Prof. Michael Stiber

My collaborators and I have been investigating information transfer across the synapses that connect nerve cells. This synaptic coding process is the functional unit of nervous systems, and as such the computational unit of neural networks. This does notmean that other issues aren't also important --- synaptic coding is necessary but not sufficient for understanding neural computation.

My methods have been based on the hypothesis that single neurons perform nontrivial operations, and that the codingof neural input to output can be understood if one views the neuron as a nonlinear dynamical system. This has involved collaboration with neuroscientists to obtain data from living cells. Realistic physiological models of neurons, and a suite of simulation and analysis tools for PCs and for Unix workstation running X Windows have been developed. These are used by computer scientists investigating models and physiologists analyzing data from living preparations.

This has also led to the development of an overall architectural plan for a bioinformatics system called 'Logos', which is intended to manage the entire data life cycle in a distributed environment. Unique aspects of Logos are the inclusion of experimental and analytic methods as data in their own right and incorporation of rule-based reasoning for automation of common data analysis operations.

My long-term research goal is to understand the computational principles underlying biological nervous system function for application to machine intelligence. Involved in this investigation include issues of computational neuroscience, artificial intelligence, neural networks, bioinformatics, nonlinear dynamics, robotics, scientific computing, scientific visualization, and collaborative computing.

In the nearer term, I wish to determine how neuron structural and behavioral complexity (small-scale dynamics) contributes to nervous system operation (large-scale behavior), such as in learning and sensorimotor systems.

My bioinformatics work (the 'Logos' system) would be of interest to any organization that collects a wide range of detailed information from spatially distributed sources. When fully implemented, Logos will provide the ability to synthesize integrated views of a situation from such data --- to answer 'big questions' using 'small data'. This is related to work in situation awareness and information fusion.