Neural network evidence for the coupling of presaccadic visual remapping to predictive eye position updating

As we look around a scene, we perceive it as continuous and stable even though each saccadic eye movement changes the visual input to the retinas. How the brain achieves this perceptual stabilization is unknown, but a major hypothesis is that it relies on presaccadic remapping, a process in which neurons shift their visual sensitivity to a new location in the scene just before each saccade. This hypothesis is difficult to test in vivo because complete, selective inactivation of remapping is currently intractable. We tested it in silico with a hierarchical, sheet-based neural network model of the visual and oculomotor system. The model generated saccadic commands to move a video camera abruptly. Visual input from the camera and internal copies of the saccadic movement commands, or corollary discharge, converged at a map-level simulation of the frontal eye field (FEF), a primate brain area known to receive such inputs. FEF output was combined with eye position signals to yield a suitable coordinate frame for guiding arm movements of a robot. Our operational definition of perceptual stability was "useful stability,” quantified as continuously accurate pointing to a visual object despite camera saccades. During training, the emergence of useful stability was correlated tightly with the emergence of presaccadic remapping in the FEF. Remapping depended on corollary discharge but its timing was synchronized to the updating of eye position. When coupled to predictive eye position signals, remapping served to stabilize the target representation for continuously accurate pointing. Graded inactivations of pathways in the model replicated, and helped to interpret, previous in vivo experiments. The results support the hypothesis that visual stability requires presaccadic remapping, provide explanations for the function and timing of remapping, and offer testable hypotheses for in vivo studies. We conclude that remapping allows for seamless coordinate frame transformations and quick actions despite visual afferent lags. With visual remapping in place for behavior, it may be exploited for perceptual continuity.

The Impact of Prenatal Nicotine Exposure on Impulsivity and Neural Firing in the Medial Prefrontal Cortex

Prenatal nicotine exposure (PNE) is linked to a large number of psychiatric disorders, including attention deficit hyperactivity disorder (ADHD). Current literature suggests that core deficits observed in ADHD reflect abnormal inhibitory control governed by the prefrontal cortex (PFC) of the brain. The PFC is structurally altered by PNE, but it is still unclear how neural firing is affected during tasks that test behavioral inhibition, such as the stop-signal task, or if neural correlates related to inhibitory control are affected after PNE in awake behaving animals. To address these questions, we recorded from single medial PFC (mPFC) neurons in control rats and PNE rats as they performed our stopsignal task. We found that PNE rats were faster for all trial types and were less likely to inhibit the behavioral response on STOP trials. Neurons in mPFC fired more strongly on STOP trials and were correlated with accuracy and reaction time. Although the number of neurons exhibiting significant modulation during task performance did not differ between groups, overall activity in PNE was reduced. We conclude that PNE makes rats impulsive and reduces firing in mPFC neurons that carry signals related to response inhibition.

Human dendritic cell maturation and cytokine secretion upon stimulation with Bordetella pertussis filamentous haemagglutinin.

In addition to antibodies, Th1-type T cell responses are also important for long-lasting protection against pertussis. However, upon immunization with the current acellular vaccines, many children fail to induce Th1-type responses, potentially due to immunomodulatory effects of some vaccine antigens, such as filamentous haemagglutinin (FHA). We therefore analysed the ability of FHA to modulate immune functions of human monocyte-derived dendritic cells (MDDC). FHA was purified from pertussis toxin (PTX)-deficient or from PTX- and adenylate cyclase-deficient Bordetella pertussis strains, and residual endotoxin was neutralized with polymyxin B. FHA from both strains induced phenotypic maturation of human MDDC and cytokine secretion (IL-10, IL-12p40, IL-12p70, IL-23 and IL-6). To identify the FHA domains responsible for MDDC immunomodulation, MDDC were stimulated with FHA containing a Gly→Ala substitution at its RGD site (FHA-RAD) or with an 80-kDa N-terminal moiety of FHA (Fha44), containing its heparin-binding site. Whereas FHA-RAD induced maturation and cytokine production comparable to those of FHA, Fha44 did not induce IL-10 production, but maturated MDDC at least partially. Nevertheless, Fha44 induced the secretion of IL-12p40, IL-12p70, IL-23 and IL-6 by MDDC, albeit at lower levels than FHA. Thus, FHA can modulate MDDC responses in multiple ways, and IL-10 induction can be dissociated from the induction of other cytokines.

