Graduate Study in Neural Computational at RUTGERS-NEWARK
Mark A. Gluck
gluck at pavlov.rutgers.edu
Thu Nov 27 10:05:10 EST 1997
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Graduate Study in Neural Computation at Rutgers-Newark
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Students interested in doing graduate study and research in
COMPUTATIONAL NEUROSCIENCE and CONNECTIONIST MODELLING
IN COGNITIVE SCIENCE, should be aware of a growing strength at Rutgers
University-Newark in these areas. There are eight relevant faculty and
research scientists available for advising students: Ben Martin Bly,
Gyorgy Buzsaki, Mike Casey, Mark Gluck, Stephen Hanson, Catherine
Myers, Michael Recce, and Ralph Siegel. Further information on their
individual research interests and background is listed below.
The Rutgers-Newark campus has a special strength in the use of these models
as research tools when integrated with empirical studies of brain
and behavior. Information on the relevant graduate programs and sources
of further information are listed at the end of this email.
Research Interests of Key Faculty and Research Scientists
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BENJAMIN MARTIN BLY
Ph.D., Stanford, Cognitive Psychology, 1993
Email: ben at psychology.rutgers.edu
Web Page: http://psychology.rutgers.edu/~ben
Research Interests:
I want to understand how functional organization in the brain supports
complex human behavior and cognition, in particular language use and the
mental representation of conceptual information. To study the cerebral
basis of these phenomena, I use cognitive psychological methods to
investigate overt behavior that depends on language comprehension or
production, concept formation, or inductive reasoning, and I use
Magnetic Resonance Imaging (MRI) to measure behavior-dependent changes
in brain function. Using mathematical modeling methods, I explore the
consequences of these empirical results for theories of language
function and conceptual representation. The primary goal of this
research is to understand the physical basis of language and conceptual
knowledge. Such understanding has broad consequences both for the
scientific explanation of intelligent behavior and for the understanding
and treatment of brain injuries that affect language and cognition.
Selected Publications:
Martin B. (1994). The Schema. in "Complexity: metaphors, models, and
reality." Cowan G.A., Pines D., Meltzer D. (eds), Reading MA,
Addison-Wesley, p. 263-286
Schlaug G., Martin B., Thangaraj V., Edelman R.R, Warach S.J. (1996).
Functional anatomy of pitch perception and pitch memory in non-musicians
and musicians. NeuroImage 3(3):S318.
Siewert B., Bly B.M., Schlaug G., Thangaraj V., Warach S.J., Edelman
R.R. (1996). Comparing the BOLD and EPISTAR techniques for functional
brain imaging using Signal Detection Theory. The Journal of Magnetic
Resonance in Medicine 36:249-255.
Bly B.M., Kosslyn S.M. (1997). Functional anatomy of object recognition
in humans: evidence from PET and FMRI. Current Opinions in Neurology 10,
1:5-9.
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GYORGY BUZSAKI
M.D., University of Pecs, Hungary, 1974
Email: buzsaki at axon.rutgers.edu
Web page: http://osiris.rutgers.edu/buzsaki.html
Research Interests:
Neurobiology of learning and memory. Experimental approaches are two-fold.
The first is the study of axonal connectivity of hippocampal principal
cells and interneurons, characterized physiologically and filled in vivo,
with the explicit goal of a complete reconstruction of the true
connectivity of the hippocampus, to serve as building blocks for
computational models. A complementary approach uses large scale recordings
of neurons with silicon probes to reveal cooperative, emergent properties
of neuronal assemblies during behavior. Computational methods are used to
understand the complex interactions of neurons in real networks and
modeling oscillatory properties of interneuronal networks.
Selected Publications:
Buzsáki, G. A two-stage model of memory trace formation: A role for "noisy"
brain states. Neuroscience 31: 551-570, 1989.
Buzsáki, G. The hippocampo-neocortical dialogue. Cerebral Cortex 6: 81-92, 1996.
Buzsaki, G., Horvath, Z., Urioste, R., Hetke, J., Wise, K. High frequency
network oscillation in the hippocampus. Science 256: 1025-1027, 1992.
Jandó, G., Siegel, R. M., Horváth, Z., and Buzsáki, G. Pattern recognition
of the electroencephalogram by artificial neural networks.
Electroencephalography and clinical Neurophysiology 86: 100-109, 1993.
Sik, A., Ylinen, A., Penttonen, M., and Buzsaki, G. Inhibitory
CA1-CA3-hilar region feedback in the hippocampus. Science 264: 1722-1724,
1994.
Traub, R. D., Miles, R., and Buzsáki, G. Computer simulation of
carbachol-driven rhythmic population oscillations in the CA3 region of the
in vitro rat hippocampus. Journal of Physiology 451: 653-672, 1992.
