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

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Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons.

Salvador Dura-Bernal^1,2, Beatriz Herrera³, Carmen Lupascu⁴

¹State University of New York (SUNY) Downstate Health Sciences University, Brooklyn, New York 11203 salvador.dura-bernal@downstate.edu.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience

|October 2, 2024

Summary

Large-scale mechanistic brain models integrate complex data to reveal neural mechanisms and disease biomarkers. These multiscale models advance our understanding of brain function and aid in developing new treatments.

Keywords:

biophysically detailed models brain circuits mechanistic models modeling morphologically detailed neurons multi-compartmental neuron models network models simulations

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Area of Science:

Computational Neuroscience
Systems Neuroscience
Neuroscience Modeling

Background:

Neuroscience research is increasingly data-driven, with large datasets on brain circuits.
Integrating and interpreting these complex datasets presents significant challenges.
Advances in supercomputing enable sophisticated, large-scale biophysical brain models.

Purpose of the Study:

To review recent advances in large-scale mechanistic modeling of brain circuits.
To highlight the integration of experimental data into detailed biophysical models.
To showcase the application of these models in understanding brain function and disease.

Main Methods:

Development of large-scale, mechanistic, multiscale biophysical circuit models.
Integration of diverse experimental data (circuit composition, connectivity, activity).
Simulation and analysis of neural activity across multiple scales (e.g., LFP, EEG/MEG).

Main Results:

Demonstrated examples of integrated models for various brain regions (hippocampus, cortex, thalamus).
Models accurately predict cellular and circuit mechanisms underlying experimental recordings.
Simulated biomarkers for neurological and psychiatric disorders (epilepsy, depression, schizophrenia, Parkinson's).

Conclusions:

Mechanistic multiscale models are crucial for integrating experimental data and advancing brain research.
These models provide insights into neural coding, behavior, disease mechanisms, and therapeutic interventions.
Cross-disciplinary collaboration between modelers, experimentalists, and clinicians is vital for future progress.