Dynamic entrainment: A deep learning and data-driven process approach for synchronization in the Hodgkin-Huxley model

Area of Science:

• Computational Neuroscience
• Dynamical Systems Theory
• Artificial Intelligence in Biology

Background:

• Resonance and synchronized rhythms are crucial for biological system function.
• Disruptions in rhythmic patterns are linked to neurological disorders such as Huntington's disease.
• The Hodgkin-Huxley model describes neuronal action potential propagation.

Purpose of the Study:

• To introduce a novel data-driven technique called dynamic entrainment.
• To leverage deep learning to sustain biological systems within their entrainment regimes.
• To validate the dynamic entrainment technique against the Hodgkin-Huxley model.

Main Methods:

• Utilized the Hodgkin-Huxley (HH) model for simulating neuronal activity.
• Developed a data-driven approach incorporating HH model outputs.
• Applied deep learning methodologies for dynamic entrainment.

Main Results:

• The dynamic entrainment technique was successfully implemented.
• The technique effectively sustained systems within their entrainment regime.
• Results from dynamic entrainment closely matched the outputs of the HH model.

Conclusions:

• Dynamic entrainment is a viable method for managing rhythmic phenomena in biological systems.
• This approach shows promise for studying and potentially treating neurological conditions.
• The findings validate the use of deep learning in conjunction with mechanistic models for biological system analysis.