Waves, bumps, and patterns in neural field theories

Neural field models of firing rate activity have had a major impact in helping to develop an understanding of the dynamics seen in brain slice preparations. These models typically take the form of integro-differential equations. Their non-local nature has led to the development of a set of analytical and numerical tools for the study of waves, bumps and patterns, based around natural extensions of those used for local differential equation models. In this paper we present a review of such techniques and show how recent advances have opened the way for future studies of neural fields in both one and two dimensions that can incorporate realistic forms of axo-dendritic interactions and the slow intrinsic currents that underlie bursting behaviour in single neurons.

Genetic algorithm seeding of idiotypic networks for mobile-robot navigation

Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, preengineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one.

Toxicity of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in the developing male Wistar(Han) rat I: no decrease in epididymal sperm count after a single acute dose

It has been reported that fetal exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) causes defects in the male reproductive system of the rat. We set out to replicate and extend these effects using a robust experimental design. Groups of 75 (control vehicle) or 55 (50, 200 or 1000 ng of TCDD kg-1 bodyweight) female Wistar(Han) rats were exposed to TCDD on Gestational Day (GD) 15, then allowed to litter. The high dose group dams showed no sustained weight loss compared to control, but four animals had total litter loss. Pups in the high dose group showed reduced body weight up till day 21, and pups in the medium dose group showed reduced body weight in the first week post partum. Balano-preputial separation (BPS) was significantly delayed in the high dose group male offspring. There were no significant effects of treatment when the offspring were subjected to a functional observational battery, or mated with females to assess reproductive capability. 25 males per group were killed on post natal day (PND) 70, and ~60 animals per group (~30 for the high dose group) on PND120 to assess seminology and other endpoints. At PND120, the two highest dose groups showed a statistically significant elevation of sperm counts, compared to control; however, this effect was small (~30%), within the normal range of sperm counts for this strain of rat, was not reflected in testicular spermatid counts nor PND70 data, and is therefore postulated to have no biological significance. Although there was an increase in the proportion of abnormal sperm at PND70, seminology parameters were otherwise unremarkable. Testis weights in the high dose group were slightly decreased at PND 70 and 120, and at PND120, brain weights were decreased in the high dose group, liver to body weight ratios were increased for all three dose groups, with an increase in inflammatory cell foci in the epididymis in the high dose group. These data show that TCDD is a potent developmental toxin after exposure of the developing fetus, but that acute developmental exposure to TCDD on GD15 caused no decrease in sperm counts.

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

Background: Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. Results: We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. Conclusion: ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases.

Performance evaluation of DCA and SRC on a single bot detection

Malicious users try to compromise systems using new techniques. One of the recent techniques used by the attacker is to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These compromised machines are said to be infected with malicious software termed a “bot”. In this paper, we investigate the correlation of behavioural attributes such as keylogging and packet flooding behaviour to detect the existence of a single bot on a compromised machine by applying (1) Spearman’s rank correlation (SRC) algorithm and (2) the Dendritic Cell Algorithm (DCA). We also compare the output results generated from these two methods to the detection of a single bot. The results show that the DCA has a better performance in detecting malicious activities.