Experience-dependent corticofugal adjustment of midbrain frequency map in bat auditory system

Recent studies of corticofugal modulation of auditory information processing indicate that cortical neurons mediate both a highly focused positive feedback to subcortical neurons “matched” in tuning to a particular acoustic parameter and a widespread lateral inhibition to “unmatched” subcortical neurons. This cortical function for the adjustment and improvement of subcortical information processing is called egocentric selection. Egocentric selection enhances the neural representation of frequently occurring signals in the central auditory system. For our present studies performed with the big brown bat (Eptesicus fuscus), we hypothesized that egocentric selection adjusts the frequency map of the inferior colliculus (IC) according to auditory experience based on associative learning. To test this hypothesis, we delivered acoustic stimuli paired with electric leg stimulation to the bat, because such paired stimuli allowed the animal to learn that the acoustic stimulus was behaviorally important and to make behavioral and neural adjustments based on the acquired importance of the acoustic stimulus. We found that acoustic stimulation alone evokes a change in the frequency map of the IC; that this change in the IC becomes greater when the acoustic stimulation is made behaviorally relevant by pairing it with electrical stimulation; that the collicular change is mediated by the corticofugal system; and that the IC itself can sustain the change evoked by the corticofugal system for some time. Our data support the hypothesis.

A two-year experience teaching computer literacy to first-year medical students using skill-based cohorts

Because it is widely accepted that providing information online will play a major role in both the teaching and practice of medicine in the near future, a short formal course of instruction in computer skills was proposed for the incoming class of students entering medical school at the State University of New York at Stony Brook. The syllabus was developed on the basis of a set of expected outcomes, which was accepted by the dean of medicine and the curriculum committee for classes beginning in the fall of 1997. Prior to their arrival, students were asked to complete a self-assessment survey designed to elucidate their initial skill base; the returned surveys showed students to have computer skills ranging from complete novice to that of a systems engineer. The classes were taught during the first three weeks of the semester to groups of students separated on the basis of their knowledge of and comfort with computers. Areas covered included computer basics, e-mail management, MEDLINE, and Internet search tools. Each student received seven hours of hands-on training followed by a test. The syllabus and emphasis of the classes were tailored to the initial skill base but the final test was given at the same level to all students. Student participation, test scores, and course evaluations indicated that this noncredit program was successful in achieving an acceptable level of comfort in using a computer for almost all of the student body.

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.