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Ted Berger - Research Description

Ted Berger - Research Description

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 currentresearch 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).

Selected Publications

Dimoka A, Courellis SH, Marmarelis VZ, Berger TW. - Modeling the Nonlinear Dynamic Interactions of Afferent Pathways in the Dentate Gyrus of the Hippocampus. - Ann Biomed Eng [ 2008 ] Feb 26; .

Dimoka A, Courellis SH, Gholmieh GI, Marmarelis VZ, Berger TW. - Modeling the nonlinear properties of the in vitro hippocampal perforant path-dentate system using multielectrode array technology. - IEEE Trans Biomed Eng [ 2008 ] Feb;55(2):693-702 .

Berger TW. - Brain-Computer Interfaces (BCIs). - J Neurosci Methods [ 2008 ] Jan 15;167(1):1 .

Song D, Chan RH, Marmarelis VZ, Hampson RE, Deadwyler SA, Berger TW. - Statistical selection of multiple-input multiple-output nonlinear dynamic models of spike train transformation. - Conf Proc IEEE Eng Med Biol Soc [ 2007 ] 1:4727-30 .

Gholmieh GI, Courellis SH, Fluster D, Chen LS, Marmarelis VZ, Baudry M, Berger TW. - Improving bioassay sensitivity for neurotoxins detection using volterra based third order nonlinear analysis. - Conf Proc IEEE Eng Med Biol Soc [ 2007 ] 1:2261-4 .

Berger TW. - Real time acoustic event location and classification system with camera display. - J Acoust Soc Am [ 2007 ] Sep;122(3):1317 .

Soussou WV, Yoon GJ, Brinton RD, Berger TW. - Neuronal network morphology and electrophysiologyof hippocampal neurons cultured on surface-treated multielectrode arrays. - IEEE Trans Biomed Eng [ 2007 ] Jul;54(7):1309-20 .

Song D, Chan RH, Marmarelis VZ, Hampson RE, Deadwyler SA, Berger TW. - Nonlinear dynamic modeling of spike train transformations for hippocampal-cortical prostheses. - IEEE Trans Biomed Eng [ 2007 ] Jun;54(6 Pt 1):1053-66 .

Lu B, Yamada WM, Berger TW. - Nonlinear dynamic neural network for text-independent speaker identification using information theoretic learning technology. - Conf Proc IEEE Eng Med Biol Soc [ 2006 ] 1:2442-5 .

Courellis SH, Zanos TP, Hsiao MC, Hampson RE, Deadwyler SA, Marmarelis VZ, Berger TW. - Modeling hippocampal nonlinear dynamic transformations with principal dynamic modes. - Conf Proc IEEE Eng Med Biol Soc [ 2006 ] 1:2300-3 .

ScienceetVieOct06[2].doc