule(...)

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Building a Trainer

The 4 primary functions of the Trainer described above are decoupled from any database. To use the Trainer in practice, the user must define Trainer.load_data(...) and Trainer.write_data(...) which connect to a user's databases for mice curriculum, mice history, and mice metrics. Please see examples/example_project/trainer.py for an example.

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Inside Allen Institute of Neural Dynamics

Allen Institute of Neural Dynamics offers an internal repository template that automatically uploads the repository's curriculum to a central bucket available here: https://github.com/AllenNeuralDynamics/aind-behavior-curriculum-template This way, curriculums can be accessed across rig computers and reused/modified similar to Github commits.

As of (5/9/2024), a Metrics database has yet to be defined, therefore a Trainer cannot be defined.