A unique role for Kv3 voltage-gated potassium channels in starburst amacrine cell signaling in mouse retina

Direction-selective retinal ganglion cells show an increased activity evoked by light stimuli moving in the preferred direction. This selectivity is governed by direction-selective inhibition from starburst amacrine cells occurring during stimulus movement in the opposite or null direction. To understand the intrinsic membrane properties of starburst cells responsible for direction-selective GABA release, we performed whole-cell recordings from starburst cells in mouse retina. Voltage-clamp recordings revealed prominent voltage-dependent K+ currents. The currents were mostly blocked by 1 mm TEA, activated rapidly at voltages more positive than -20 mV, and deactivated quickly, properties reminiscent of the currents carried by the Kv3 subfamily of K+ channels. Immunoblots confirmed the presence of Kv3.1 and Kv3.2 proteins in retina and immunohistochemistry revealed their expression in starburst cell somata and dendrites. The Kv3-like current in starburst cells was absent in Kv3.1-Kv3.2 knock-out mice. Current-clamp recordings showed that the fast activation of the Kv3 channels provides a voltage-dependent shunt that limits depolarization of the soma to potentials more positive than -20 mV. This provides a mechanism likely to contribute to the electrical isolation of individual starburst cell dendrites, a property thought essential for direction selectivity. This function of Kv3 channels differs from that in other neurons where they facilitate high-frequency repetitive firing. Moreover, we found a gradient in the intensity of Kv3.1b immunolabeling favoring proximal regions of starburst cells. We hypothesize that this Kv3 channel gradient contributes to the preference for centrifugal signal flow in dendrites underlying direction-selective GABA release from starburst amacrine cells.

Abnormal Error Monitoring in Math-Anxious Individuals: Evidence from Error-Related Brain Potentials.

This study used event-related brain potentials to investigate whether math anxiety is related to abnormal error monitoring processing. Seventeen high math-anxious (HMA) and seventeen low math-anxious (LMA) individuals were presented with a numerical and a classical Stroop task. Groups did not differ in terms of trait or state anxiety. We found enhanced error-related negativity (ERN) in the HMA group when subjects committed an error on the numerical Stroop task, but not on the classical Stroop task. Groups did not differ in terms of the correct-related negativity component (CRN), the error positivity component (Pe), classical behavioral measures or post-error measures. The amplitude of the ERN was negatively related to participants" math anxiety scores, showing a more negative amplitude as the score increased. Moreover, using standardized low resolution electromagnetic tomography (sLORETA) we found greater activation of the insula in errors on a numerical task as compared to errors in a nonnumerical task only for the HMA group. The results were interpreted according to the motivational significance theory of the ERN.

Secure logarithmic audio watermarking scheme based on the human auditory system

Peer-reviewed

Bowing the violin : a case study for auditory-motor pattern modelling in the context of music performance

This project addresses methodological and technological challenges in the development of multi-modal data acquisition and analysis methods for the representation of instrumental playing technique in music performance through auditory-motor patterning models. The case study is violin playing: a multi-modal database of violin performances has been constructed by recording different musicians while playing short exercises on different violins. The exercise set and recording protocol have been designed to sample the space defined by dynamics (from piano to forte) and tone (from sul tasto to sul ponticello), for each bow stroke type being played on each of the four strings (three different pitches per string) at two different tempi. The data, containing audio, video, and motion capture streams, has been processed and segmented to facilitate upcoming analyses. From the acquired motion data, the positions of the instrument string ends and the bow hair ribbon ends are tracked and processed to obtain a number of bowing descriptors suited for a detailed description and analysis of the bow motion patterns taking place during performance. Likewise, a number of sound perceptual attributes are computed from the audio streams. Besides the methodology and the implementation of a number of data acquisition tools, this project introduces preliminary results from analyzing bowing technique on a multi-modal violin performance database that is unique in its class. A further contribution of this project is the data itself, which will be made available to the scientific community through the repovizz platform.