Interactions between PsyNeuLink and MDF

Simple

ABCD

Python source | JSON | Reconstructed source

An example with four Nodes, as in other environments.

SimpleLinear

SimpleLinear-conditional

Python source | JSON | Reconstructed source

A three-Node example with Conditions.

SimpleLinear-timing

Python source | JSON | Reconstructed source

The same model as in SimpleLinear-conditional with Conditions for timeline scheduling. Note: these conditions are still not fully implemented by the scheduler.

Nested

Nested without scheduling

Python source | JSON | Reconstructed source

A model with several Nodes in two Graphs, one of which contains the other.

Nested with scheduling

Python source | JSON | Reconstructed source

A similar model as in Nested without scheduling with Conditions.

SimpleFN

Python source | JSON | Reconstructed source

An example with a single Node using the PsyNeuLink implementation of the FitzHugh–Nagumo model.

SimpleFN-timing

Python source | JSON | Reconstructed source

The same model as in SimpleFN with Conditions for timeline scheduling. Note: these conditions are still not fully implemented by the scheduler.

SimpleFN-conditional

Python source | JSON | Reconstructed source

The same model in SimpleFN with scheduling Conditions that mimic the behavior in SimpleFN-timing.

Stroop

Python source | JSON | Reconstructed source

A model representing the Stroop effect with conflict monitoring that uses Conditions.