Control of neural chaos by synaptic noise

We study neural automata - or neurobiologically inspired cellular automata - which exhibits chaotic itinerancy among the different stored patterns or memories. This is a consequence of activity-dependent synaptic fluctuations, which continuously destabilize the attractor and induce irregular hopping to other possible attractors. The nature of these irregularities depends on the dynamic details, namely, on the intensity of the synaptic noise and the number of sites of the network, which are synchronously updated at each time step. Varying these factors, different regimes occur, ranging from regular to chaotic dynamics. As a result, and in absence of external agents, the chaotic behavior may turn regular after tuning the noise intensity. It is argued that a similar mechanism might be on the basis of self-controlling chaos in natural systems.     

神经前体细胞的迁移机制及其应用研究进展

神经前体细胞脑内移植后可在全脑迁移。在病变的中枢神经系统 ,神经前体细胞的迁移具有损伤区靶向性。神经前体细胞的迁移可能与中枢神经系统微环境中的神经导向分子以及神经损伤区释放的包括炎症因子在内的一些因子有关。神经前体细胞的靶向性迁移特性使其可作为运载治疗性分子的载体细胞 ,特异性治疗多发性中枢神经系统退行性病变和转移性脑肿瘤。同时 ,神经前体细胞还可以参与损伤区的神经修复和功能重建     

Designing a neural network simulator--the MENS modelling environment for network systems: II

During recent years, neural network research has been extendedto a large number of different fields, increasingly attractingthe interest of workers from various disciplines. The computersimulations carried out with this research require an appropriatesoftware environment. The computational similarities of manykinds of simulations allow the design of software componentsthat are largely independent of the specific application. Theseconsiderations are reflected, for example, by the general layoutof the MENS network simulator, as described in the accompanyingfirst paper. This paper presents the design considerations forthe simulator's different software components in more detail.In particular, design and implementation are discussed withrespect to computational and memory efficiency. The discussionincludes, for example, the representation of a network by thesimulator's data structure, the file-driven configuration andinitialization of a network, the simulator's stimulus and monitorsystem, and the simulator's control structures. In addition,the separation and interaction of application-specific and application-independentsoftware components are addressed. Particular performance aspectscomprise the implementation of synaptic delays, the dynamicdeletion of synaptic links in network learning, and the preprocessingof stimulus films. In addition, some general aspects of simulatorperformance and testing are considered. The material presentedin this paper concerns both the development of new simulationsoftware and the efficient use of existing programs. Therefore,both the general user as well as the software designer may hopefullybenefit from this presentation.     

Mechanistic theory and modelling of complex food-web dynamics in Lake Constance

Mechanistic understanding of consumer-resource dynamics is critical to predicting the effects of global change on ecosystem structure, function and services. Such understanding is severely limited by mechanistic models' inability to reproduce the dynamics of multiple populations interacting in the field. We surpass this limitation here by extending general consumer-resource network theory to the complex dynamics of a specific ecosystem comprised by the seasonal biomass and production patterns in a pelagic food web of a large, well-studied lake. We parameterised our allometric trophic network model of 24 guilds and 107 feeding relationships using the lake's food web structure, initial spring biomasses and body-masses. Adding activity respiration, the detrital loop, minimal abiotic forcing, prey resistance and several empirically observed rates substantially increased the model's fit to the observed seasonal dynamics and the size-abundance distribution. This process illuminates a promising approach towards improving food-web theory and dynamic models of specific habitats.     

Artificial neural network modelling of a large-scale wastewater treatment plant operation

Artificial Neural Networks (ANNs), a method of artificial intelligence method, provide effective predictive models for complex processes. Three independent ANN models trained with back-propagation algorithm were developed to predict effluent chemical oxygen demand (COD), suspended solids (SS) and aeration tank mixed liquor suspended solids (MLSS) concentrations of the Ankara central wastewater treatment plant. The appropriate architecture of ANN models was determined through several steps of training and testing of the models. ANN models yielded satisfactory predictions. Results of the root mean square error, mean absolute error and mean absolute percentage error were 3.23, 2.41 mg/L and 5.03% for COD; 1.59, 1.21 mg/L and 17.10% for SS; 52.51, 44.91 mg/L and 3.77% for MLSS, respectively, indicating that the developed model could be efficiently used. The results overall also confirm that ANN modelling approach may have a great implementation potential for simulation, precise performance prediction and process control of wastewater treatment plants.     

