Michael R. DeWeese, Ph.D. - US grants

Proposal No: 0749051
PI: Michael DeWeese

Award Abstract:

This award supports the preparation and sharing of computational neuroscience data as part of an exploratory activity aimed at catalyzing rapid and innovative advances in computational neuroscience and related fields. The data to be shared in this project are intracellular (whole-cell patch) recordings obtained in vivo from visual, auditory, somatosensory, and motor areas of the neocortex by the laboratories of Judith Hirsch, Anthony Zador, Michael DeWeese, and Michael Brecht. These data include not only spikes but also membrane voltages or currents generated by synaptic connections and intrinsic membrane channels. In addition to providing data, the investigators will develop tutorial materials describing recording methods, stimulus paradigms, and issues relevant to the interpretation of intracellular recordings. It is anticipated that this pooled data set will be useful for those wishing to study a particular sensory modality as well as those who hope to understand common features of neocortical function. It will also be of great value for the development of new methods of data analysis.

The ability to accurately model such complex phenomena as the natural scene statistics inherent in stacks of photographs or movies, or the collective behavior of hundreds of simultaneously recorded neurons in the cerebral cortex, would be transformative for our understanding of the natural world and of human thought. The insights gained would not only enhance our understanding of the brain and the sensory stimuli it can process, but they would confer practical advantages as well -- leading to improvements in automated speech recognition and meaningful analysis of real-time video, for example. The various data needed for these studies is coming online at a rapid pace, but these large and complex data sets defy traditional modeling and analysis techniques. Unfortunately, the complexity and size of many recently acquired corpora in biology, physics, and engineering domains render them incapable of being fit by powerful mathematical models unless they are constrained by strong and unjustified assumptions about the data. This, coupled with the general difficulty of developing general purpose machine learning algorithms has driven most contemporary scientists and engineers to focus on algorithms tailored to narrow problem spaces rather than tackling the more general machine learning problem. Fortunately, some researchers have continued to push for general learning algorithms with capabilities more similar to human intelligence, but they have typically had to rely on ad hoc assumptions or uncontrolled approximations in order to make progress on this daunting problem. This proposal is to further develop a recently introduced machine learning technique, called Minimum Probability Flow learning, so that it is capable of fitting exceedingly general parametric models to much larger data sets than has ever been possible before. In addition, this proposal is to develop novel, complimentary methods for sampling efficiently from a model distribution once the parameters have been fit to data, so that the models can be understood and meaningfully compared with one another. These techniques will be used to study the statistical structure of natural scenes by fitting a new and powerful mathematical model to a database consisting of a large number of photographs. The program proposed here is highly interdisciplinary, drawing ideas and approaches from physics, engineering, computer science, and systems neuroscience.

1265085 - DeWeese

The primary goal of this project is to develop a nanometer-scale electrophysiological probe capable of recording the electrical activity both in and around individual neurons in the intact brain. The probe will consist of a single carbon nanotube (CNT) or a small bundle of multiple CNTs, grown from a sharpened tungsten electrode. The device will be coated with Parylene, which will provide electrical insulation as well as structural support for the contact between the tungsten wire and the CNT. This probe will be the smallest passive neural recording electrode ever built, by far, opening doors to many previously impossible experiments and medical
applications.

Public abstract
This proposal will use nanotechnology to develop more sensitive electrodes for monitoring activity of nerve cells in the brain. This new technology will provide better information about neurological diseases and may lead to better treatment of such diseases.