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MIKE CASEY
Ph.D., UC, San Diego, Mathematics, 1995
Email: mcasey at psychology.rutgers.edu
Web Page: http://psychology.rutgers.edu/~mcasey
Research Interests:
My research interests are in the mathematical foundations of
cognitive science, and abstract physical models of intelligent
behavior. This interest is pursued through the study of
recurrent neural networks and other dynamical models of
cognitive and other neural processes. My current research is
focussed on dynamical models of abstract knowledge acquisition
and use.
Selected Publications:
Casey, M. (1996) "The Dynamics of Discrete-Time Computation, With
Application to Recurrent Neural Networks and Finite State Machine
Extraction," Neural Computation, 8:6, 1135-1178
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MARK GLUCK
Ph.D., Stanford University, Cognitive Psychology, 1987
Email: gluck at pavlov.rutgers.edu
Web Page: www.gluck.edu
Research Interests:
Neurobiology of learning and memory, with emphasis on the
role of the hippocampus in associative learning. Computational
models of conditioning and human learning. Experimental studies
include behavioral neuroscience studies of rabbit eyeblink
conditioning under various lesion and drug manipulations. Human
experimental research includes studies of conditioning, associative
learning, and categorization in normals, aged, and medial temporal
lobe amnesics. Applied work in neural networks for pattern
classification and novelty detection for mechanical fault diagnosis.
Selected Publications:
Gluck, M.A. & Myers, C. E. (1997). Psychobiological models of
hippocampal function in learning and memory. Annual Review
of Psychology. 48. 481-514.
Gluck, M. A., Ermita, B. R., Oliver, L. M., & Myers, C. E. (1996).
Extending models of hippocampal function in animal conditioning
to human amnesia. Memory.
Knowlton, B. J., Squire, L. R. , & Gluck, M. A. (1994). Probabilistic
category learning in amnesia. Learning and Memory. 1, 106-120.
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STEPHEN JOSE HANSON
Ph.D. Arizona State University, Experimental and Mathematical
Psychology, 1981
Email: jose at psychology.rutgers.edu,
Web Page: http://www-psych.rutgers.edu
Research Interests:
I am examining the following general aspects of connectionist learning
systems as they relate to human/animal cognition and learning. (1)
Learnability Theory: the effects of representation on learning, prior
knowledge on learning (trade-off), sample protocol on learning, presence of
noise and errors on learning. (2) Studies of Generalization: the effects of
sample size, ``pedagogy'' (ordering or organizing the training samples),
analyses of classification or categorization complexity, and the ability of
the learning system to correctly generalize. (3) Studies of ``Scaling'' and
complexity in task structure. Scaling involves ``realistic'' tasks that
possess
significant complexity. Scaling up the task may require increasing the
dimensionality of the task (e.g. inverse dynamics with realistic degrees of
freedom, 10 or 12), increasing task interactions (linguistic or language
constraints arising from syntax, semantics, discourse etc.), increasing
task memory requirements (as in grammar induction) or decreasing the
supervision of the algorithm as in reinforcement learning. (4) Studies of
Algorithms inspired by biophysical properties. Somehow the brain controls
the degrees of freedom in its representation language and is able
to induce complex ``rules'' for its conduct. What trick does it use? Are
there simple principles that relate cell growth, death, noise, locality,
parallelism, network topology, to seemingly more complex phenomena, like
language use, problem solving, and reasoning?
Selected Publications:
Hanson S. J. & Burr, D. J. (1990). What Connectionist Models Learn:
Toward a theory of representation in Connectionist Networks, Behavioral and
Brain Sciences, 13, 471-518.
Hanson, S. J.(1990). A Stochastic Version of the Delta Rule, PHYSICA
D,42, 265-272.
Hanson, S. J. (1991). Behavioral Diversity, Search, and Stochastic
Connectionist Systems, In Neural Network Models of Conditioning and Action,
M. Commons, S. Grossberg & J. Staddon (Eds.), New Jersey: Erlbaum.
Hanson, S. J., Petsche, T., Kearns, M. & Rivest, R. (1994), Computational
Learning Theory and Natural Learning
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CATHERINE MYERS
Ph.D., Imperial College, University of London,
Artificial Neural Networks, 1990
Email: myers at pavlov.rutgers.edu
Research Interests:
1. Computational Neuroscience: I am interested in building connectionist
models of brain regions involved in learning and memory. These models
are meant to capture functionality but also be consistent with known
anatomical and physiological constraints. In particular, I am concerned
with the role of the hippocampal region in associative learning, its
interaction with cerebellum, neocortex and amygdala, and its response to
various pharmacological manipulations, particularly cholinergic drugs.
