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

Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits

Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI) computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent's rule, which is related to the dimensionality of the circuit's interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent's rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks

Sources of heavy metals in the Western Bay of Izmit surface sediments

The study aimed to examine source apportionment of heavy metals of the surface sediments in the < 63 mu m size fraction. The sediment samples collected from 34 sites at the Western Bay of Izmit were subjected to a total digestion technique and analysed for major (total organic carbon, Al, Fe, Mg, and S) and trace (As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Sn, V, and Zn) elements by inductively coupled plasma-atomic emission spectrometry. The results were compared with the marine sediment quality standards, as well as literature values reported to assess the pollution status of the sediments. A factor analysis/multiple regression (FA/MR) multivariate receptor modelling technique was used for quantitative source apportionment to estimate the contributions from each source of contamination. Source fingerprints were obtained from the literature. A varimax rotated factor analysis was applied to the whole data set, and four probable source types were identified as the iron and steel industry, paint industry, crustal and sewage for heavy metals, explaining about 84% of the total variance. Source apportionment results derived from the FA and FA/MR methods agree well with each other

Characterization and selection of indigenous fruit variants in the Eastern Black Sea region (Turkey) for future breeding

The Turkish province of Rize, in the Eastern Black Sea Region, is very rich in natural diversity. Its districts are home to a wide range of landrace fruit species including whortleberry, Caucasian whortleberry, aromatic black grapes, apples, pears, tangerines, chestnuts, plums, and quinces. This study aimed to investigate the diversity of fruit trees and bushes in the province of Rize and determine the natural expansion area of various species. The survey also recorded morphological characterizations of existing genotypes and allowed for the propagation of interesting accessions for ex situ conservation. Trial plots were created for the preservation and curation of the local fruit varieties that were observed. Disease resistance, ease of vegetative propagation, and fruit quality of these accessions were also examin.Thanks to Recep Tayyip Erdogan University Scientific Research Projects Unit (Project No: RUBAP ?? ??. ? ??. ? ?. ?) for financial support