Learning, Memory, and the Role of Neural Network Architecture

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems

Genetic diversity of noroviruses in Brazil

Norovirus (NoV) infections are a major cause of acute gastroenteritis outbreaks around the world. In Brazil, the surveillance system for acute diarrhoea does not include the diagnosis of NoV, precluding the ability to assess its impact on public health. The present study assessed the circulation of NoV genotypes in different Brazilian states by partial nucleotide sequencing analysis of the genomic region coding for the major capsid viral protein. NoV genogroup II genotype 4 (GII.4) was the prevalent (78%) followed by GII.6, GII.7, GII.12, GII.16 and GII.17, demonstrating the great diversity of NoV genotypes circulating in Brazil. Thus, this paper highlights the importance of a virological surveillance system to detect and characterize emerging strains of NoV and their spreading potential