Abstract
A basic neurobiology-clinical trial paradigm motivates our use of constrained mathematical models and analysis of personalized human-derived brain organoids toward predicting clinical outcomes and safely developing new therapeutics. Physical constraints imposed on the brain can guide the analyses an interpretation of experimental data and the construction of mathematical models that attempt to make sense of how the brain works and how cognitive functions emerge. Development of these mathematical models for human-derived brain organoids offer an opportunity for testing new hypotheses about the human brain. When it comes to testing ideas about the brain that require a careful balance between experimental accessibility, manipulation, and complexity, in order to connect neurobiological details with higher level cognitive properties and clinical considerations, we argue that fundamental structure-function constraints applied to models of brain organoids offer a path forward. Moreover, we show these constraints appear in canonical and novel math models of neural activity and learning, and we make the case that constraint-based modeling and use of representations can bridge to machine learning for powerful mutual benefit.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Fixed a number of missing references. The text was not changed from the original version.
