For Arthropod Mitochondria, Variety in the Genetic Code Is Standard

Abstract

PLoS Biology | www.plosbiology.org 0664 complex, refi ned, and specifi c responses to these signals-even though they inhabit anatomically similar structures. There also seems to be some crossover in job duties, with evidence that the functions of specialized areas can be assumed by other regions. How the different regions of the brain acquire their specialized functions remains an open question. Is specialization an inherent trait, with each cortical region following unique computational principles? Or does each region follow the same principles and learn its specialized tasks based on its different position and input? Theoretical neuroscientists investigate such questions by creating models to simulate the computational tasks performed by different brain structures. Such approaches have identifi ed statistical measures called "objective functions" that can describe the computational principles of the primary visual cortex, which processes signals from the retina. For example, a statistical property that optimizes sparse representations corresponds to neurons called simple cells, while optimally stable representations correspond to complex cells. Wyss et al. asked whether objective functions could also describe the computational principles that govern the integration of visual stimuli across cortical regions. To investigate this question, the researchers used a mobile robot programmed to navigate its environment while collecting visual inputs through a camera embedded in its circuitry. The camera provides ongoing inputs to the researcher's visual system model, which includes connections both within and between fi ve computational units in the visual hierarchy. The model includes an unsupervised learning algorithm to optimize the stability of visual representations in feed-forward connections in conjunction with ongoing independent neuron interactions within each level-representing local memory-simulating stimulus-driven learning. The feed-forward connections also show increasing convergence, akin to that reported in the primate visual pathway. How did the model respond to the robot-collected input? After nearly three days, all the computational levels achieved stable representations, with higher levels reaching stability only after lower levels had done so. At this point, the computational units exhibited selectivity in their response properties. Lower-level units responded to features visible from many different positions within the robot's environment and had large responsive areas that depended on the robot's orientation. Intermediate units responded to landmarks-particular views from a small region-and were highly selective for the robot's orientation. The higher units learned to link nearby landmarks, relying on small responsive regions. And the highest unit grouped these landmarks into a more complex system for representing external space-a place fi eld-which was highly dependent on the robot's position. The researchers used the responses of the different levels to reconstruct the position of the robot, and found that responses from the highest computational unit produced the most accurate reconstruction-in keeping with reconstructions based on the responses of rat hippocampal place cells. These results indicate that just a few general computational principles, temporal stability and local memory, can produce specialized functions in different cortical areas. Specialization is not an intrinsic feature of these cortical areas but comes from the complex visual properties of the environment. This model of functional organization likely applies to other sensory systems, Wyss et al. conclude. If it turns out that just a few computational principles underlie higher cognitive functions as well, a real-life Robby may not be so far-fetched after all. Wyss R, König P, Verschure PFMJ (2006) A model of the ventral visual system based on temporal stability and local memory. A mobile robot helped test a model of the ventral visual system based on two computational principles. Humans have never been known to tread lightly on the earth, but as our global reach has expanded so have our impacts on other species. Vanishing habitat caused by human activity is the number one threat to biodiversity, but the dispersal of alien invasive species-again, caused by humans-is not far behind. Over 4,500 non-native plant and animal species have established residence in the United States since European settlement, according to a 1993 report by the US Offi ce of Technology and Assessment. Many alien species cause little disturbance, while others radically transfi gure their new habitat by displacing less competitive native species and disrupting fragile ecological relationships that evolved over millions of years. Of a growing list of invasive plants in North America, garlic mustard (Alliaria petiolata) has been on the Nature Conservancy's Red Alert list since 2000. Originally found in Europe, it was planted in the late 1860s by European settlers for its medicinal and culinary properties. The weed has since spread from New York to Canada and 30 US states in the East and Midwest, with recent sightings as far west as Oregon. Many mechanisms have been proposed to explain the success of alien plant invasions, mostly related to the absence of natural predators or parasites or the disruption of long-established interactions among native organisms. Few studies, however, have directly tested these possibilities. In a new study, Kristina A. Stinson, John N. Klironomos, and colleagues do just that by investigating garlic mustard's effects on native hardwood North American trees. The weed gains a competitive advantage, they discovered, by releasing