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I am currently a Postdoctoral Research Fellow in the Division of Clinical and Computational Neuroscience at the Krembil Research Institute (formerly Toronto Western Research Institute), part of the University Hospital Network and affiliated with the University of Toronto.
As a Computational Neuroscientist, I work to apply my mathematical expertise to the study of neuroscience. My current research aims to apply computational techniques to the study of epilepsy and seizure initiation at micro-, meso-, and macro- scales. Check out the video on the left for an "elevator pitch" summary of my work!
Here you'll find links to my publications along with other details about me, including an up-to-date CV. You can learn more about the work of the labs of my co-supervisors, Dr. Frances Skinner, Dr. Taufik Valiante, and Dr. Jeremie Lefebvre, at their respective websites. If you are interested in learning more about my research, contact me via e-mail or on LinkedIn!
My research lies in the field of Computational Neuroscience with a focus on topics, both broad and specific, outlined below.
A diverse and vibrant field of research involving the application of interdisciplinary tools from fields including mathematics and computer science to the study of neuroscientific problems. All of my research, ranging from the "abstract" study of synchrony in simplified inhibitory neural networks to the "applied" study of the initiation of seizure in patients with epilepsy, falls under this broad banner.
The brain is made up of billions of individual units known as neurons, and a wealth of research indicates that information is processed and encoded in the brain not just by the activity of individual neurons, but in the more complex dynamics of networks of neurons. Computational neuroscience is uniquely situated to understand the mechanisms underlying these dynamics, which are often difficult to directly study.
One of the most important dynamics exhibited by neural networks is synchronous activity of a majority of the neurons in the network. This behavior appears to have a role both in necessary brain functions, such as memory formation and processing, and pathological brain activity, such as seizure. My doctoral research focused on studying how inhibitory interneurons can drive this dynamic in different types of networks.
My research has transitioned from studying synchrony in more "abstract" neural networks to studying an archetypal example of such activity in the brain, seizure initiation. This involves utilizing experimental data, from both rodents and humans, to inform and constrain the computational experiments, which ensures the results of this work have potential clinical applicability for future epilepsy research.
Scott Rich, Homeira Moradi Chameh, Vladislav Sekulic, Frances K. Skinner and Taufik A. Valiante. "Modeling reveals human-rodent differences in h-current kinetics in cortical layer 5 neurons." bioRxiv, 2020 (manuscript submitted to Cerebral Cortex).
Scott Rich, Homeira Moradi Chameh, Marjan Rafiee, Katie Ferguson, Frances K. Skinner and Taufik A. Valiante. "Inhibitory network bistability explains increased interneuronal activity prior to seizure onset." Frontiers in Neural Circuits, 13, 2020.
Homeira Moradi Chameh, Lihua Wang, Scott Rich, Liang Zhang, Peter L Carlen, Shreejoy Tripathy and Taufik A. Valiante. "Sag currents are a major contributor to human pyramidal cell intrinsic differences across cortical layers and between individuals." bioRxiv, 2019 (manuscript in review at Nature Communications).
Scott Rich, Michal Zochowski and Victoria Booth. "Effects of Neuromodulation on Excitatory-Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure.'' Journal of Nonlinear Science, 2018. DOI: 10.1007/s00332-017-9438-6
Scott Rich, Michal Zochowski and Victoria Booth. "Dichotomous dynamics in E-I networks with strongly and weakly intra-connected inhibitory neurons.'' Frontiers in Neural Circuits, 11, 2017. DOI: 10.3389/fncir.2017.00104
Scott Rich, Victoria Booth and Michal Zochowski. "Intrinsic cellular properties and connectivity density determine variable clustering patterns in randomly connected inhibitory neural networks.'' Frontiers in Neural Circuits, 10, 2016. DOI: 10.3389/fncir.2016.00082
Opinion pieces, blog posts, and other writings relating to academia
For additional details on topics such as my teaching experience, grants and awards, and research skills, please see my CV.
Last updated: June 23 2020
University of Michigan
2012 - 2018
PhD: Applied and Interdisciplinary Mathematics
Certificate: Computational Discovery and Engineering
2008 - 2012
Minors: Chemistry and Philosophy