A neural network simulation of the vestibular system: implications on the role of intervestibular nuclear coupling during vestibular compensation

 Previous neural network simulations of the vestibular system have been based loosely on known physiology. This research involved the use of a strongly physiologically based neural network model which was used to investigate the role of the vestibular commissure in restoring the bilateral symmetry of the resting rates of the vestibular nuclei during vestibular compensation following unilateral labyrinthectomy. It was found that readjustments in the gain of the vestibular commissure were not primarily responsible for vestibular compensation, as has previously been suggested, but rather that it was modifications in extralabyrinthine sources of tone which mediated the restoration of the central symmetry between the two nuclei. Received: 20 November 1995/Accepted in revised form: 24 July 1996     

Dynamic muscle force predictions from EMG: an artificial neural network approach

EMG signals of dynamically contracting muscle have never been used to predict experimentally known muscle forces across subjects. Here, we use an artificial neural network (ANN) approach to first derive an EMG–force relationship from a subset of experimentally determined EMGs and muscle forces; second, we use this relationship to predict individual muscle forces for different contractile conditions and in subjects whose EMG and force data were not used in the derivation of the EMG–force relationship; and third, we validate the predicted muscle forces against the known forces recorded in vivo. EMG and muscle forces were recorded from the cat soleus for a variety of locomotor conditions giving a data base from three subjects, four locomotor conditions, and 8–16 steps per subject and condition. Considering the conceptual differences in the tasks investigated (e.g. slow walking vs. trotting), the intra-subject results obtained here are superior to those published previously, even though the approach did not require a muscle model or the instantaneous contractile conditions as input for the force predictions. The inter-subject results are the first of this kind to be presented in the literature and they typically gave cross-correlation coefficients between actual and predicted forces of >0.90 and root mean square errors of <15%, thus they were considered excellent.

From the results of this study, it was concluded that ANNs represent a powerful tool to capture the essential features of EMG–force relationships of dynamically contracting muscle, and that ANNs might be used widely to predict muscle forces based on EMG signals.     

A model biological neural network: the cephalopod vestibular system

Artificial neural networks (ANNs) have become increasingly sophisticated and are widely used for the extraction of patterns or meaning from complicated or imprecise datasets. At the same time, our knowledge of the biological systems that inspired these ANNs has also progressed and a range of model systems are emerging where there is detailed information not only on the architecture and components of the system but also on their ontogeny, plasticity and the adaptive characteristics of their interconnections. We describe here a biological neural network contained in the cephalopod statocysts; the statocysts are analogous to the vertebrae vestibular system and provide the animal with sensory information on its orientation and movements in space. The statocyst network comprises only a small number of cells, made up of just three classes of neurons but, in combination with the large efferent innervation from the brain, forms an 'active' sense organs that uses feedback and feed-forward mechanisms to alter and dynamically modulate the activity within cells and how the various components are interconnected. The neurons are fully accessible to physiological investigation and the system provides an excellent model for describing the mechanisms underlying the operation of a sophisticated neural network.     

Microgeographic socio-genetic structure of an African cooperative breeding passerine revealed: integrating behavioural and genetic data

Dispersal can be motivated by multiple factors including sociality. Dispersal behaviour affects population genetic structure that in turn reinforces social organization. We combined observational information with individual-based genetic data in the Karoo scrub-robin, a facultative cooperatively breeding bird, to understand how social bonds within familial groups affect mating patterns, cause sex asymmetry in dispersal behaviour and ultimately influence the evolution of dispersal. Our results revealed that males and females do not have symmetrical roles in structuring the population. Males are extremely philopatric and tend to delay dispersal until they gain a breeding position within a radius of two territories around the natal site. By contrast, females dispersed over larger distances, as soon as they reach independence. This resulted in male neighbourhoods characterized by high genetic relatedness. The long-distance dispersal strategy of females ensured that Karoo scrub-robins do not pair with relatives thereby compensating for male philopatry caused by cooperation. The observed female-biased strategy seems to be the most prominent mechanism to reduce the risk of inbreeding that characterizes social breeding system. This study demonstrates that tying together ecological data, such as breeding status, determining social relationships with genetic data, such as kinship, provides valuable insights into the proximate causes of dispersal, which are central to any evolutionary interpretation.     

Modeling the adaptive immune system: predictions and simulations

MOTIVATION: Immunological bioinformatics methods are applicable to a broad range of scientific areas. The specifics of how and where they might be implemented have recently been reviewed in the literature. However, the background and concerns for selecting between the different available methods have so far not been adequately covered. SUMMARY: Before using predictions systems, it is necessary to not only understand how the methods are constructed but also their strength and limitations. The prediction systems in humoral epitope discovery are still in their infancy, but have reached a reasonable level of predictive strength. In cellular immunology, MHC class I binding predictions are now very strong and cover most of the known HLA specificities. These systems work well for epitope discovery, and predictions of the MHC class I pathway have been further improved by integration with state-of-the-art prediction tools for proteasomal cleavage and TAP binding. By comparison, class II MHC binding predictions have not developed to a comparable accuracy level, but new tools have emerged that deliver significantly improved predictions not only in terms of accuracy, but also in MHC specificity coverage. Simulation systems and mathematical modeling are also now beginning to reach a level where these methods will be able to answer more complex immunological questions.     

