The correlation grid: analysis of synchronous spiking in multi-dimensional spike train data and identification of feasible connection architectures.

This paper presents a visualization technique specifically designed to support the analysis of synchronous firings in multiple, simultaneously recorded, spike trains. This technique, called the correlation grid, enables investigators to identify groups of spike trains, where each pair of spike trains has a high probability of generating spikes approximately simultaneously or within a constant time shift. Moreover, the correlation grid was developed to help solve the following reverse problem: identification of the connection architecture between spike train generating units, which may produce a spike train dataset similar to the one under analysis. To demonstrate the efficacy of this approach, results are presented from a study of three simulated, noisy, spike train datasets. The parameters of the simulated neurons were chosen to reflect the typical characteristics of cortical pyramidal neurons. The schemes of neuronal connections were not known to the analysts. Nevertheless, the correlation grid enabled the analysts to find the correct connection architecture for each of these three data sets

Visualisation of synchronous firing in multi-dimensional spike trains.

The gravity transform algorithm is used to study the dependencies in firing of multi-dimensional spike trains. The pros and cons of this algorithm are discussed and the necessity for improved representation of output data is demonstrated. Parallel coordinates are introduced to visualise the results of the gravity transform and principal component analysis (PCA) is used to reduce the quantity of data represented whilst minimising loss of information

Candidate Ethnic Origins and Voter Preferences: Examining Name Discrimination in Local Elections in Britain

This article examines the relationship between candidate names as they appear on the ballot paper and voting patterns in British local elections. Specifically, it explores whether some voters favour candidates with British-sounding names over those whose names suggest either European or non-European ethnic origins. Name classification software identifies three categories of candidate: British, other European and non-European. Separate analyses of aggregate voting data are undertaken of multi-member and single-member electoral districts. Data cover the period 1973-2012, and votes for more than 400,000 candidates are examined. In multi-member districts, after comparing within-party slates and finishing order generally, candidates whose surnames suggest a British ethnic origin perform best, while non-Europeans attract fewer votes. The analysis of single-member districts focuses on a party's vote share after taking into account the pattern of candidate recruitment across electoral cycles. It shows that vote share is adversely affected when British candidates are replaced by those with European and non-European surnames, while the opposite pattern of succession is associated with a boost in votes. It is clear that the outcome of some elections has been determined by the parties' choice of candidates

Can simple rules control development of a pioneer vertebrate neuronal network generating behavior?

How do the pioneer networks in the axial core of the vertebrate nervous system first develop? Fundamental to understanding any full-scale neuronal network is knowledge of the constituent neurons, their properties, synaptic interconnections, and normal activity. Our novel strategy uses basic developmental rules to generate model networks that retain individual neuron and synapse resolution and are capable of reproducing correct, whole animal responses. We apply our developmental strategy to young Xenopus tadpoles, whose brainstem and spinal cord share a core vertebrate plan, but at a tractable complexity. Following detailed anatomical and physiological measurements to complete a descriptive library of each type of spinal neuron, we build models of their axon growth controlled by simple chemical gradients and physical barriers. By adding dendrites and allowing probabilistic formation of synaptic connections, we reconstruct network connectivity among up to 2000 neurons. When the resulting "network" is populated by model neurons and synapses, with properties based on physiology, it can respond to sensory stimulation by mimicking tadpole swimming behavior. This functioning model represents the most complete reconstruction of a vertebrate neuronal network that can reproduce the complex, rhythmic behavior of a whole animal. The findings validate our novel developmental strategy for generating realistic networks with individual neuron- and synapse-level resolution. We use it to demonstrate how early functional neuronal connectivity and behavior may in life result from simple developmental "rules," which lay out a scaffold for the vertebrate CNS without specific neuron-to-neuron recognition

Tadpole VR: virtual reality visualization of a simulated tadpole spinal cord

Recent advances in “developmental” approach (combining experimental study with computational modelling) of neural networks produces increasingly large data sets, in both complexity and size. This poses a significant challenge in analyzing, visualizing and understanding not only the spatial structure but also the behavior of such networks. This paper describes a Virtual Reality application for visualization of two biologically accurate computational models that model the anatomical structure of a neural network comprised of 1,500 neurons and over 80,000 connections. The visualization enables a user to observe the complex spatio-temporal interplay between seven unique types of neurons culminating in an observable swimming pattern. We present a detailed description of the design approach for the virtual environment, based on a set of initial requirements, followed up by the implementation and optimization steps. Lastly, the results of a pilot usability study are being presented on how confident participants are in their ability to understand how the alternating firing pattern between the two sides of the tadpole’s body generate swimming motion

A developmental approach to predicting neuronal connectivity from small biological datasets: a gradient-based neuron growth model.

