Presented under the auspices of the DIMACS/BioMaPS/MB Center Special Focus on Information Processing in Biology.
This special focus is jointly sponsored by the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), the Biological, Mathematical, and Physical Sciences Interfaces Institute for Quantitative Biology (BioMaPS), and the Rutgers Center for Molecular Biophysics and Biophysical Chemistry (MB Center).
Many theories of brain function have been proposed over the last century but only in the last few years has it become feasible to record responses simultaneously from large enough numbers of neurons to put these theories to the test experimentally. The data-processing and quantitative methods traditionally used in neuroscience are not sophisticated enough to exploit this new flood of information. Fortunately, modern statistics and machine learning theory are making great strides in precisely the types of techniques needed to process the large multivariate data sets arising from the study of brain function. It is the purpose of this workshop to explore these methods.
In quantitative neuroscience research, a major area of interest lies in the study of how neuronal circuitries of the brain support its cognitive and functioning capacities. The goal is to provide rational, mechanistic explanations of cognitive functions at a descriptive level. A very promising area of cognitive faculties for scientific inquiry is memory, since it is a well-circumscribed term, can be studied in animals, and substantial knowledge has accumulated on the molecular mechanisms of synaptic plasticity.(See: Elgersma, Y, and Silva, A.J., ``Molecular mechanisms of synaptic plasticity and memory,'' Curr. Opin. Neurobiol., 9 (1999), 209-213.) In this workshop, we will concentrate on cognitive functions such as memory and discuss how, by applying newer analytic methods to the recorded data from neuronal circuitries, we can now test long-standing hypotheses about brain function. For instance, global optimization is being applied in an effort to find patterns in the vast amount of information being generated by neuroimaging and neurophysiological investigations. (See, e.g., Uutela, K., Hamalainen, M., Salmelin, R., ``Global optimization in the localization of neuromagnetic sources,'' IEEE Trans. on Biomedical Eng., 45 (1998), 716-723. Mathematical methods are needed to test things like the ``temporal coding hypothesis,'' which says that neurons encode information by the exact timing of spikes. ``Spike train dynamics predicts theta-related phase precession in hippocampal pyramidal cells,'' (See: Harris, K.D., Henze, D. A., Hirase, H, Leinekugel, X., Dragoi, G., Czurko, A. and Buzsaki, G., Nature, 417 (2002), 738-741.) describes temporal coding in which the timing of pyramidal cell spikes in the hippocampus relative to the theta rhythm shows a unidirectional forward ``precession'' during spatial behavior. Other related papers on similar topics are Harris, K.D., Csicsvari J., Hirase, H, Dragoi, G., and Buzsaki G., ``Organization of cell assemblies in the hippocampus,'' Nature, 424 (2003), 552-556. Advances in the fields of signal processing, nonlinear dynamics, statistics, and optimization theory, coupled with development of computers with huge storage capacity and lightening-like computational speed, have made it possible to try to attempt to test such hypotheses. The results could lead to understanding more about diseases such as epilepsy, sleep disorders, movement disorders, and cognitive disorders that affect millions of people every year.
Research breakthroughs have provided investigators with an unparalleled opportunity, but it opens up a new question: How do we go from the gigabytes of experimental data that we now have to concise conclusions about the function of the brain? We expect that the workshop will result in lively discussions of this question.