2. Experimental Neuropsychology: Anterograde amnesia is a syndrome which
follows hippocampal-region damage via stroke, Alzheimer's Dementia and
other etiologies. I am interested in developing simple procedures to
determine what kinds of learning and memory survive such damage, and
whether this pattern matches the impairments seen in animal models. One
aim of this work is to develop discriminative/diagnostic procedures to
differentiate amnesic etiologies, as well as identifying locus of damage in
patients for whom neuroimaging is counterindicated.
3. Experimental Psychology: I focus on underlying representational
principles of learning and memory which may operate across many different
paradigms and response systems, including classical conditioning,
computer-based operant analogs of conditioning, and category learning.
Selected Publications:
Myers, C. & Gluck, M. (1994). Context, conditioning and hippocampal
re-representation. Behavioral Neuroscience, 108(5), 835-847.
Myers, C., Gluck, M. & Granger (1995). Dissociation of hippocampal and
entorhinal function in associative learning: A computational
approach. Psychobiology, 23(2), 116-138.
Myers, C., Ermita, B., Harris, K., Gluck, M. & Hasselmo, M. (1996). A
computational model of the effects of septohippocampal disruption
on classical eyeblink conditioning. Neurobiology of Learning
and Memory, 66, 51-66.
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MICHAEL RECCE
Ph.D. University College London, Neurophysiology, 1993
Email: recce at axon.rutgers.edu
Research Interests:
The spatial and memory function of the hippocampus and nearby brain
structures. This is investigated using a wide range of methods, including
neurophysiological recording from the hippocampus in freely moving rats and
evaluation spatial abilities of human subjects using virtual reality.
These
and other data are then used to construct computational models, and the
models are tested using computer simulation and on mobile robots.
Selected Publications:
Recce, M. and Harris, K.D. (1996) Memory for places: a navigational model
in support fo Marr's theory of hippocampal function. Hippocampus. vol 6:735-748.
Harris, K.D. and Recce, M. (1997) Absolute localization for a mobile robot
using place cells. Robotics and autonomous systems 658 p 1-13.
Hirase, H. and Recce, M. (1996) A search for the optimal thresholding
sequence in an associative memory. Network. vol 7. pp 741-756.
O'Keefe, J. and Recce, M. (1993) Phase relationship between hippocampal
place units and EEG theta rhythm. Hippocampus. vol 3. pp 317-330
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RALPH SIEGEL
Ph.D, McGill University, Physiology, 1985
Email: axon at cortex.rutgers.edu
Web Page: www.cmbn.rutgers.edu/cmbn/faculty/rsiegel.html
Research Interests:
We use a multidisciplinary approach to understand the physiology,
psychophysics, theory and neurology underlying visual perception. The
ultimate goal of this work is an understanding of the visual perceptual
process and application of this knowledge to assist persons who have
suffered neurological damage.
Selected Publications:
Read, H.L. and Siegel, R.M. Modulation of Responses to Optic Flow in Area 7a
by Retinotopic and Oculomotor Cues in Monkey. Cerebral Cortex. In press,
1997 PDF
Jando, G., Siegel, R.M., Horvath, Z. and Buzsaki, G., Pattern recognition
of the electroencephalogram by artificial neural networks, Electroenceph.
Clin. Neurophysiol. 86: 100-109 (1993).
Siegel, R.M., Tresser, C and Zettler, G., A coding problem in dynamics and
number theory. Chaos 2:473-494 (1992).
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INFORMATION ON GRADUATE PROGRAMS:
There are two graduate program appropriate for the study of neural
computation and connectionist modelling, depending on whether the
students are more oriented towards brain (Neuroscience) or behavior
(Psychology/Cognitive Science). Regardless of which graduate program
students choose, they are free to take classes from, and do research with,
faculty from both programs. There is an extensive computational
infrastructure to support computational students in both programs,
supported by a recent University strategic initiative in computational
neuroscience.
NEUROSCIENCE OPTION:
Students whose interests are oriented towards neuroscience, including
the study of basic molecular, cellular, systems, behavioral, and cognitive
neuroscience, should apply to the BEHAVIORAL AND NEURAL SCIENCES
graduate program at Rutgers-Newark.
For more info, see the web page: www.bns.rutgers.edu
For admissions applications, email: bns at cortex.rutgers.edu
PSYCHOLOGY/COGNITIVE SCIENCE OPTION:
Students whose interests are oriented towards behavior, including
cognitive psychology, cognitive science, cognitive neuroscience,
animal behavior, linguistics, and philosophy, should
apply to the PSYCHOLOGY/COGNITIVE SCIENCE program at Rutgers-Newark.
For more info, see the web page: www-psych.rutgers.edu
For admissions applications, email: cogsci at psychology.rutgers.edu
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