Shaoyun Chen | PubMed Health

PubMed2003

Quantitative analysis of axon collaterals of single cells in layer III of the piriform cortex of the guinea pig.

Recent physiological and morphological studies suggest that the piriform cortex (PC) functions like the association areas of the neocortex rather than the typical primary sensory area as was previously assumed. The axon connection patterns of single cells are important for understanding the functional organization of the PC. The axon collaterals of three single pyramidal cells and one spiny multipolar cell in layer III of the PC were labeled and quantitatively analyzed by intracellular injections of biocytin in guinea pigs. The individual pyramidal and spiny multipolar cells have highly distributed axon collaterals, which display little tendency for patchy concentrations, within the PC and multiple higher order behavior/reward/contextual-related areas, such as the prefrontal cortex, amygdaloid nuclei, and entorhinal cortex. For the pyramidal cells, the average length of axonal collaterals is 143 mm; the average number of boutons is 12,930. For the spiny multipolar cell, the length of the axonal collaterals is 88 mm; the number of boutons is 7,052. The pyramidal cells in the anterior subdivision of the PC (APC) have both rostrally and caudally directed intrinsic association fibers, whereas the pyramidal and spiny multipolar cells in the posterior subdivision (PPC) have predominantly caudally directed intrinsic association fibers in the PC. Our results reveal that the connection patterns of single cells in layer III resemble those of pyramidal cells in layer II, suggesting that the PC performs correlative functions analogous to those in the association area of other sensory systems. The rostrally-to-caudally directed connections in the APC provide a substrate for the recurrent process, whereas largely caudally directed connections in the PPC suggest the dominance of the feed-forward process.

Animals↗

PubMed1998

Vision-guided navigation using SHOSLIF.

This paper presents an unconventional approach to vision-guided autonomous navigation. The system recalls information about scenes and navigational experience using content-based retrieval from a visual database. To achieve a high applicability to various road types, we do not impose a priori scene features, such as road edges, that the system must use, but rather, the system automatically derives features from images during supervised learning. To accomplish this, the system uses principle component analysis and linear discriminant analysis to automatically derive the most expressive features (MEF) for scene reconstruction or the most discriminating features (MDF) for scene classification. These features best describe or classify the population of the scenes and approximate complex decision regions using piecewise linear boundaries up to a desired accuracy. A new self-organizing scheme called recursive partition tree (RPT) is used for automatic construction of a vision-and-control database, which quickly prunes the data set in the content-based search and results in a low time complexity of O(log(n)) for retrieval from a database of size n. The system combines principle component and linear discriminant analysis networks with a decision tree network. It has been tested on a mobile robot, Rome, in an unknown indoor environment to learn scenes and the associated navigation experience. In the performing phase, the mobile robot navigates autonomously in similar environments, while allowing the presence of scene perturbations such as the presence of passersby.

Journal Article↗