Cortical activity pattern computation

Neural computations are modelled in various ways, but still there is no clear understanding of how the brain performs its computational tasks. This paper presents new results about analysis of neural processes in terms of activity pattern computations. It is shown that it is possible to extract from high-resolution EEG data a first order Markov approximation of a neural communication system employing pattern computations, which is significantly different from similar purely random systems. In our view this result shows that it is likely that neural activity patterns measurable at the macro-level by EEG are correlated with underlying neural computations.     

Neural correlations, population coding and computation

How the brain encodes information in population activity, and how it combines and manipulates that activity as it carries out computations, are questions that lie at the heart of systems neuroscience. During the past decade, with the advent of multi-electrode recording and improved theoretical models, these questions have begun to yield answers. However, a complete understanding of neuronal variability, and, in particular, how it affects population codes, is missing. This is because variability in the brain is typically correlated, and although the exact effects of these correlations are not known, it is known that they can be large. Here, we review studies that address the interaction between neuronal noise and population codes, and discuss their implications for population coding in general.     

Evolutionary computation method for pattern recognition of cis-acting sites

This paper develops an evolutionary method that learns inductively to recognize the makeup and the position of very short consensus sequences, cis-acting sites, which are a typical feature of promoters in genomes. The method combines a Finite State Automata (FSA) and Genetic Programming (GP) to discover candidate promoter sequences in primary sequence data. An experiment measures the success of the method for promoter prediction in the human genome. This class of method can take large base pair jumps and this may enable it to process very long genomic sequences to discover gene specific cis-acting sites, and genes which are regulated together.     

Water relations pattern of understorey species influenced by sunflecks

Water relations of plant cenopopulations of understorey species (7 herbaceous perennials, 1 shrub, and 1 tree seedling) growing in an oak-hornbeam forest in SW. Slovakia is markedly influenced by natural sunflecks moving on the forest floor, where stomatal conductance and transpiration rate are significantly higher than in shade areas. In the course of a day, the leaf stomatal conductance fluctuates, following irradiance of the leaf, from minimum in full shade to maximum in sunflecks, including intermediate values in transient conditions. Therefore, there is also a large variability in water saturation deficit and transpiration rate within the cenopopulations of the understorey species. These variations should be considered in modelling the physiology of woodland understorey species.     

Evaluating empirical scaling relations of pattern metrics with simulated landscapes

Landscape patterns demonstrate scale-dependent properties that have been parsimoniously described by empirical scaling functions. These functions, derived from multiple-scale analysis of real landscapes, are evaluated here for their generality and robustness via a series of simulated landscapes with known landscape patterns. A factorial design was used to generate these landscapes, varying the number of classes, class abundance distribution, and patch dispersion. The results confirm that the three types of scaling relations were both general and robust. Type I metrics were predictable with simple scaling functions (e.g. power laws or linear functions); Type II metrics showed stair-case like response patterns and were essentially not predictable; Type III metrics exhibited erratic response patterns that were unpredictable in most cases. However, significant differences were found between real and simulated landscapes when landscape extent was increased. Systematic changes in grain size show that the predictability of scaling relations increases with the number of classes, the evenness of class abundance distribution, and the aggregation of patch dispersion. However, random patch dispersion seemed to enhance the predictability of scaling relations when changing spatial extent.     

Neural network modeling of batch cell growth pattern

The capability of neural networks in modeling batch cell growth by providing initial conditions only is tested in this study. The neural network tested is of the back-propagation-type including a newly discovered saturation-type transfer function. The simulation and prediction results of this neural network modeling will be demonstrated. (c) 1993 John Wiley & Sons, Inc.     

A geometric model for the cortical folding pattern of simple folded brains

A model for the folding of the cerebral cortex is described which effects a direct link between the geometry of the brain and its folding pattern. Using some simple approximations to the brain's geometry the model is able to predict folding patterns. As the model is based on an assumption of uniformity of the cortex, the success of its application tends to lend support to homogeneous mechanical models of folding.     

Combining geometric morphometrics with pattern recognition for the investigation of species complexes

