Dr Michael E. Rule
Background - Research - Publications Position: Research Associate E-mail: mer49 [at] cam.ac.uk Office Location: BN4-76Background
I studied computer science (and some biology), then did a neuroscience PhD with the Truccolo lab. Presently, I work in the lab of Tim O'Leary on motor control and homeostasis in neural population codes. [Profile on Google Scholar]Research Interests
Neuroscience studies how behaviour and conscious sensory experiences arise from neuronal activity. Prevailing dogma holds that our experiences and identities are encoded in patterns of neural activity and connections between neurons. The vision that we might one day 'upload' minds into computers hinges on this assumption, and current progress in building brain-machine interfaces seems to support it. New results, however, challenge this understanding: synaptic connections and neuronal responses reconfigure, even for fixed behaviours and sensory input. If neural connections and responses continually change, what is stable? The qualitative nature of conscious sensory experiences cannot be explained without considering how behaviour feeds back to affect those senses. Studying closed-loop sensorimotor control may therefore reveal how aspects of sensation and action remain stable despite reconfiguration within the brain.Publications
Rule, M.E., Loback, A.R., Raman, D.V., Driscoll, L.N., Harvey, C.D. and O'Leary, T., 2020. Stable task information from an unstable neural population. Elife, 9, p.e51121. [ more; PDF ] Rule, M.E., Sorbaro, M. and Hennig, M.H., 2020. Optimal encoding in stochastic latent-variable Models. Entropy, 22(7), p.714. [ more; PDF ] Rule, M.E., O’Leary, T. and Harvey, C.D., 2019. Causes and consequences of representational drift. Current opinion in neurobiology , 58 , pp.141-147. [ more; PDF ]Rule, M.E., Schnoerr, D., Hennig, M.H. and Sanguinetti, G., 2019. Neural field models for latent state inference: Application to large-scale neuronal recordings. PLoS computational biology, 15(11), p.e1007442. [ more; PDF ] Rule, M. and Sanguinetti, G., 2018. Autoregressive point processes as latent state-space models: A moment-closure approach to fluctuations and autocorrelations. Neural Computation, 30(10), pp.2757-2780. [ more; PDF ] Rule, M.E., Vargas-Irwin, C., Donoghue, J.P. and Truccolo, W., 2018. Phase reorganization leads to transient β-LFP spatial wave patterns in motor cortex during steady-state movement preparation. Journal of neurophysiology, 119(6), pp.2212-2228. [ more; PDF ] Heitmann, S., Rule, M., Truccolo, W. and Ermentrout, B., 2017. Optogenetic stimulation shifts the excitability of cerebral cortex from type I to type II: oscillation onset and wave propagation. PLoS computational biology, 13(1), p.e1005349. [ more; PDF ] Rule, M.E., Vargas-Irwin, C.E., Donoghue, J.P. and Truccolo, W., 2017. Dissociation between sustained single-neuron spiking and transient β-LFP oscillations in primate motor cortex. Journal of neurophysiology, 117(4), pp.1524-1543. [ more; PDF ] Rule, M. (2016). Collective neural dynamics in primate motor cortex. (Ph.D. Thesis) Brown University, Providence, Rhode Island. Available at Brown University Library, doi.org/10.7301/Z0KS6Q07. [ more; PDF ] Guler, S.D. and Rule, M.E., 2013, June. Invent-abling: enabling inventiveness through craft. In Proceedings of the 12th International Conference on Interaction Design and Children (pp. 368-371). [ more; PDF ] Rule, M.E., Vargas-Irwin, C., Donoghue, J.P. and Truccolo, W., 2015. Contribution of LFP dynamics to single-neuron spiking variability in motor cortex during movement execution. Frontiers in systems neuroscience, 9, p.89. [ more; PDF ] Rule, M., Stoffregen, M. and Ermentrout, B., 2011. A model for the origin and properties of flicker-induced geometric phosphenes. PLoS Comput Biol, 7(9), p.e1002158. [ more; PDF ] Nain, A.S., Chung, F., Rule, M., Jadlowiec, J.A., Campbell, P.G., Amon, C. and Sitti, M., 2007, April. Microrobotically fabricated biological scaffolds for tissue engineering. In Proceedings 2007 IEEE International Conference on Robotics and Automation (pp. 1918-1923). IEEE. [ more; PDF ]