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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

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Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based

Nicholas Tolley¹, Pedro L C Rodrigues², Alexandre Gramfort³

¹Department of Neuroscience, Brown University, Providence, Rhode Island, United States of America.

Plos Computational Biology

|February 26, 2024

Summary

This summary is machine-generated.

Simulation-based inference (SBI) now estimates parameters in complex neural models, overcoming challenges with likelihood-free Bayesian inference for time series data. This approach enables better understanding of neural dynamics in health and disease.

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Area of Science:

Computational neuroscience
Neuroscience
Biophysics

Background:

Biophysically detailed neural models are crucial for studying neural dynamics.
Parameter inference in these complex models remains a significant challenge.
Simulation-based inference (SBI) offers a promising, likelihood-free Bayesian approach.

Purpose of the Study:

To provide guidelines for applying SBI to estimate time series waveforms in large-scale biophysically detailed neural models.
To demonstrate SBI's application to common MEG/EEG waveforms using the Human Neocortical Neurosolver.
To establish methods for assessing the quality and uniqueness of parameter estimates.

Main Methods:

Utilized deep learning for density estimation within the SBI framework.
Applied SBI to infer parameters from simulated oscillatory and event-related potential data.
Employed diagnostic tools to evaluate posterior estimate quality.

Main Results:

Successfully applied SBI to estimate parameters for time series waveforms in detailed neural models.
Demonstrated SBI's utility with the Human Neocortical Neurosolver framework.
Provided methods for assessing the reliability of inferred neural model parameters.

Conclusions:

SBI offers a principled foundation for parameter inference in detailed neural models, particularly for time series data.
The presented guidelines facilitate the application of SBI to complex models for neuroscience research.
This work advances the use of detailed neural models for understanding neural dynamics in various conditions.