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Rudy Richardson

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Rudy Richardson is a Professor Emeritus of Environmental Health Sciences, in the School of Public Health and of Neurology and Toxicology in Michigan Medicine. He runs the computational molecular modeling lab and is certified by the American Board of Toxicology. He works on computational/predictive toxicology including computational studies on medicinal chemistry projects focused on discovery of therapeutic agents for Alzheimer’s disease and other neurodegenerative disorders. He remains fully active in research and selective mentoring of students and postdoctoral fellows.

Kamran Diba

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Kamran Diba is an Associate Professor in the Department of Anesthesiology in the School of Medicine. His research group is interested in how the brain computes, coordinates, stores and transfers information. Neuronal networks generate an assortment of neuronal oscillations that vary depending on the behavior and state of an animal, from active exploration to resting and different stages of sleep and anesthesia. Accordingly, in their recordings of large populations of spiking neurons in rodents, they observe state-dependent temporal relationships at multiple timescales. What role do these unique spike patterns play and what do they tell us about the function and limitations of each brain state? To answer these and related questions, they combine behavioral studies of freely moving, learning and exploring rats, multi-channel recordings of the simultaneous electrical (spiking) activity from hundreds of neurons during behavior and sleep, neural network models of this behavior, statistical and machine learning tools to uncover deep structure within high-dimensional spike trains and chemogenetics and optogenetics to manipulate protein signaling and action potentials in specific neural populations in precise time windows.

Omar Ahmed

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The Ahmed lab studies behavioral neural circuits and attempts to repair them when they go awry in neuropsychiatric disorders. Working with patients and with transgenic rodent models, we focus on how space, time and speed are encoded by the spatial navigation and memory circuits of the brain. We also focus on how these same circuits go wrong in addiction, epilepsy and traumatic brain injury.

In addition to electrophysiology in rodents and humans, we use imaging and photoactivation techniques to record and alter neuronal activity as rodents navigate custom-designed virtual reality environments. We also work on novel computational techniques to model and analyze our immensely large electrophysiology and imaging datasets to better understand how specific behaviors are encoded by neural circuits.

Dr. Ahmed received both his undergraduate and Ph.D. degrees in Neuroscience from Brown University. He then worked with epilepsy patients at Massachusetts General Hospital during his postdoctoral work, before joining the faculty at the University of Michigan as an Assistant Professor.

Polar plots showing the rhythmic phases of spikes fired by human neurons, revealing systematic variations across space and time.

Scott Lempka

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Scott Lempka is an Assistant Professor in the Department of Biomedical Engineering and director of the Neuromodulation Laboratory. The Neuromodulation Lab focuses on clinical neurostimulation (a.k.a. neuromodulation) therapies, such as spinal cord stimulation and deep brain stimulation. These therapies are used to treat a variety of neurological disorders, such as chronic pain and Parkinson’s disease. In these therapies, metal electrodes are used to apply electrical pulses that override pathological activity in the nervous system. The Neuromodulation Lab develops computer models of the electric fields generated by the stimulation and the direct neural response. These computer models are combined with clinical data, such as quantitative sensory testing and functional neuroimaging, to understand the effects of various therapies – why they work in some patients and not in others.

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Michal Zochowski

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Michal Zochowski is a Professor in the Departments of Physics and Biophysics Program. His research interests lie in the intersection of physics and neuroscience. His group focuses on understanding the mechanisms of the formation of spatio-temporal patterns in coupled dynamical systems, their applicability and role during information processing in the brain. They use theoretical and experimental approaches, including computational modeling of various brain processes including memory storage, consolidation and its retrieval.

Victoria Booth

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Victoria Booth is a Professor in the Department of Mathematics and an Associate Professor in the Department of Anesthesiology. Her interdisciplinary research in mathematical and computational neurosciences focuses on constructing and analyzing biophysical models of neurons and neural networks in order to quantitatively probe experimental hypothesis and provide experimentally-testable predictions. Her research provides continuous reciprocal interactions between modeling and experimental results.

Prof. Booth and her colleagues are constructing neurophysiologically based models of the neuronal networks and neurotransmitter interactions in the brainstem and the hypothalamus that regulate wake and sleep states. She is also addressing the question of the influence of intrinsic neuron properties and network topology on the generation of spatio-temporal activity patterns in large-scale neural networks.

Daniel Forger

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Daniel Forger is a Professor in the Department of Mathematics. He is devoted to understanding biological clocks. He uses techniques from many fields, including computer simulation, detailed mathematical modeling and mathematical analysis, to understand biological timekeeping. His research aims to generate predictions that can be experimentally verified.

Cynthia Chestek

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Cynthia Chestek is an Associate Professor of Biomedical Engineering, Electrical Engineering – Electrical and Computer Engineering Division, and the Neurosciences Graduate Program.

The Chestek lab focuses on brain machine interface (BMI) systems using 100 channel arrays implanted in motor and pre-motor cortex. The goal of this research is to eventually develop clinically viable systems to enable paralyzed individuals to control prosthetic limbs, as well as their own limbs using functional electrical stimulation and assistive exoskeletons. The lab apply a variety of machine learning algorithms to large-scale neural datasets obtained from spiking activity or field potentials in order to decode the motor commands. This is done both offline, and in real-time during experiments. Other computational challenges include mitigating non-stationarities in neural recordings over time. Over the next few decades, the size of these datasets is most likely to increase with the development of larger electrode arrays, and novel surgical techniques for implanting them.
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