Stimulation of apolipoprotein D secretion by steroids coincides with inhibition of cell proliferation in human LNCaP prostate cancer cells

Although steroid hormones are known to play a predominant role in the regulation of cell growth in hormone-sensitive cancers, their mechanisms of action, especially their interaction with growth factors and/or growth inhibitors, is poorly understood. We have recently observed that the effects of androgens and estrogens on the expression of the major protein found in human breast gross cystic disease fluid, protein-24, are opposite to their respective action on cell proliferation in human breast cancer cell lines. Somewhat surprisingly, the recent elucidation of the amino acid sequence of this progesterone binding protein reveals that this tumor marker is apolipoprotein D (apo D), a member of a superfamily of lipophilic ligand carrier proteins. The present study was designed to determine whether apo D is secreted by human prostate cancer cells and could thus be a new marker of steroid action in these cancer cells, and whether the sex steroid-induced stimulation of apo D secretion coincides with inhibition of cell proliferation. We took advantage of the biphasic pattern of the effect of steroids on the proliferation of the human prostate cancer LNCaP cell line, which offers the opportunity to discriminate between positive and negative steroid receptor-regulated cell growth processes. A 10-day exposure to low concentrations of dihydrotestosterone and testosterone caused a potent stimulation of LNCaP cell proliferation, whereas incubation with higher concentrations of these androgens led to a progressive decrease in cell proliferation towards basal levels. The biphasic action of androgens was also observed on apo D secretion, the effects on apo D secretion being inversely related to their action on LNCaP cell proliferation. Similar opposite biphasic effects were also observed with 9 other steroids, thus indicating that the stimulation of secretion of this new biochemical marker coincides with inhibition of cell proliferation in LNCaP human prostatic cancer cells.

Upgrading automation for nuclear fuel in-core management: from the symbolic generation of configurations to the neural adaptation of heuristics

FUELCON is an expert system in nuclear engineering. Its task is optimized refueling-design, which is crucial to keep down operation costs at a plant. FUELCON proposes sets of alternative configurations of fuel-allocation; the fuel is positioned in a grid representing the core of a reactor. The practitioner of in-core fuel management uses FUELCON to generate a reasonably good configuration for the situation at hand. The domain expert, on the other hand, resorts to the system to test heuristics and discover new ones, for the task described above. Expert use involves a manual phase of revising the ruleset, based on performance during previous iterations in the same session. This paper is concerned with a new phase: the design of a neural component to carry out the revision automatically. Such an automated revision considers previous performance of the system and uses it for adaptation and learning better rules. The neural component is based on a particular schema for a symbolic to recurrent-analogue bridge, called NIPPL, and on the reinforcement learning of neural networks for the adaptation.

A novel neural/symbolic hybrid approach to heuristically optimized fuel allocation and automated revision of heuristics in nuclear engineering

FUELCON is an expert system for optimized refueling design in nuclear engineering. This task is crucial for keeping down operating costs at a plant without compromising safety. FUELCON proposes sets of alternative configurations of allocation of fuel assemblies that are each positioned in the planar grid of a horizontal section of a reactor core. Results are simulated, and an expert user can also use FUELCON to revise rulesets and improve on his or her heuristics. The successful completion of FUELCON led this research team into undertaking a panoply of sequel projects, of which we provide a meta-architectural comparative formal discussion. In this paper, we demonstrate a novel adaptive technique that learns the optimal allocation heuristic for the various cores. The algorithm is a hybrid of a fine-grained neural network and symbolic computation components. This hybrid architecture is sensitive enough to learn the particular characteristics of the ‘in-core fuel management problem’ at hand, and is powerful enough to use this information fully to automatically revise heuristics, thus improving upon those provided by a human expert.

Intelligent technologies for nuclear power systems: heuristic and neural tools

The consecutive, partly overlapping emergence of expert systems and then neural computation methods among intelligent technologies, is reflected in the evolving scene of their application to nuclear engineering. This paper provides a bird's eye view of the state of the application in the domain, along with a review of a particular task, the one perhaps economically more important: refueling design in nuclear power reactors.

Multicomponent gas-mixture measurements using an array of gas sensors and an artificial neural-network

An Electronic Nose is being jointly developed between the University of Greenwich and the Institute of Intelligent Machines to detect the gases given off from an oil filled transformer when it begins to break down. The gas sensors being used are very simple, consisting of a layer of Tin Oxide (SnO2) which is heated to approximately 640 K and the conductivity varies with the gas concentrations. Some of the shortcomings introduced by the commercial gas sensors available are being overcome by the use of an integrated array of gas sensors and the use of artificial neural networks which can be 'taught' to recognize when the gas contains several components. At present simulated results have achieved up to a 94% success rate of recognizing two component gases and future work will investigate alternative neural network configurations to maintain this success rate with practical measurements.

