In this paper we present an oscillatory neural network composed of two coupled neural oscillators of the Wilson-Cowan type. Each of the oscillators describes the dynamics of average activities of excitatory and inhibitory populations of neurons. The network serves as a model for several possible network architectures. We study how the type and the strength of the connections between the oscillators affect the dynamics of the neural network. We investigate, separately from each other, four possible connection types (excitatory-->excitatory, excitatory-->inhibitory, inhibitory-->excitatory, and inhibitory-->inhibitory) and compute the corresponding bifurcation diagrams. In case of weak connections (small strength), the connection of populations of different types lead to periodic in-phase oscillations, while the connection of populations of the same type lead to periodic anti-phase oscillations. For intermediate connection strengths, the networks can enter quasiperiodic or chaotic regimes, and can also exhibit multistability. More generally, our analysis highlights the great diversity of the response of neural networks to a change of the connection strength, for different connection architectures. In the discussion, we address in particular the problem of information coding in the brain using quasiperiodic and chaotic oscillations. In modeling low levels of information processing, we propose that feature binding should be sought as a temporally coherent phase-locking of neural activity. This phase-locking is provided by one or more interacting convergent zones and does not require a central ¿top level¿ subcortical circuit (e.g., the septo-hippocampal system). We build a two layer model to show that although the application of a complex stimulus usually leads to different convergent zones with high frequency oscillations, it is nevertheless possible to synchronize these oscillations at a lower frequency level using envelope oscillations. This is interpreted as a feature binding of a complex stimulus.

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A new method is proposed to analyse dependencies in point processes, which takes into account specific character of neuronal activity. Simulation modelling of neuronal network revealed that the estimated weight of connection depends monotonically on the value of the model synaptic strength. In contrast to the crosscorrelation, the method allows for nonlinear interconnections and does not require point processes to be stationary and samples to be large. Examples are presented of the method's application to neurophysiological data analysis.

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 singlemember 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.Ballot-order effects -the relationship between each candidate's position and description on the ballot paper and the number of votes received -have been investigated extensively. They affect candidates in different ways, and have led some countries to introduce measures to remove them. The identification of alphabetic bias, the tendency for candidates to gain an advantage in votes solely by virtue of being placed at or near the top of the ballot paper, prompted the randomization of ballot orders in Australia and some American states, for example.1 This removes alphabetic bias, but sometimes replaces it with positional bias -the candidate placed at the top of the ballot may obtain an advantage over competitors.2 The growing practice of including candidates' photographs on the ballot paper can lead to an association between attractiveness and vote, giving some candidates an advantage over others.3 A third category of ballot effect stems from voters' reactions to names on the ballot paper as information cues about the candidate's gender, ethnic identity and/or religious affiliation. 4 How voters react to name characteristics is a more complex problem to address than alphabetic bias, and finding a workable solution is considerably more difficult to achieve.

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