Research Divisions: Computer Science, Neuroscience, Psychiatry
Keywords: Computational neuroscience, machine learning, bioinformatics, neuroinformatics
In Dr. Hill’s work in the Blue Brain Project, he oversaw a team of scientists integrating morphological, electrophysiological and gene expression data into large-scale biophysically detailed computational models of neocortical microcircuitry, which are simulated, analyzed and visualized on a supercomputing infrastructure. He and his team have contributed to our understanding of the neocortical wiring diagram, principles underlying its robustness and the relationship between the local field potential and individual cellular activity.
Sheraz Cheema (Research Analyst)
Nelson Shen (Post-Doc Fellow)
Laura Sikstrom (Post-Doc Fellow)
Minfan Zhang (Student)
Marta Maslej (Post-Doc Fellow)
Most Significant Publications
1. Markram, H., Eilif Muller, Srikanth Ramaswamy et al. 2015. Reconstruction and Simulation of Neocortical Microcircuitry. Cell, Oct 8;163(2):456-92. doi: 10.1016/j.cell.2015.09.029. Joint senior author, responsible for design and implementation of project. The result of a 10-year study to create the most biophysically detailed model of cortical microcircuitry to date. This data-driven reconstruction of brain circuitry, simulated on a supercomputer, exhibits emergent structural and functional properties including network phenomena across different states corresponding to in vitro and in vivo-like conditions.
2. Reimann, M.W., Anastassiou, C.A., Perin, R., Hill, S.L., Markram, H., and Koch, C. A Biophysically Detailed Model of Neocortical Local Field Potentials Predicts the Critical Role of Active Membrane Currents. Neuron 79, no. 2 (July 24, 2013): 375–390. doi:10.1016/j.neuron.2013.05.023. Impact Factor: 13.97 Trainee Publication, supervised first author as trainee. Project lead. This study is the first computational model of the local field potential in brain circuitry generated from a biophysically detailed model of neocortex. The model enables a cellular and synaptic dissection of the local field potential and predicts the contribution of active dendritic conductances and specific cellular and synaptic currents during different network states.
3. Hill, S.L.*, Wang, Y.*, Riachi, I.*, Schürmann, F. and Markram, H. (2012) Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits. Proceedings of the National Academy of Sciences. doi: 10.1073/pnas.1202128109.Trainee Publication, supervised co-first author as trainee.*Joint first authorship. This paper documents the discovery of core structural principles underlying functional connectivity and apparent specificity in cortical microcircuitry as well as the role of the diversity of neuron morphologies in creating robust and invariant neural innervations.
4. Hill, S.L. and Tononi G. (2005) Modeling sleep and wakefulness in the thalamocortical system. Journal of Neurophysiology, Nov 10. 93(3): 1671-1698. - Featured in Nature Neuroscience Reviews Jan 2005. Impact Factor:2.89. First Author. The first large-scale computational model of the cat visual thalamocortical system which recreates multiscale electrophysiological activity during wakefulness and slow wave sleep.
5. Massimini M., Huber R., Ferrarelli F., Hill, S.L. and Tononi G. (2004) The sleep slow oscillation as a traveling wave. Journal of Neuroscience, Aug 4;24(31):6862-70. Impact Factor:2.89. Co-Author. This paper shows that the slow oscillation during sleep in humans propagates as a traveling wave throughout the brain.
Cross-Appointed to Physiology
Primary: Department of Psychiatry, University of Toronto
Scientific Director, Krembil Centre for Neuroinformatics
Senior Scientist, Centre for Addiction and Mental Health
Faculty Affiliate, Vector Institute
Titular Professor, École polytechnique fédérale de Lausanne, Switzerland