Artificial neural networks in modelling osmotic dehydration of foods

Artificial neural network (ANN) models for water loss (WL) and solid gain (SG) were evaluated as potential alternative to multiple linear regression (MLR) for osmotic dehydration of apple, banana and potato. The radial basis function (RBF) network with a Gaussian function was used in this study. The RBF employed the orthogonal least square learning method. When predictions of experimental data from MLR and ANN were compared, an agreement was found for ANN models than MLR models for SG than WL. The regression coefficient for determination (R2) for SG in MLR models was 0.31, and for ANN was 0.91. The R2 in MLR for WL was 0.89, whereas ANN was 0.84.Osmotic dehydration experiments found that the amount of WL and SG occurred in the following descending order: Golden Delicious apple > Cox apple > potato > banana. The effect of temperature and concentration of osmotic solution on WL and SG of the plant materials followed a descending order as: 55 > 40 > 32.2C and 70 > 60 > 50 > 40%, respectively.

Relating sardine recruitment in the Northern Benguela to satellite-derived sea surface height using a neural network pattern recognition approach

Processes of enrichment, concentration and retention are thought to be important for the successful recruitment of small pelagic fish in upwelling areas, but are difficult to measure. In this study, a novel approach is used to examine the role of spatio-temporal oceanographic variability on recruitment success of the Northern Benguela sardine Sardinops sagax. This approach applies a neural network pattern recognition technique, called a self-organising map (SOM), to a seven-year time series of satellite-derived sea level data. The Northern Benguela is characterised by quasi-perennial upwelling of cold, nutrient-rich water and is influenced by intrusions of warm, nutrient-poor Angola Current water from the north. In this paper, these processes are categorised in terms of their influence on recruitment success through the key ocean triad mechanisms of enrichment, concentration and retention. Moderate upwelling is seen as favourable for recruitment, whereas strong upwelling, weak upwelling and Angola Current intrusion appear detrimental to recruitment success. The SOM was used to identify characteristic patterns from sea level difference data and these were interpreted with the aid of sea surface temperature data. We found that the major oceanographic processes of upwelling and Angola Current intrusion dominated these patterns, allowing them to be partitioned into those representing recruitment favourable conditions and those representing adverse conditions for recruitment. A marginally significant relationship was found between the index of sardine recruitment and the frequency of recruitment favourable conditions (r super(2) = 0.61, p = 0.068, n = 6). Because larvae are vulnerable to environmental influences for a period of at least 50 days after spawning, the SOM was then used to identify windows of persistent favourable conditions lasting longer than 50 days, termed recruitment favourable periods (RFPs). The occurrence of RFPs was compared with back-calculated spawning dates for each cohort. Finally, a comparison of RFPs with the time of spawning and the index of recruitment showed that in years where there were 50 or more days of favourable conditions following spawning, good recruitment followed (Mann-Whitney U-test: p = 0.064, n = 6). These results show the value of the SOM technique for describing spatio-temporal variability in oceanographic processes. Variability in these processes appears to be an important factor influencing recruitment in the Northern Benguela sardine, although the available data time series is currently too short to be conclusive. Nonetheless, the analysis of satellite data, using a neural network pattern-recognition approach, provides a useful framework for investigating fisheries recruitment problems.

Automatic classification of field-collected dinoflagellates by artificial neural network

Automatic taxonomic categorisation of 23 species of dinoflagellates was demonstrated using field-collected specimens. These dinoflagellates have been responsible for the majority of toxic and noxious phytoplankton blooms which have occurred in the coastal waters of the European Union in recent years and make severe impact on the aquaculture industry. The performance by human 'expert' ecologists/taxonomists in identifying these species was compared to that achieved by 2 artificial neural network classifiers (multilayer perceptron and radial basis function networks) and 2 other statistical techniques, k-Nearest Neighbour and Quadratic Discriminant Analysis. The neural network classifiers outperform the classical statistical techniques. Over extended trials, the human experts averaged 85% while the radial basis network achieved a best performance of 83%, the multilayer perceptron 66%, k-Nearest Neighbour 60%, and the Quadratic Discriminant Analysis 56%.

Hormesis - The Stimulation Of Growth By Low-Levels Of Inhibitors

Hormesis is the name given to the stimulatory effects caused by low levels of potentially toxic agents. When this phenomenon was first identified it was called the Arndt-Schulz Law or Hueppe's Rule, because it was thought to occur generally. Although this generalisation is not accepted today, there has never been more evidence in its support, justifying a re-examination of the phenomenon. Evidence from the literature shows that not only has growth hormesis been observed in a range of taxa after exposure to a variety of agents, but also that the dose-response data have a consistent form. While there are a number of separate hypotheses to explain specific instances of hormesis, the evidence presented here suggests that different examples might have a common explanation, and the possibility of a general theory is considered.