Inner-ear abnormalities and their functional consequences in Belgian Waterslager canaries (Serinus canarius)

Recent reports of elevated auditory thresholds in canaries of the Belgian Waterslager strain have shown that this strain has an inherited auditory deficit in which absolute auditory thresholds at high frequencies (i.e. above 2.0 kHz) are as much as 40 dB less sensitive than the thresholds of mixed-breed canaries and those of other strains. The measurement of CAP audiograms showed that the hearing deficit is already present at the level of the auditory nerve (Gleich and Dooling, 1992). Here we show gross abnormalities in the anatomy of the basilar papilla of Belgian Waterslager canaries at the level of the hair cell. The extent of these abnormalities was correlated with the amount of hearing deficit as measured behaviorally

Multimodal Network Alignment

A multimodal network encodes relationships between the same set of nodes in multiple settings, and network alignment is a powerful tool for transferring information and insight between a pair of networks. We propose a method for multimodal network alignment that computes a matrix which indicates the alignment, but produces the result as a low-rank factorization directly. We then propose new methods to compute approximate maximum weight matchings of low-rank matrices to produce an alignment. We evaluate our approach by applying it on synthetic networks and use it to de-anonymize a multimodal transportation network.Comment: 14 pages, 6 figures, Siam Data Mining 201

Temporal modulation transfer functions in the European Starling (Sturnus vulgaris): II. Responses of auditory-nerve fibres

The temporal resolution of cochlear-nerve fibres in the European starling was determined with sinusoidally amplitude-modulated noise stimuli similar to those previously used in a psychoacoustic study in this species (Klump and Okanoya, 1991). Temporal modulation transfer curves (TMTFs) were constructed for cochlear afferents allowing a direct comparison with the starling's behavioural performance. On average, the neuron's detection of modulation was less sensitive than that obtained in the behavioural experiments, although the most sensitive cells approached the values determined psychophysically. The shapes of the neural TMTFs generally resembled low-pass or band-pass filter functions, and the shapes of the averaged neural functions were very similar to those obtained in the behavioural study for two different types of stimuli (gated and continuous carrier). Minimum integration times calculated from the upper cut-off frequency of the neural TMTFs had a median of 0.97 ms with a range of 0.25 to 15.9 ms. The relations between the minimum integration times and the tuning characteristics of the cells (tuning curve bandwidth, Q10 dB-value, high- and low-frequency slopes of the tuning curves) are discussed. Finally, we compare the TMTF data recorded in the starling auditory nerve with data from neurophysiological and behavioural observations on temporal resolution using other experimental paradigms in this and other vertebrate species

Unifying Sparsest Cut, Cluster Deletion, and Modularity Clustering Objectives with Correlation Clustering

Graph clustering, or community detection, is the task of identifying groups of closely related objects in a large network. In this paper we introduce a new community-detection framework called LambdaCC that is based on a specially weighted version of correlation clustering. A key component in our methodology is a clustering resolution parameter, $\lambda$, which implicitly controls the size and structure of clusters formed by our framework. We show that, by increasing this parameter, our objective effectively interpolates between two different strategies in graph clustering: finding a sparse cut and forming dense subgraphs. Our methodology unifies and generalizes a number of other important clustering quality functions including modularity, sparsest cut, and cluster deletion, and places them all within the context of an optimization problem that has been well studied from the perspective of approximation algorithms. Our approach is particularly relevant in the regime of finding dense clusters, as it leads to a 2-approximation for the cluster deletion problem. We use our approach to cluster several graphs, including large collaboration networks and social networks