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Keck Seminar: Computational Design of Enhanced Learning Protocols
John H. “Jack” Byrne, PhD, Professor & Chair, Neurobiology & Anatomy, University of Texas Medical School
date:4:00PM   US Central (GMT −0500)
Friday, April 5, 2013
location:BRC Auditorium, BioScience Research Collaborative Building, Rice University
sponsor:Gulf Coast Consortia

In the fields of neuroscience and experimental psychology, multiple learning trials spaced over time generally produce long-term memory more effectively than a single trial or multiple trials massed together. However, virtually all of the learning protocols and their neuronal analogues used in animal and human studies have been developed on an ad hoc basis. The optimal procedure or spacing of trials is not predicted by any learning theory. The seminar will demonstrate the feasibility of using computational models of biochemical signaling in nerve cells to design enhanced training protocols that increase synaptic plasticity, transcriptional activation, and long-term memory. Moreover, the seminar will illustrate that a detailed understanding of biochemical signaling pathways underlying synaptic plasticity, as represented in a computational model, can facilitate the development of training protocols that can rescue deficits resulting from a molecular lesion.

The research interest of Dr. Byrne’s laboratory are the neuronal and molecular mechanisms underlying learning and memory. The marine mollusk Aplysia californica is being used as model system. In Aplysia they are studying mechanisms of implicit (nondeclarative) memory associated with simple forms of learning such as habituation, sensitization, classical or Pavlovian conditioning and operant conditioning. A variety of molecular, biochemical, biophysical electrophysiological and imaging techniques are used to analyze the properties of the neural circuits and the individual neurons. The empirical analyses are complemented with realistic mathematical modeling in order to determine the extent to which the observed processes and their interactions are sufficient to explain the behavior of the system.

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