Professor of Biomedical Engineering
David Packard Chair of Engineering
Director, Center for Neural Engineering
Nonlinear systems analysis of hippocampal neuron/network properties, neural prostheses for cognitive/memory function, biologically-based signal processing.
Office: DRB 164
Phone: (213) 740-8017 / (213) 740-9361
Fax: (213) 740-6174
Dr. Berger is associated with the Biomimetic Microelectronic Systems (BMES)
, Biomedical Simulations Resource (BMSR)
, and Center for Neural Engineering (CNE)
Neurophysiology of memory and learning, nonlinear systems analysis of hippocampal network properties.
The research of Dr. T.W. Berger involves the complementary use of experimental and theoretical approaches to developing biologically constrained mathematical models of mammalian neural systems. The focus of the majority of current research is the hippocampus, a neural system essential for learning and memory functions. The goal of this research is to address three general issues: (1) the relation between cellular/molecular processes, systems-level functions, and learned behavior; (2) the extent of which the functional dynamics of neural systems are altered by activity-dependent synaptic plasticity; (3) the extent to which the essential functions of a neural system can be incorporated within a hardware representation (e.g., VLSI circuitry).
Experimental studies involve the use of extracellular, intracellular, and whole-cell electrophysiological recording techniques, applied in vivo using anesthetized and chronically implanted animals, and in vitro using hippocampal slice preparations. A number of neurobiological issues are being investigated, including: (1) quantifying the signal processing capabilities of hippocampal neurons and the extent to which these capabilities reflect regulation due to feedforward and feedback circuitry vs. intrinsic neuronal mechanisms, such as voltage-dependent conductances or second messenger biochemical systems; (2) the spatio-temporal distribution of activity in neural networks and its dependence on input pattern and network connectivity; (3) the cellular mechanisms underlying changes in the strength of connections among neurons, i.e., synaptic plasticity, and the influence of synaptic plasticity on signal processing characteristics of neurons and the spatio-temporal distributions of activity in networks.
These and other experimental studies are used in conjunction with several different theoretical approaches to develop models of: (1) the nonlinear, input/output properties of single hippocampal neurons and circuits composed of several populations of hippocampal neurons (in collaboration with Dr. V. Marmarelis, Biomedical Engineering, USC), (2) the hierarchical relationship between synaptic and neuronal events (in collaboration with Dr. G. Chauvet, Institute for Theoretical Biology, University of Angers, France), (3) the kinetic properties of glutamatergic receptor subtypes, and (4) adaptive properties expressed by the "hippocampal-like" neural networks implemented with analog VLSI technology (in collaboration with Dr. B. Sheu, Electrical Engineering, USC).