Gregory A. Clark, Ph.D.

Title
Associate Professor
Department
Department of Bioengineering
Institution
University of Utah
Address
20 South 2030 East
506 BPRB
City, State, Zip
Salt Lake City, UT 84112-9458
Phone
(801) 585-9796
E-mail
Greg.Clark[at]m.cc.utah.edu
Website
http://www.bioen.utah.edu/directory/profile.php?userID=23
Research Field
Neuroscience
Award Year
1989

Research

The nervous system exhibits an extraordinary capacity for plasticity, ranging from neuronal repair to information acquisition and storage. To identify the cellular and network-level mechanisms that confer these capabilities, this laboratory combines intracellular electrophysiological and computational approaches to investigate neuronal plasticity in two simple model systems, Aplysia and Hermissenda, which have large, identified neurons of known function. In brief, we characterize electrophysiological and molecular mechanisms underlying changes in synaptic strength, excitability, and other neuronal properties, and we explore potential methods for engineering neuronal change. We also develop biologically realistic computational models to determine how subcellular modifications produce changes expressed at the level of individual neurons, at the level of the entire neural network, and, ultimately, in behavior. Finally, in other work we are investigating whether bio-based strategies for functional electrical stimulation may lead to improved sensory and motor neuroprostheses.

One current line of investigation in Aplysia explores the question of synapse-specific plasticity and its underlying mechanisms. Because individual neurons can make synaptic contacts with thousands of other cells, synapse-specific plasticity is a potentially important property that could provide a highly precise means of modifying and repairing neural pathways, including re-establishing synaptic connections from regenerated axons preferentially onto appropriate postsynaptic target neurons. Our research indicates that changes in synaptic strength can be induced selectively at specific connections of a given neuron, relative to other connections of the same cell. These findings raise a number of intriguing questions regarding underlying mechanisms, especially in the case of long-term (> 24 hr) facilitation, which appears to depend on modifications of gene expression. In particular, how are new gene products used preferentially at facilitated synapses, compared with other synapses of the same cell?

We are also pursuing the role and mechanisms of synaptic plasticity in Hermissenda. One site of learning-related plasticity in Hermissenda is the eye, which serves as the conditioned stimulus pathway for light during training and testing. Because the eye is so simple (only 5 photoreceptors), we also have been able to develop biologically realistic computational models to explore the contribution of ion channel properties, synaptic strength, and connectivity to network function, in conjunction with our physiological investigations. We are currently focusing on the effects of subcellular-level neural noise, which paradoxically improves, rather than degrades, systems-level performance of the simulated network. One goal of these studies is to identify effective learning strategies utilized by biological nervous systems that may be profitably incorporated into artificial neural networks or neuroprostheses.