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 .
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 .
