Dau, Torsten

Epp, Bastian

Joshi, Suyash Narendra

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 18, 2017Cross-correlation model of interaural time difference coding in listeners with bilateralcochlear implantsJoshi, Suyash Narendra; Dau, Torsten; Epp, BastianPublication date:2016Document VersionPeer reviewed versionLink back to DTU OrbitCitation (APA):Joshi, S. N., Dau, T., & Epp, B. (2016). Cross-correlation model of interaural time difference coding in listenerswith bilateral cochlear implants. Poster session presented at 5th Joint Meeting of the Acoustical Society ofAmerica and Acoustical Society of Japan, Honolulu, Hawaii, United States.Cross-correlationmodelof interauraltimedifferencecodinginlistenerswithbilateralcochlearimplantsSuyashN. Joshi,TorstenDau,BastianEppHearingSystemsGroup,TechnicalUniversityofDenmarkIntroduction• Sound localization is crucial for successful communication in natural acoustic en-vironments.• Listeners with binaural hearing loss or deafness are increasingly prescribed withbilateral cochlear implants (BiCI) which provides the listeners an access to binau-ral input. However, the BiCI listeners show severely impaired sound localizationability, despite an access to the binaural signals.• It has been suggested that the impaired localization is mainly due to the inabilityof the BiCI listeners to utilize interaural time difference (ITD) cues.• Various stimulation related parameters (e.g. pulse rate or shape of the envelope)have been shown to affect ITD coding, however neural factors that affect theencoding of ITD in BiCI listeners are unknown.• The current study presents a physiologically inspired model of ITD coding in BiCIlisteners, which simulated the responses of electrically stimulated auditory nervefibers (ANF) from ipsilateral and contraleteral ears and performs a coincidencedetection using a neural cross correlation analysis.• The model predictions are compared with behavioral data from several studies.Cross-correlation model of ITD codingFigure 1 The structure ofthe cross-correlation model ofITD coding, consisting modelof electrically stimulated au-ditory nerve fiber responses,cross-correlation stage anddecision device.Model of electrically-stimulated auditory nerve responsesFigure 2 The structure ofthe model of electricallystimulated auditory nervefiber responses (Joshi etal, in press). The modelconsists of two sites ofspike generation as well assub-thresholds and supra-threshold adaptive currentfeedback loops. The modelhas been shown to cor-rectly account for the effectof stimulation with vari-ous pulse shapes and pulserates on response statisticsof the ANFs.StimuliCarrier rate(A)Envelope slope(B)Silent gap(C)Modulation frequency(D)Inter-pulse interval jitter(E)Figure 3 Illustration of stim-uli used in the current study,showing the variation in car-rier rate (A), variation in en-velope defined using enve-lope slope (B) or duration ofthe silent gap between theenvelope bursts (C), varia-tion in sinusoidal modulationfrequency (D) and jitter ofthe inter-pulse-intervals (E).Neurometric analysis2000 1000 0 1000 2000delay (µs)# count(A) SCC2000 1000 0 1000 2000delay (µs)# count(B) Weighted SCC0 µs160 µs480 µs28 29 30 31 32 33 34Decision veriable, D# count(C) Distributions of D0 µs160 µs480 µs10 100 1000ITD (µs)0.40.60.81.0% correct(D) Neurometric function% correctFitted functionITD-JNDFigure 4 Examples of the SCC functions for a pulse train of 1000 pps with an im-posed ITDs of 0 μs (blue line), 160 μs (green line) and 480 μs (red line) are shownin A. Corresponding centrality-weighted correlograms are shown in B. The resultingdistributions of decision variables for the three ITDs based on 100 repetitions of themodel simulations are shown in C for each of the three ITD values. The distributionsof D show that the is highest for ITD = 0 μs and decreases with increasing the ITD. Anexample of the neurometric function fitted to the percent discrimination as a functionof the value of imposed ITDs is shown in D.Results10 20 40 80Spike/second2550100200400800ITD-JND (µs)10 20 30 40 50 60 70 80% Dynamic RangeModelE16-BE16-CE16-DE16-trendFigure 5 Predicted ITD-JNDs (squares)as a function of spike-rate for 100 ppspulse train. The correspondingbehavioral data from Egger et al(2016) are indicated with differentsymbols as a function of percentdynamic range indicated on the topaxis. The dashed line indicates atrend-line through the data.Figure 6 The predicted effect ofcarrier pulse rate on ITD-JNDs(blue squares), along withcorresponding behavioral datafrom Laback et al. (2007) and vanHoesel (2007) and van Hoesel(2009).100 400 1600 6400Carrier rate (pps)501002004008001600ITD-JND (µs)L07vH07vH09Model0 5 10 15 20 25Off time (ms)20040080016003200ITD-JND (µs)Model9 ms6.8 ms5.4 ms4.5 ms0 5 10 15 20 25Off time (ms)DataFigure 5 The predicted effect of envelope shape on ITD-JNDs. The ITD-JNDsfor the envelope slope of 9 ms (squares), 6.8 ms (circles), 5.4 ms (downwardtriangles) and 4.5 ms (upward triangles) are indicated as a function of the silentgap duration. The corresponding behavioral data from Laback et al. (2011) isshown for a comparison.2 10 100 1000Modulation frequency (Hz)10100100010000ITD-JND (µs)Model1000 pps4000 pps2 10 100 1000Modulation frequency (Hz)DataNE13-1000 ppsvH09-6000 ppsFigure 6 The predicted effect of modulation frequency on ITD-JNDs for carrierrates of 1000 pps (squares) and 4000 pps (circles). The corresponding behav-ioral data from Noel and Eddington (2013) and van Hoesel et al. (2009) areshown for a comparison. The model can predict reasonably accurate trends forthe modulation frequencies up to 100 Hz.100 400 1600 6400Carrier rate (pps)100100010000ITD-JND (µs)Modelk = 0.000k = 0.125 - 0.5k = 0.75 - 0.9100 400 1600 6400Carrier rate (pps)DataFigure 7 The predicted effect inter-pulse-interval jitter on the ITD-JNDs in-dicated as a function of the carrier rate along with corresponding data fromLaback and Majdak (2008).Discussion• A physiologically inspired phenomenological model of ITD coding in bilateral CIlisteners is presented and the model predictions were compared with behavioraldata from several studies.• The model can successfully account for the effect of various stimulus parame-ters such as the stimulus level, carrier pulse rate, envelope shape, modulationfrequency and the inter-pulse-interval jitter on the ITD-JNDs observed in the data.• In case of the amplitude modulated pulse train, the model can account for theeffect of modulation frequencies up to 100 Hz. Beyond 100 Hz, the model predictslowering in the ITD-JND whereas the data shows a slight increase.• The model can be used to study the effect of various stimulus and auditory nervefiber related parameters on the coding of neural coding and perception of ITDs.Acknowledgements: The authors would like to thank Bernhard Labackfor sharing their data. This work was supported by the EuropeanUnion through a grant from the People Programme under REA grantsagreement number PITN-GA-2012-317521.References: Joshi et al (in press), JARO; Joris (2003) JNeurosci 23(15); Shackle-ton et al (1992). JASA, 91(4); Egger et al (2016) JARO 17(1); Laback et al (2007) JASA,121(4); Laback & Majdak (2008) PNAS 105(2); Van Hoesel (2007) JASA, 121(4); vanHoesel et al (2009) JARO, 10(4); Noel & Eddington (2013) JASA, 133(4)

Cross-correlation model of interaural time difference coding in listeners with bilateral cochlear implants

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Cross-correlation model of interaural time difference coding in listeners with bilateral cochlear implants

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