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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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Using computational theory to constrain statistical models of neural data.

Scott W Linderman¹, Samuel J Gershman²

¹Department of Statistics, Columbia University, United States.

Current Opinion in Neurobiology

|July 22, 2017

Summary

Computational neuroscience benefits from linking theory and data. Integrating computational theories with neural recordings via Bayesian models enables rigorous testing and revision of scientific models.

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Area of Science:

Computational Neuroscience
Neuroscience
Machine Learning

Background:

Computational neuroscience traditionally uses distinct 'bottom-up' (data-driven) and 'top-down' (theory-driven) approaches.
These approaches are often pursued separately, limiting the integration of theoretical insights with empirical neural data.
A more unified framework is needed to bridge the gap between computational theories and neural recordings.

Purpose of the Study:

To advocate for a more integrated approach in computational neuroscience.
To demonstrate how Bayesian inference can link computational theories with neural data analysis.
To provide a statistically rigorous framework for testing, evaluating, and revising computational theories using empirical data.

Main Methods:

Utilizing a Bayesian perspective to frame computational theories as prior distributions for neural data.
Developing a probabilistic modeling framework to connect theoretical predictions with observed neural activity.
Reviewing existing literature and presenting a worked example using temporal difference learning and dopamine models.

Main Results:

The proposed framework facilitates a data-driven and statistically rigorous evaluation of computational theories.
Examples demonstrate the successful application of theory-driven pipelines in analyzing neural data.
The temporal difference learning model serves as a practical illustration of the integrated approach.

Conclusions:

Integrating 'top-down' computational theories with 'bottom-up' neural data analysis via Bayesian probabilistic models is essential.
This unified approach enhances the scientific rigor and data-driven revision of computational neuroscience models.
The presented framework offers a powerful tool for advancing our understanding of neural computation.