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Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Generative models for network neuroscience: prospects and promise.

Richard F Betzel¹, Danielle S Bassett^2,3

¹Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.

Journal of the Royal Society, Interface

|December 1, 2017

Summary

Network generative models offer powerful insights into neural system organization and development. This review explores their application in network neuroscience, from basic principles to biological network analysis across species.

Keywords:

generative models graph theory network science neuroengineering

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Published on: May 31, 2017

Area of Science:

Network neuroscience
Computational neuroscience
Systems neuroscience

Background:

Network neuroscience investigates complex interconnections in neural systems.
Network generative modeling creates synthetic networks based on observed data.
These models help elucidate principles of neural organization and development.

Purpose of the Study:

To review the prospects and promise of generative models for network neuroscience.
To provide a primer on network generative models, their history, and utility.
To discuss practical applications, cross-validation, and biological network modeling.

Main Methods:

Algorithmic implementation of wiring rules to produce synthetic network architectures.
Review of existing literature on generative models in network science and neuroscience.
Analysis of generative models across cellular and large-scale biological neural networks.

Main Results:

Generative models can highlight organizational principles and developmental mechanisms.
Cross-validation is critical for the practical application of these models.
Models are reviewed across diverse species and network levels (e.g., *C. elegans* to human).

Conclusions:

Generative models are a promising approach for understanding neural systems.
Distinctions between model types (generative vs. null) and connectivity (functional vs. structural) are important.
Future directions involve enhanced data collection and methodological advancements.