DASMiner: discovering and integrating data from DAS sources

Background  

DAS is a widely adopted protocol for providing syntactic interoperability among biological databases. The popularity of DAS is due to a simplified and elegant mechanism for data exchange that consists of sources exposing their RESTful interfaces for data access. As a growing number of DAS services are available for molecular biology resources, there is an incentive to explore this protocol in order to advance data discovery and integration among these resources.     

Incorporating anatomically realistic cellular-level connectivity in neural network models of the rat hippocampus

The specific connectivity patterns among neuronal classes can play an important role in the regulation of firing dynamics in many brain regions. Yet most neural network models are built based on vastly simplified connectivity schemes that do not accurately reflect the biological complexity. Taking the rat hippocampus as an example, we show here that enough quantitative information is available in the neuroanatomical literature to construct neural networks derived from accurate models of cellular connectivity. Computational simulations based on this approach lend themselves to a direct investigation of the potential relationship between cellular connectivity and network activity. We define a set of fundamental parameters to characterize cellular connectivity, and are collecting the related values for the rat hippocampus from published reports. Preliminary simulations based on these data uncovered a novel putative role for feedforward inhibitory neurons. In particular, "mopp" cells in the dentate gyrus are suitable to help maintain the firing rate of granule cells within physiological levels in response to a plausibly noisy input from the entorhinal cortex. The stabilizing effect of feedforward inhibition is further shown to depend on the particular ratio between the relative threshold values of the principal cells and the interneurons. We are freely distributing the connectivity data on which this study is based through a publicly accessible web archive (http://www.krasnow.gmu.edu/L-Neuron).     

PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data

Background

Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data.

Results

In this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes and pathways to predict clinical outcomes by leveraging deep learning. The sparse solution of PASNet provides the capability of model interpretability that most conventional fully-connected neural networks lack. We applied PASNet for long-term survival prediction in Glioblastoma multiforme (GBM), which is a primary brain cancer that shows poor prognostic performance. The predictive performance of PASNet was evaluated with multiple cross-validation experiments. PASNet showed a higher Area Under the Curve (AUC) and F1-score than previous long-term survival prediction classifiers, and the significance of PASNet’s performance was assessed by Wilcoxon signed-rank test. Furthermore, the biological pathways, found in PASNet, were referred to as significant pathways in GBM in previous biology and medicine research.

Conclusions

PASNet can describe the different biological systems of clinical outcomes for prognostic prediction as well as predicting prognosis more accurately than the current state-of-the-art methods. PASNet is the first pathway-based deep neural network that represents hierarchical representations of genes and pathways and their nonlinear effects, to the best of our knowledge. Additionally, PASNet would be promising due to its flexible model representation and interpretability, embodying the strengths of deep learning. The open-source code of PASNet is available at https://github.com/DataX-JieHao/PASNet.

     

Deriving neural network controllers from neuro-biological data: implementation of a single-leg stick insect controller

This article presents modular recurrent neural network controllers for single legs of a biomimetic six-legged robot equipped with standard DC motors. Following arguments of Ekeberg et?al. (Arthropod Struct Dev 33:287?C300, 2004), completely decentralized and sensori-driven neuro-controllers were derived from neuro-biological data of stick-insects. Parameters of the controllers were either hand-tuned or optimized by an evolutionary algorithm. Employing identical controller structures, qualitatively similar behaviors were achieved for robot and for stick insect simulations. For a wide range of perturbing conditions, as for instance changing ground height or up- and downhill walking, swing as well as stance control were shown to be robust. Behavioral adaptations, like varying locomotion speeds, could be achieved by changes in neural parameters as well as by a mechanical coupling to the environment. To a large extent the simulated walking behavior matched biological data. For example, this was the case for body support force profiles and swing trajectories under varying ground heights. The results suggest that the single-leg controllers are suitable as modules for hexapod controllers, and they might therefore bridge morphological- and behavioral-based approaches to stick insect locomotion control.     

Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network

In this work we applied a TSK-type recurrent neural fuzzy approach to extract regulatory relationship among genes and reconstruct gene regulatory network from microarray data. The identified signature has captured the regulatory relationship among 27 differentially expressed genes from microarray dataset. We applied three different methods viz., feed forward neural fuzzy, modified genetic algorithm and recurrent neural fuzzy, on the same data set for the inference of GRNs and the results obtained are almost comparable. In all tested cases, TRNFN identified more biologically meaningful relations. We found that 87.8% of the total interactions extracted by TRNFN are correct in accordance with the biological knowledge. Our analysis resulted in 2 major outcomes. First, upregulated genes are regulated by more genes than downregulated genes. Second, tumor activators activate other tumor activators and suppress tumor suppressers strongly in the disease environment. These findings will help to elucidate the common molecular mechanism of colon cancer, and provide new insights into cancer diagnostics, prognostics and therapy.     

