Learning from Machines to Understand the Brain
We usually look to the brain to inspire how we build smarter, more efficient machines, but this project flips that idea. By learning from how machines stay efficient, we can uncover why the brain becomes inefficient in disease.
Every engineered system, from aircraft to computer networks, must balance energy use, information flow, fault tolerance, and maintenance to perform reliably. When these principles fail, systems overheat, signals become noisy, and performance breaks down. The same may hold true for the human brain.
This research treats the brain as a complex engineered system, exploring how it maintains efficiency in health and how this balance is lost in conditions such as motor neuron disease (ALS), Alzheimer’s, and schizophrenia. By combining insights from genetics, neuroimaging, and systems modelling, the project aims to define what an “efficient brain” looks like, and measure how different diseases drift away from that optimal state.
The ultimate goal is to identify universal principles of brain efficiency that can guide new ways to detect, predict, and potentially prevent brain disorders, transforming how we understand and protect brain health.