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I am the Online Community Manager at PLoS ONE. My scientific specialty is chronobiology (circadian rhythms and photoperiodism), with additional interests in comparative physiology, animal behavior and evolution. I am not an MD so I cannot diagnose and treat your sleep problems. This is a personal blog and opinions within in no way reflect the policies of PLoS ONE. You can contact me at: Coturnix@gmail.com

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New and Exciting in PLoS Computational Biology and Genetics

Category: Science News
Posted on: September 7, 2007 8:57 AM, by Coturnix

A Balanced Memory Network:

A critical component of cognition is memory--the ability to store information, and to readily retrieve it on cue. Existing models postulate that recalled items are represented by self-sustained activity; that is, they are represented by activity that can exist in the absence of input. These models, however, are incomplete, in the sense that they do not explain two salient experimentally observed features of persistent activity: low firing rates and high neuronal variability. Here we propose a model that can explain both. The model makes two predictions: changes in synaptic weights during learning should be much smaller than the background weights, and the fraction of neurons selective for a memory should be above some threshold. Experimental confirmation of these predictions would provide strong support for the model, and constitute an important step toward a complete theory of memory storage and retrieval.

Adaptive Evolution of Conserved Noncoding Elements in Mammals:

Conservation of DNA sequences across evolutionary history is a highly informative signal for identifying regions with important biological functions. In particular, conserved noncoding regions have been shown to be good candidates for containing regulatory elements that have roles in gene regulation. Recent studies have found that there are many thousands of conserved noncoding elements (CNCs) in vertebrate genomes and have suggested possible functions for some of these elements, but the function of most CNCs remains unknown. To study the evolution of CNCs, we developed a statistical method to identify CNCs that show changes in evolutionary rates on particular branches of the mammalian phylogenetic tree. Those rate changes may indicate changes in the function of a CNC. We applied our method to CNCs of five mammalian genomes, and found that, indeed, many CNCs have experienced rate changes during their evolution. We also found a subset of CNCs showing accelerations in evolutionary rate that actually exceed the neutral rates, suggesting that adaptive evolution has shaped the evolution of those elements.

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