Dr M Rule
BSc, PhD
Expertise
Dr. Rule's group explores the intersection of neuroscience and artificial intelligence, with specific focus on representational drift, biological cybernetics, neural data science, and mathematical theories of neural computation.
Current positions
Lecturer in Computational Neuroscience and Machine Learning
School of Engineering Mathematics and Technology
Contact
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Biography
Dr. Rule has seventeen years experience in academic research. Before joining Bristol, he spent four years as an independently-funded research PI role at the Control Group at the University of Cambridge. During this time, he advanced the mathematical and theoretical understanding representational drift — the process through which the neural representations of memories and learned skills changes over time without being forgotten.
Dr. Rule's academic background includes neural computation, computer science, mathematical biology, neuroscience, control theory, and machine learning (among other topics). His research has covered eclectic subjects such as mathematical models of visual hallucinations, colelctive dynamics in motor cortex, information theory in neural dynamics, and machine-learning methods for data science.
Dr. Rule has held a life-long dedication to computational neuroscience: At a young age, he was unsettled by the movement disorder symptoms he saw in a family member with neurological injury, and frustrated that medical science offered no clear mechanistic explanation. Since beginning his informal studies in 2003, he has dedicated his career to exploring a wide range of mathematical and theoretical approaches to understand neural computation and collective dynamics in neural systems.
Research interests
Dr. Rule's group studies computational neuroscience in the broad sense. Active research topics include:
- Representational drift and learning dynamics in hippocampus
- Mathematical models of spatiotemporal dynamics in the brain
- Biological cybernetics applied to adaptive, closed-loop sensorimotor control
The group applies mathematics and engineering principles to neurophysiology and neural computation, with two broad approaches:
- Experimental–theory collaborations to adapt emerging AI and ML tools to answer specific questions using large datasets, or model experimentally observed phenomena.
- Mathematical and computational studies to advance our understand of machine and biological learning, and the mathematics underlying machine learning and simulation methods applied to neuroscience.
The group has capacity to supervise up to 2 further Ph.D. students. Dedicated Ph.D. studentships are not currently available, but expressions of interest are welcome from externally funded doctoral students, and students currently accepted to the University of Bristol's Practice-Oriented Artificial Intelligence CDT and other funded doctoral training routes. Example topics might relate to, for example
- Mathematical and theoretical modelling of representational drift in ongoing learning — for a technically minded student interested in the AI/ML counterpart to ongoing neurophysiology collaborations;
- Control engineering principles applied to the pathophysiology of dystonias — for a creative and mathematically inclined student interested in the woolly particulars of our current understanding of neurophysiology.
The above are suggestive; new or collaborative topics are welcome. For a broader idea of topics that might be a good fit for the group, please see Dr. Rule 's Google scholar profile.
Proposals for new and opportune computational–experimental collaborations are always welcome.