Oscillatory attractor neural networks can perform temporal segmentation, i.e., separate the joint inputs they receive, through the formation of staggered oscillations. This property, which may be basic to many perceptual functions, is investigated here in the context of a symmetric dynamic system. The fully segmented mode is one type of limit cycle that this system can develop. It can be sustained for only a limited number n of oscillators. This limitation to a small number of segments is a basic phenomenon in such systems. Within our model we can explain it in terms of the limited range of narrow subharmonic solutions of the single nonlinear oscillator. Moreover, this point of view allows us to understand the dominance of three leading amplitudes in solutions of partial segmentation, which are obtained for high n. The latter are also abundant when we replace the common input with a graded one, allowing for different inputs to different oscillators. Switching to an input with fluctuating components, we obtain segmentation dominance for small systems and quite irregular waveforms for large systems.

Arrays of interacting identical neurons can develop coherent firing patterns, such as moving stripes that have been suggested as possible explanations of hallucinatory phenomena. Other known formations include rotating spirals and expanding concentric rings. We obtain all of them using a novel two-variable description of integrate-and-fire neurons that allows for a continuum formulation of neural fields. One of these variables distinguishes between the two different states of refractoriness and depolarization and acquires topological meaning when it is turned into a field. Hence, it leads to a topologic characterization of the ensuing solitary waves, or excitons. They are limited to pointlike excitations on a line and linear excitations, including all the examples noted above, on a two-dimensional surface. A moving patch of firing activity is not an allowed solitary wave on our neural surface. Only the presence of strong inhomogeneity that destroys the neural field continuity allows for the appearance of patchy incoherent firing patterns driven by excitatory interactions.

This paper extends upon a previous work using Mean Shift algorithm to perform speaker clustering on i-vectors generated from short speech segments. In this paper we examine the effectiveness of probabilistic linear discriminant analysis (PLDA) scoring as the metric of the mean shift clustering algorithm in the presence of different number of speakers. Our proposed method, combined with k-nearest neighbors (kNN) for bandwidth estimation, yields better and more robust results in comparison to the cosine similarity with fixed neighborhood bandwidth for clustering segments of large number of speakers. In the case of 30 speakers, we achieved evaluation parameter K of 72.1 with the PLDA-based mean shift algorithm compared to 65.9 with the cosine-based baseline system.

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In this paper we try to identify spectral and acoustic features that are distinctive of Parkinson's disease patients' speech. We investigate the contribution of several features' families to a simple classification task that distinguishes between two balanced groups-patients with Parkinson's disease and their age and gender matched group of Healthy Controls, both uttering sustained vowels. We achieve over 75% correct classification using a combination of acoustic and spectral features. We show that combining a few statistical functionals of these features yields very good results.. This can be explained by two reasons: the first is that the statistics of Parkinson's disease patients' speech defer from those of Healthy people's speech; the second and more important one is the gradual nature of the Parkinsonian speech that is manifested by the changes within an utterance. We speculate that the feature families that most contribute to the classification task are the most distinctive for detecting the disease and suggest testing this hypothesis by performing long-term analysis of both patient and healthy control subjects. Similar accuracy is obtained when analyzing spontaneous speech where each utterance is represented by a single normalized i-vector.