Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations

Humans are able to form internal representations of the information they process—a capability which enables them to perform many different memory tasks. Therefore, the neural system has to learn somehow to represent aspects of the environmental situation; this process is assumed to be based on synaptic changes. The situations to be represented are various as for example different types of static patterns but also dynamic scenes. How are neural networks consisting of mutually connected neurons capable of performing such tasks? Here we propose a new neuronal structure for artificial neurons. This structure allows one to disentangle the dynamics of the recurrent connectivity from the dynamics induced by synaptic changes due to the learning processes. The error signal is computed locally within the individual neuron. Thus, online learning is possible without any additional structures. Recurrent neural networks equipped with these computational units cope with different memory tasks. Examples illustrate how information is extracted from environmental situations comprising fixed patterns to produce sustained activity and to deal with simple algebraic relations.     

Characterization of a native mRNA containing preinitiation complex from rabbit reticulocytes: RNA and protein constituents

From a rabbit reticulocyte postpolysomal supernatant a fraction has been isolated which is enriched in ribosomal particles sedimenting at 50S. This fraction is efficiently in vitro translated predominantly into α-globin. Besides the RNAs and proteins of the small ribosomal subunit the 50S particle contains α-globin mRNA and additional high molecular weight proteins, most of which correspond to polypeptides of the initiation factors eIF-2 and eIF-3. The 50S particle may represent a native [mRNA·40S·eIF′s·Met-tRNA_f·GTP] complex which may occur in vivo as a translatable intermediate in the initiation sequence.     

Linking habitat quality with genetic diversity: a lesson from great bustards in Spain

The effects of habitat loss and fragmentation on the genetic structure and variability of wild populations have received wide empirical support and theoretical formalization. By contrast, the effects of habitat quality seem largely underinvestigated, partly due to technical difficulties in properly assessing habitat quality. In this study, we combine geographic information system (GIS)-based habitat-quality modelling with a landscape genetics approach based on mitochondrial DNA markers to evaluate the possible influence of habitat quality on the levels and distribution of genetic diversity in a range of natural populations (n = 15) of Otis tarda throughout Spain. Ninety-three percent of the population represented by our countrywide sample lives in good-quality habitats, while 4.5% and 2.5% occur respectively in intermediate and poor habitats. Habitat quality was highly correlated with patch size, population size and population density, indicating the reliability and predictive power of the habitat suitability model. Genetic diversity was significantly correlated with habitat quality, size and density of the population, but not with patch size. Three of a total of 20 existing matrilineages from the species’ current genetic pool are restricted to poor-quality habitats. This study therefore highlights the importance of considering both population genetics and habitat quality in a species of high conservation priority.