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Modeling brain dynamics with spatial gradients


A core aim of neuroscience is to understand how the brain, with its staggering complexity of 100 billion neurons, makes sense of the world around it. Large-scale global initiatives have measured the brain in intricate detail, but have given limited physical understanding of how the brain’s microscopic properties shape the whole-brain dynamics that underlie cognition. In this project, we will develop, constrain, and validate a new generation of physiologically based brain models that tightly integrate large-scale neuroscience data.


Dr Ben Fulcher.

Research location

School of Physics

Program type



Mathematical models of brain dynamics, based on physiology and physics, have been formulated and refined over decades, but they remain disconnected from the modern neuroscience data that have recently become available to the community. This project will bridge this gap, exploiting the wealth of intricate microscale brain maps and using it to refine our best neural field models of brain dynamics. This should allow us to connect the macroscale patterns observed in neuroimaging experiments (such as fMRI and EEG) to principles governing the interactions between large populations of neurons at the microscale. Progress would have dramatic consequences for understanding the healthy brain, and for diagnosing and treating brain disorders.

Additional information

Excellent facilities are available to carry out all aspects of the work, including access to large-scale computing resources required for simulation experiments, and processed microscale datasets. The student should have a strong interest in mathematical modeling (with a quantitative background in e.g., physics, mathematics, statistics, engineering, or computer science) and enjoy working in an interdisciplinary team. Top-up funding is available for the highest quality of applicants, with additional funding available to support travel to present research results at national and international conferences and to visit collaborators. Additional Supervisor: Dr Mac Shine.

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Opportunity ID

The opportunity ID for this research opportunity is 2857

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