The parasitoid Hymenoptera contain a large number of species complexes that are as yet unresolved by traditional taxonomic studies. Evolutionary studies as well as biological control programmes often require further investigations which cannot rely on simple qualitative morphological characters; in many cases, particularly with dried specimens housed in museums, molecular approaches cannot be used. Recent developments in geometric morphometrics and statistical exploratory approaches open new perspectives for the objective evaluation of morphological characters in this taxonomic context. In this study, geometric morphometrics and pattern recognition approaches were applied to the wing shape and venation of two closely related braconid species considered to differ by subtle qualitative morphological head characters. Exploratory analyses such as kernel density estimates and Gaussian mixture analyses were used to explore the structure of the data in the multivariate morphometric space. Discrimination techniques (linear discriminant functions and neural networks combined with cross‐validations) were used to estimate the taxonomic value of qualitative characters. Gaussian mixtures highlighted the existence of two non‐overlapping groups. A good congruence was found between one of the two groups and the a priori defined Bassus tumidulus. The misclassification rate was higher for B. tegularis specimens, which also appeared morphometrically heterogeneous. Discrimination between the two a priori defined species was incomplete with misclassification rates higher than, or equal to, 6%. In most cases, the lack of congruence between species and morphometrically defined subgroups could be related to specimens that exhibited ambiguous qualitative character states. In summary, if two entities are present, they still need to be defined morphologically, while B. tegularis heterogeneity calls for further investigation of specimens of known origin and hosts. © 2003 The Linnean Society of London, Biological Journal of the Linnean Society, 2003, 80, 89–98.     

Exact computation of pattern probabilities in random sequences generated by Markov chains

Observed patterns in macromolecular sequences are often consideredas words and compared with their probabilities of occurringin random sequences. Calculation of these probabilities, however,often lacks rigour. We have developed an algorithm for exactcomputation of such probabilities for stochastic sequences thatfollow a Markov chain model. The method is applicable to thecase that a random sequence contains one out of two given patternsP and Q, or both simultaneously. Another application yieldsthe probability Junction P(x) that a sequence contains patternP exactly x times. An application to patterns that include wild-cardcharacters yields probabilities for homonucleotide clustersof a given length. We prove the probability of multiple runsof single nucleotides in the SV40 genome to be in accordancewith the dinucleotide composition of the sequence, althoughit is in conflict with mononucleotide composition. Received on January 10, 1990; accepted on April 23, 1990     

The pattern and meaning of neighbor relations in high-rise housing in Israel

This paper deals with the behavioral pattern of neighbor relations and their relationships to the subjective attitudes and expectations of the residents. The sample was 318 middle-class women living in eight-to 20-story buildings. The findings indicate that respondents so desiring were able to develop active social ties with their neighbors. Moreover, they interacted with neighbors despite the fact that the majority had opportunities for alternative social relations. The distinction between localized and nonlocalized high-rise residents does not seem meaningful in this case. Actual social ties with neighbors were related to norms and expectations regarding neighbor relations. Despite active neighboring, respondents did not have difficulties obtaining privacy.     

Rheumatic fever susceptibility in four ascertainments: regressive segregation on a geometric ascertainment pattern

On resolving the ascertainment biases of the observed data in the geometric continuum vaffected-1 x P(sibship), where 0 less than v----infinity, four published ascertainments of rheumatic fever show excellent conformation with Mendelian recessive segregation, even in multiplex sibships. In two surveys in which ascertainment bias is near or a little above random sampling (v = 1), this conclusion is further corroborated by classical segregation analysis. The other two surveys have bias trends declining (v less than 1) very much below random sampling. Such levels of ascertainment bias, if defined through the ascertainment probability parameter pi, would be out of range because the range is from single ascertainment, where pi----0 to random sampling where pi = 1 and probability cannot exceed unity. Highly successful antimicrobial measures that would reduce the number of diseased sibs independent of the distribution of susceptible sibs could produce a dissociation of the gene-to-"rheumatic" relationship and thus explains the declining ascertainment bias.     

The ramification pattern of amacrine cells within the inner plexiform layer of the carp retina

Summary The morphology of amacrine cells in the retina of the carp is described using the Golgi technique. The ramification pattern of these cells was analyzed in flat-mounts of retinas. Based on these observations classification into five groups was made. Cells possessing one principal process leaving the soma were subdivided into starburst A-neurons and radiate neurons. Cells having two or more principal processes were subdivided into starburst B-neurons and spindle-shaped soma neurons. Small, diffuse amacrine cells form the fifth group. With respect to the shape of the field of arborization, the following cell types could be distinguished: (i) uniform cells, (ii) cells with a preferential direction, and (iii) cells with a marked edge, i.e., cells that lack processes in one direction. The latter form rarely occurs among starburst neurons; most of the spindle-shaped soma cells possess processes with a preferred direction, and cells with a marked edge are mainly found among the radiate neurons.All five cell types are found throughout the retina. The size of the cells varies within each group, and there is no correlation between size and distance from the optic nerve.The radial arborization pattern of each cell was examined in serial transverse sections. Starburst A-neurons ramify in the middle of the inner plexiform layer (IPL), radiate neurons in the inner half, and spindle-shaped soma neurons without overlapping processes (type B) as well as starburst B-neurons in the outer half. The ramification can be monostratified (narrow or broad), bistratified or multistratified. Small, diffuse amacrine cells and spindle-shaped soma neurons with overlapping processes (type A) ramify throughout the entire IPL.This work was supported by the Deutsche Forschungsgemeinschaft