A modelling exploration of vertical migration by phytoplankton

The behaviour of phytoplankton having different abilities to assimilate N in darkness was considered in simulations of vertical migrations. Such behaviour is especially important for the competitive advantage of flagellates, including harmful algal species. Three phases of biomass development were apparent. (1) Cells remained at a subsurface location with migration down to avoid photoinhibitory light at midday; as the attenuation of light increased with biomass growth, the mean depth became shallower. (2) On exhaustion of nutrients in surface waters, cells migrated down through the nutricline in the latter half of the daylight period, with a subsurface maximum in the photic zone as long as light penetration matched requirements. When that condition was no longer met (3), cells migrated between the very surface (forming dense aggregations) and the nutricline. While the ability to perform dark N-assimilation is not critical when N-sources are available at low concentrations, it is important when (as encountered following migration down to a nutricline), nutrients are available at higher concentration in darkness. The most advantageous configuration tested, where nitrate assimilation (as well as that of ammonium) continued at a high rate in darkness as long as C-reserves remained, is not actually used in migratory species but in non-migratory diatoms. The use of the outwardly inferior configurations typical of migratory species, in which dark nitrate-assimilation is notably poorer than assimilation in the light, reflects a deficient metabolism or indicates that N-sources other than nitrate are more important. It is unfortunate then that most attention has been paid to nitrate nutrition in experiments on migrating species. While an ability to continue N-assimilation in darkness as well as during daylight is advantageous, there is no evidence for phytoplankton to be able to grow at high growth rates when decoupling photosynthesis at the surface and N-assimilation at depth.     

Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks

1. We used stream fish and decapod spatial occurrence data extracted from a national database and recent surveys with geospatial landuse data, geomorphologic, climatic, and spatial data in a geographical information system (GIS) to model fish and decapod occurrence in the Wellington Region, New Zealand. 2. To predict the occurrence of each species at a site from a common set of predictor variables we used a multi‐response, artificial neural network (ANN), to produce a single model that predicted the entire fish and decapod assemblage in one procedure. 3. The predictions from the ANN using this landscape scale data proved very accurate based on evaluation metrics that are independent of species abundance or probability thresholds. The important variables contributing to the predictions included the latitudinal and elevational position of the site reach, catchment area, average air temperature, the vegetation type, landuse proportions of the catchment, and catchment geology. 4. Geospatial data available for the entire regional river network were then used to create a habitat‐suitability map for all 14 species over the regional river network using a GIS. This prediction map has many potential uses including: monitoring and predicting temporal changes in fish communities caused by human activities and shifts in climate, identifying areas in need of protection, biodiversity hotspots, and areas suitable for the reintroduction of endangered or rare species.     

Novel neural network application for bacterial colony classification

Background

Bacterial colony morphology is the first step of classifying the bacterial species before sending them to subsequent identification process with devices, such as VITEK 2 automated system and mass spectrometry microbial identification system. It is essential as a pre-screening process because it can greatly reduce the scope of possible bacterial species and will make the subsequent identification more specific and increase work efficiency in clinical bacteriology. But this work needs adequate clinical laboratory expertise of bacterial colony morphology, which is especially difficult for beginners to handle properly. This study presents automatic programs for bacterial colony classification task, by applying the deep convolutional neural networks (CNN), which has a widespread use of digital imaging data analysis in hospitals. The most common 18 bacterial colony classes from Peking University First Hospital were used to train this framework, and other images out of these training dataset were utilized to test the performance of this classifier.

Results

The feasibility of this framework was verified by the comparison between predicted result and standard bacterial category. The classification accuracy of all 18 bacteria can reach 73%, and the accuracy and specificity of each kind of bacteria can reach as high as 90%.

Conclusions

The supervised neural networks we use can have more promising classification characteristics for bacterial colony pre-screening process, and the unsupervised network should have more advantages in revealing novel characteristics from pictures, which can provide some practical indications to our clinical staffs.

