Formation of temporal-feature maps by axonal propagation of synaptic learning

Computational maps are of central importance to a neuronal representation of the outside world. In a map, neighboring neurons respond to similar sensory features. A well studied example is the computational map of interaural time differences (ITDs), which is essential to sound localization in a variety of species and allows resolution of ITDs of the order of 10 μs. Nevertheless, it is unclear how such an orderly representation of temporal features arises. We address this problem by modeling the ontogenetic development of an ITD map in the laminar nucleus of the barn owl. We show how the owl's ITD map can emerge from a combined action of homosynaptic spike-based Hebbian learning and its propagation along the presynaptic axon. In spike-based Hebbian learning, synaptic strengths are modified according to the timing of pre- and postsynaptic action potentials. In unspecific axonal learning, a synapse's modification gives rise to a factor that propagates along the presynaptic axon and affects the properties of synapses at neighboring neurons. Our results indicate that both Hebbian learning and its presynaptic propagation are necessary for map formation in the laminar nucleus, but the latter can be orders of magnitude weaker than the former. We argue that the algorithm is important for the formation of computational maps, when, in particular, time plays a key role.

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.

Age-related learning deficits in transgenic mice expressing the 751-amino acid isoform of human beta-amyloid precursor protein.

The beta-amyloid precursor protein (beta-APP), from which the beta-A4 peptide is derived, is considered to be central to the pathogenesis of Alzheimer disease (AD). Transgenic mice expressing the 751-amino acid isoform of human beta-APP (beta-APP751) have been shown to develop early AD-like histopathology with diffuse deposits of beta-A4 and aberrant tau protein expression in the brain, particularly in the hippocampus, cortex, and amygdala. We now report that beta-APP751 transgenic mice exhibit age-dependent deficits in spatial learning in a water-maze task and in spontaneous alternation in a Y maze. These deficits were mild or absent in 6-month-old transgenic mice but were severe in 12-month-old transgenic mice compared to age-matched wild-type control mice. No other behavioral abnormalities were observed. These mice therefore model the progressive learning and memory impairment that is a cardinal feature of AD. These results provide evidence for a relationship between abnormal expression of beta-APP and cognitive impairments.