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Predicting slow and fast neuronal dynamics with machine learning.

Rosangela Follmann¹, Epaminondas Rosa²

¹School of Information Technology, Illinois State University, Normal, Illinois 61790, USA.

Chaos (Woodbury, N.Y.)

|November 30, 2019

Summary

This summary is machine-generated.

Reservoir computing accurately predicts periodic neuronal activity but only short-term predicts chaotic states. Predicted chaotic behavior still shows similarities to actual neuronal dynamics.

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Area of Science:

Computational Neuroscience
Machine Learning

Background:

Neuronal activity modeling is crucial for understanding brain function.
Predicting complex neuronal dynamics remains a challenge.

Purpose of the Study:

To apply reservoir computing for predicting Hindmarsh-Rose neuronal model activity.
To assess the accuracy of reservoir computing for periodic and chaotic neuronal states.

Main Methods:

Utilized reservoir computing, a machine learning technique.
Modeled neuronal activity using the Hindmarsh-Rose model.
Analyzed prediction accuracy for periodic (tonic, bursting) and chaotic states.

Main Results:

Accurate short- and long-term predictions for periodic neuronal behaviors.
Accurate short-term predictions for chaotic neuronal states.
Continued similarity between predicted and actual chaotic behavior after initial accuracy loss, supported by matching bifurcation diagrams.

Conclusions:

Reservoir computing shows promise for modeling neuronal activity.
The technique is highly effective for periodic dynamics.
Further investigation is needed for precise long-term chaotic state prediction.