     

A density functional theory parameterised neural network model of zirconia

We have developed a Behler–Parrinello Neural Network (BPNN) that can be employed to calculate energies and forces of zirconia bulk structures with oxygen vacancies with similar accuracy as that of the density functional theory (DFT) calculations that were used to train the BPNN. In this work, we have trained the BPNN potential with a reference set of 2178 DFT calculations and validated it against a dataset of untrained data. We have shown that the bulk structural parameters, equation of states, oxygen vacancy formation energies and diffusion barriers predicted by the BPNN potential are in good agreement with the reference DFT data. The transferability of the BPNN potential has also been benchmarked with the prediction of structures that were not included in the reference set. The evaluation time of the BPNN is orders of magnitude less than corresponding DFT calculations, although the training process of the BPNN potential requires non-negligible amount of computational resources to prepare the dataset. The computational efficiency of the NN enabled it to be used in molecular dynamics simulations of the temperature-dependent diffusion of an oxygen vacancy and the corresponding diffusion activation energy.     

A neural network model for generating complex birdsong syntax

The singing behavior of songbirds has been investigated as a model of sequence learning and production. The song of the Bengalese finch, Lonchura striata var. domestica, is well described by a finite state automaton including a stochastic transition of the note sequence, which can be regarded as a higher-order Markov process. Focusing on the neural structure of songbirds, we propose a neural network model that generates higher-order Markov processes. The neurons in the robust nucleus of the archistriatum (RA) encode each note; they are activated by RA-projecting neurons in the HVC (used as a proper name). We hypothesize that the same note included in different chunks is encoded by distinct RA-projecting neuron groups. From this assumption, the output sequence of RA is a higher-order Markov process, even though the RA-projecting neurons in the HVC fire on first-order Markov processes. We developed a neural network model of the local circuits in the HVC that explains the mechanism by which RA-projecting neurons transit stochastically on first-order Markov processes. Numerical simulation showed that this model can generate first-order Markov process song sequences.     

Curve-crossing problem for Gaussian stochastic processes and its application to neural modelling

A first time crossing problem for Gaussian stochastic process and monotonic time curve is considered and results are discussed with application to neural modelling. Using diffusion approximation of the stochastic process, integral equation for probability density function of the first time crossing has been obtained. Exact solution of the equation is given for two kinds of stochastic processes which have correspondingly infinitesimal and infinitely large correlation time; approximation methods are constructed for processes characterized by intermediate values of this parameter.     

Artificial neural network modelling of macrophyte indices based on physico-chemical characteristics of water

The study focused on modelling of macropyte indices against physico-chemical parameters of waters by artificial neural networks. Several macrophyte diversity indices were analysed (species richness—N, the Shannon index—H′, the Simpson index—D, and the Pielou index—J) as well as the ecological status index (the Macrophyte Index for Rivers—MIR). The aim of the study was to verify knowledge about potential application of macrophytes in the environmental monitoring. A Multi-Layer Perceptron type of network was used in the analyses. The study included 260 river sites located throughout Poland. Alkalinity, conductivity, pH, nitrate and ammonium nitrogen, reactive and total phosphorus, and biochemical oxygen demand were used as the explanatory variables. The quality of the constructed models was assessed using calculated errors (RMSE and NRMSE) and r Pearson’s linear correlation coefficient. The neural network for the MIR index was characterised by the highest quality. Neural networks for other diversity indices (N, H′, D, and J) did not provide adequate results for modelling, which shows their ineffectiveness biological monitoring. Sensitivity analysis revealed the influence of each variable to the models. It indicated that modelled values of MIR are most strongly influenced by total phosphorus and alkalinity.     

Sequential modelling of a full-scale wastewater treatment plant using an artificial neural network

This work proposes a sequential modelling approach using an artificial neural network (ANN) to develop four independent multivariate models that are able to predict the dynamics of biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solid (SS), and total nitrogen (TN) removal in a wastewater treatment plant (WWTP). Suitable structures of ANN models were automatically and conveniently optimized by a genetic algorithm rather than the conventional trial and error method. The sequential modelling approach, which is composed of two parts, a process disturbance estimator and a process behaviour predictor, was also presented to develop multivariate dynamic models. In particular, the process disturbance estimator was first employed to estimate the influent quality. The process behaviour predictor then sequentially predicted the effluent quality based on the estimated influent quality from the process disturbance estimator with other process variables. The efficiencies of the developed ANN models with a sequential modelling approach were demonstrated with a practical application using a data set collected from a full-scale WWTP during 2 years. The results show that the ANN with the sequential modelling approach successfully developed multivariate dynamic models of BOD, COD, SS, and TN removal with satisfactory estimation and prediction capability. Thus, the proposed method could be used as a powerful tool for the prediction of complex and nonlinear WWTP performance.     

