attractor-metadynamics-neural

Slow adaptation shapes neural attractor landscapes by gradually modifying neural dynamics to alter stable states. This framework provides a Python-based approach to simulate plasticity and visualize how attractor landscapes evolve during continuous learning.

You simulate attractor dynamics with meta-dynamics using the provided Python API. Running the example with default parameters visualizes attractor landscape evolution, offering a straightforward way to model neural dynamics and plasticity without complex setup.

Yes, you can analyze long-term memory stability in continuous learning scenarios. The framework supports evaluating how slow adaptation maintains stable neural attractor states over extended periods, helping assess memory retention within dynamical systems.

Visualizing neural attractor evolution during plasticity is best handled through a metadynamics framework. This Python-based tool simulates slow adaptation and generates visualizations of the changing attractor landscape, making the dynamical transformations explicit.

No specific dependencies are required to run the neural dynamics simulation. The framework is self-contained with a Python implementation, allowing you to simulate dynamics and visualize attractor evolution using the simple API provided.