Automatic identification of algae: neural network analysis of flow cytometric data

The performance of an artificial neural network for automaticidentification of phytoplankton was investigated with data fromalgal laboratory cultures, analysed on the Optical PlanktonAnalyser (OPA), a flow cytometer especially developed for theanalysis of phytoplankton. Data from monocultures of eight algalspecies were used to train a neural network. The performanceof the trained network was tested with OPA data from mixturesof laboratory cultures. The network could distinguish Cyanobacteriafrom other algae with 99% accuracy. The identification of specieswas performed with less accuracy, but was generally >90%.This indicates that a neural network under supervised learningcan be used for automatic identification of species in relativelycomplex mixtures. Incorporation of such a system may also increasethe operational size range of a flow cytometer. The combinationof the OPA and neural network data analysis offers the elementsto build an operational automatic algal identification system.     

Spatio-temporal modeling and analysis of fMRI data using NARX neural network

This paper presents spatio-temporal modeling and analysis methods to fMRI data. Based on the nonlinear autoregressive with exogenous inputs (NARX) model realized by the Bayesian radial basis function (RBF) neural networks, two methods (NARX-1 and NARX-2) are proposed to capture the unknown complex dynamics of the brain activities. Simulation results on both synthetic and real fMRI data clearly show that the proposed schemes outperform the conventional t-test method in detecting the activated regions of the brain.     

Coupling isoprene and monoterpene emissions from Amazonian tree species with physiological and environmental parameters using a neural network approach

The ability to predict isoprene emissions from plants is important for predicting atmospheric chemistry. To improve the basis for prediction capability, data obtained from continuous field measurements of isoprene and monoterpene emissions from three Amazonian tree species were related to observed environmental and leaf physiological parameters using a new neural network approach. The environmental parameters included leaf temperature, light, relative humidity, water vapour pressure deficit, and the history of ambient temperature and ozone concentration, whereas the physiological parameters included stomatal conductance, assimilation and intercellular CO₂ concentration. The neural approach with 24 different combinations of these parameters was applied to predict the emission variability observed during short time periods (2–3 d) with individual tree branches and, on a longer-term scale, in aggregated data sets from different seasons, leaf developmental stage, and light environment. The results were compared to the quasi standard emission algorithm for isoprene. On the short-term scale, good agreement (r²≈ 0.9) was obtained between observations and predictions of the standard algorithm as well as predictions of the neural network using the same input parameters (leaf temperature and light). When these predictors were used to model the long-term emission variability, r² was reduced to < 0.5 for both approaches. Remarkably, for the neural technique, more than 50% of the unexplained variance could be explained by the mean temperature of the preceding 36 h. An even better network performance was obtained with physiological parameter combinations (r² > 0.9) suggesting a strong and applicable link between isoprenoid emission and leaf primary metabolism.     

Toward ecologically realistic predictions of species distributions: A cross‐time example from tropical montane cloud forests

There is an urgent need for more ecologically realistic models for better predicting the effects of climate change on species’ potential geographic distributions. Here we build ecological niche models using MAXENT and test whether selecting predictor variables based on biological knowledge and selecting ecologically realistic response curves can improve cross‐time distributional predictions. We also evaluate how the method chosen for extrapolation into nonanalog conditions affects the prediction. We do so by estimating the potential distribution of a montane shrew (Mammalia, Soricidae, Cryptotis mexicanus) at present and the Last Glacial Maximum (LGM). Because it is tightly associated with cloud forests (with climatically determined upper and lower limits) whose distributional shifts are well characterized, this species provides clear expectations of plausible vs. implausible results. Response curves for the MAXENT model made using variables selected via biological justification were ecologically more realistic compared with those of the model made using many potential predictors. This strategy also led to much more plausible geographic predictions for upper and lower elevational limits of the species both for the present and during the LGM. By inspecting the modeled response curves, we also determined the most appropriate way to extrapolate into nonanalog environments, a previously overlooked factor in studies involving model transfer. This study provides intuitive context for recommendations that should promote more realistic ecological niche models for transfer across space and time.     

The vestibular end organs: morphological and physiological diversity of afferents.

Vestibular-nerve fibers, even those innervating a single end organ, have been found to differ in their branching patterns within the neuroepithelium. They also vary in their responses to head movements and to activation of efferent fibers, and in the central pathways to which they contribute. These results are enabling plausible inferences to be made about the peripheral mechanisms determining the discharge properties of physiologically distinguishable afferents, and about the contributions the different afferents make to the overall functioning of the vestibular system.