A self-organizing neural network model for the development of complex cells

On the basis of recent neurophysiological findings on the mammalian visual cortex, a selforganizing neural network model is proposed for the understanding of the development of complex cells. The model is composed of two kinds of connections from LGN cells to a complex cell. One is direct excitatory connections and the other is indirect inhibitory connections via simple cells. Inhibitory synapses between simple cells and complex cells are assumed to be modifiable. The model was simulated on a computer to confirm its behavior.     

Distribution and migration pathways of HNK-1-immunoreactive neural crest cells in teleost fish embryos.

Whole mounts and cross-sections of embryos from three species of teleost fish were immunostained with the HNK-1 monoclonal antibody, which recognizes an epitope on migrating neural crest cells. A similar distribution and migration was found in all three species. The crest cells in the head express the HNK-1 epitope after they have segregated from the neural keel. The truncal neural crest cells begin to express the epitope while they still reside in the dorsal region of the neural keel; this has not been observed in other vertebrates. The cephalic and anterior truncal neural crest cells migrate under the ectoderm; the cephalic cells then enter into the gill arches and the anterior truncal cells into the mesentery of the digestive tract where they cease migration. These cephalic and anterior trunk pathways are similar to those described in Xenopus and chick. The neural crest cells of the trunk, after segregation, accumulate in the dorsal wedges between the somites, however, unlike in chick and rat, they do not migrate in the anterior halves of the somites but predominantly between the neural tube and the somites, the major pathway observed in carp and amphibians; some cells migrate over the somites. The HNK-1 staining of whole-mount embryos revealed a structure resembling the Rohon-Beard and extramedullary cells, the primary sensory system in amphibians. Such a system has not been described in fish.     

Predictive modelling of freshwater fish as a biomonitoring tool in New Zealand

SUMMARY 1. A challenge has been issued to ecologists to find quantitative ecological relationships that have predictive power. A predictive approach has been successful when applied to biomonitoring using stream invertebrates with the River Invertebrate Prediction and Classification System (RIVPACS). This approach, to our knowledge, has not been applied to freshwater fish assemblages.
2. This paper describes the initial results of the application of a regional predictive model of freshwater fish occurrence using 200 reference sites sampled in the Manawatu–Wanganui region of New Zealand over late summer/autumn 2000. In brief (i) sites were classified into biotic groups (ii) the physical and chemical characteristics that best describe variation among these groups were determined and (iii) the relationship between these environmental variables and fish communities was used to predict the fauna expected at a site.
3. Reference sites clustered into six groups based on fish density and community composition. Using 14 physical variables least influenced by human activities, a discriminant model allocated 70% of sites to the correct biological classification group. The variables that best separated the site groups were mainly large-scale variables including altitude, distance from the coast, lotic ecoregion and map co-ordinates.
4. The model was further validated by randomly removing 20% of the sites, rebuilding the model and then determining the number of removed sites correctly allocated to their original biotic groups using environmental variables. Using this process 67% of the removed sites were correctly reassigned to the six predetermined groups.
5. A further 30 sites were used to determine the ability of the model to detect anthropogenic impact. The observed over expected taxa ( O / E ) ratios were significantly lower than the reference site O / E ratios, indicating a response of the fish assemblages to the known stressors.     

Menstruation, perimenopause, and chaos theory

This article argues that menstruation, including the transition to menopause, results from a specific kind of complex system, namely, one that is nonlinear, dynamical, and chaotic. A complexity-based perspective changes how we think about and research menstruation-related health problems and positive health. Chaotic systems are deterministic but not predictable, characterized by sensitivity to initial conditions and strange attractors. Chaos theory provides a coherent framework that qualitatively accounts for puzzling results from perimenopause research. It directs attention to variability within and between women, adaptation, lifespan development, and the need for complex explanations of disease. Whether the menstrual cycle is chaotic can be empirically tested, and a summary of our research on 20- to 40-year-old women is provided.