PMCID: PMC3931784 Open Access article: BB/G006652/1 and BB/G006369/1.Relating structure and function of neuronal circuits is a challenging problem. It requires demonstrating how dynamical patterns of spiking activity lead to functions like cognitive behaviour and identifying the neurons and connections that lead to appropriate activity of a circuit. We apply a "developmental approach" to define the connectome of a simple nervous system, where connections between neurons are not prescribed but appear as a result of neuron growth. A gradient based mathematical model of two-dimensional axon growth from rows of undifferentiated neurons is derived for the different types of neurons in the brainstem and spinal cord of young tadpoles of the frog Xenopus. Model parameters define a two-dimensional CNS growth environment with three gradient cues and the specific responsiveness of the axons of each neuron type to these cues. The model is described by a nonlinear system of three difference equations; it includes a random variable, and takes specific neuron characteristics into account. Anatomical measurements are first used to position cell bodies in rows and define axon origins. Then a generalization procedure allows information on the axons of individual neurons from small anatomical datasets to be used to generate larger artificial datasets. To specify parameters in the axon growth model we use a stochastic optimization procedure, derive a cost function and find the optimal parameters for each type of neuron. Our biologically realistic model of axon growth starts from axon outgrowth from the cell body and generates multiple axons for each different neuron type with statistical properties matching those of real axons. We illustrate how the axon growth model works for neurons with axons which grow to the same and the opposite side of the CNS. We then show how, by adding a simple specification for dendrite morphology, our model "developmental approach" allows us to generate biologically-realistic connectomes

Multifractal analysis of stress time series during ultrathin lubricant film melting

Melting of an ultrathin lubricant film confined between two atomically flat surfaces is we studied using the rheological model for viscoelastic matter approximation. Phase diagram with domains, corresponding to sliding, dry, and two types of $stick-slip$ friction regimes has been built taking into account additive noises of stress, strain, and temperature of the lubricant. The stress time series have been obtained for all regimes of friction using the Stratonovich interpretation. It has been shown that self-similar regime of lubricant melting is observed when intensity of temperature noise is much larger than intensities of strain and stress noises. This regime is defined by homogenous distribution, at which characteristic stress scale is absent. We study stress time series obtained for all friction regimes using multifractal detrended fluctuation analysis. It has been shown that multifractality of these series is caused by different correlations that are present in the system and also by a power-law distribution. Since the power-law distribution is related to small stresses, this case corresponds to self-similar solid-like lubricant.Comment: 22 pages, 10 figures, 41 reference

Are Θ+\Theta^+ and the Roper resonance diquark-diquark-antiquark states?

We consider a $[ud]^2\bar{s}$ current in the QCD sum rule framework to study the mass of the recently observed pentaquark state $\Theta^+(1540)$, obtaining good agreement with the experimental value. We also study the mass of the pentaquark $[ud]^2\bar{d}$. Our results are compatible with the interpretation of the $[ud]^2\bar{d}$ state as being the Roper resonance N(1440), as suggested by Jaffe and Wilczek.Comment: 9 pages RevTex4 and 3 eps figures. Revised version accepted for publication in Phys. Lett.

Model validation for a noninvasive arterial stenosis detection problem

Copyright @ 2013 American Institute of Mathematical SciencesA current thrust in medical research is the development of a non-invasive method for detection, localization, and characterization of an arterial stenosis (a blockage or partial blockage in an artery). A method has been proposed to detect shear waves in the chest cavity which have been generated by disturbances in the blood flow resulting from a stenosis. In order to develop this methodology further, we use both one-dimensional pressure and shear wave experimental data from novel acoustic phantoms to validate corresponding viscoelastic mathematical models, which were developed in a concept paper [8] and refined herein. We estimate model parameters which give a good fit (in a sense to be precisely defined) to the experimental data, and use asymptotic error theory to provide confidence intervals for parameter estimates. Finally, since a robust error model is necessary for accurate parameter estimates and confidence analysis, we include a comparison of absolute and relative models for measurement error.The National Institute of Allergy and Infectious Diseases, the Air Force Office of Scientific Research, the Deopartment of Education and the Engineering and Physical Sciences Research Council (EPSRC)

Electoral bias at the 2015 general election: reducing Labour’s electoral advantage

Electoral bias results in an asymmetrical seat distribution between parties with similar vote shares. Over recent British general elections Labour held an advantage because it efficiently converted votes into seats. Following the 2015 election result this advantage has reduced considerably, principally because Labour’s vote distribution saw it accumulate more ineffective votes, particularly where electoral support was not converted into seats. By contrast, the vote distribution of the Conservative party is now superior to that of Labour because it acquired fewer wasted votes although Labour retains a modest advantage overall because it benefits from inequalities in electorate size and differences in voter turnout. Features of the 2015 election, however, raise general methodological challenges for decomposing electoral bias. The analysis, therefore, considers the effect of substituting the Liberal Democrats as the third party with the United Kingdom Independence Party. It also examines the outcome in Scotland separately from that in England and Wales. Following this analysis it becomes clear that the method for decomposing electoral bias requires clearer guidelines for its application in specific settings