Hearing - From Sensory Processing to Perception

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Hearing – From Sensory Processing to Perception

B. Kollmeier G. Klump V. Hohmann U. Langemann M. Mauermann S. Uppenkamp J. Verhey (Eds.)

Hearing – From Sensory Processing to Perception With 224 Figures

Prof. Dr. Birger Kollmeier Prof. Dr. Georg Klump Dr. Volker Hohmann Dr. Ulrike Langemann Dr. Manfred Mauermann Dr. Stefan Uppenkamp Dr. Jesko Verhey Fakult¨at V Institut f¨ur Physik Carl-von-Ossietzky Universit¨at 26111 Oldenburg Germany

Library of Congress Control Number: 2007928331 ISBN: 978-3-540-73008-8 Springer Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permissions for use must always be obtained from Springer-Verlag. Violations are liable for prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2007 The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Editor: Dr. Dieter Czeschlik, Heidelberg, Germany Desk editor: Dr. Jutta Lindenborn, Heidelberg, Germany Cover design: WMXDesign GmbH, Heidelberg, Germany Production and typesetting: SPi Printed on acid-free paper SPIN 11915300

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Preface

The current book presents the written contributions to a kind of “World summit on hearing research”, i.e., the “International Symposium on Hearing” (ISH 2006) which was held in Cloppenburg, a small northern German town close to Oldenburg and Bremen in August 2006. The International Symposium on Hearing has been held approximately every three years in Europe since 1969. The participants come from groups mostly in Europe and in the USA that focus on a wide range of topics in research on auditory system function. It is a hallmark of this truly interdisciplinary meeting to bring together well known researchers specializing on psychophysics, physiology and models of hearing. This connection stimulates the discussion on the physiological mechanisms underlying perception and provides the basis for a better understanding of auditory function. Modelling approaches complement the experimental studies and serve as a framework for interpreting the results and developing new experimental paradigms. The main themes of the current meeting are at the focus of interest in hearing research. The physiological representation of the temporal and the spectral structure of stimuli on different levels of the auditory system is a pervasive topic of the studies presented at the meeting, helping us to understand the perception of modulation patterns, pitch and signal intensity. Our knowledge of the physiological mechanisms of binaural processing in mammals is developing further, providing an improved basis for understanding spatial hearing. How the different stimulus features are integrated into auditory scene analysis and which physiological mechanisms allow the formation of auditory objects is another unifying theme linking researchers focussing on modeling, physiology and psychophysics. Finally, the topics of speech perception and the limitations of auditory perception resulting from hearing disorders were discussed on the basis of our understanding of the physiology of the auditory system. The chapters of this volume with the proceedings of the “14th International Symposium on Hearing” provide an up-to-date status of the field of hearing research. We hope that it will stimulate further discussion and will also enable newcomers to the field to access the newest developments in our understanding of auditory system function and auditory perception. The organizers of the ISH 2006 and editors of this book are affiliated with the Universität Oldenburg where one of the largest European centres for

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Preface

hearing research is located. Institutional support for the ISH 2006 was therefore provided by: ● Kompetenzzentrum HörTech (i.e., national centre of competence for hearing aid system technology, located in the “house of hearing” in Oldenburg) ● Sonderforschungsbereich/Transregio “Das aktive Gehör” (Oldenburg/ Magdeburg, i.e., collaborative research center “the active auditory system” supported by DFG) ● Internationales Graduiertenkolleg (international research training site) “neurosensory science, systems, and applications” (Oldenburg/Groningen, supported by DFG and NWO) Further financial support was kindly provided by Widex A/S and Siemens Audiologische Technik (SAT). The organizers wish to thank these institutions and all individuals that made the ISH 2006 an unforgettable event. Oldenburg, December 2006

Birger Kollmeier, Georg Klump, Volker Hohmann, Ulrike Langemann, Manfred Mauermann, Stefan Uppenkamp, and Jesko Verhey

List of participants (and key to photograph) Bahmer, Andreas Beutelmann, Rainer Bleeck, Stefan Carlyon, Bob Carney, Laurel L. Carr, Catherine E. Chait, Maria Chen, Hsi-Pin Christiansen, Thomas Ulrich Colburn, Steve de Cheveigné, Alan Demany, Laurent Dietz, Matthias Divenyi, Pierre

49 89 13 80 91 16 17 73 45 1 28 82 26 36

Dooling, Robert J. Duifhuis, Hendrikus Egorova, Marina El Hilali, Mounya Emiroglu, Suzan Englitz, Bernhard Ernst, Stephan Ewert, Stephan D. Festen, Joost M. Garre, Susanne Ghitza, Oded Gleich, Otto Goossens, Tom Goupell, Matthew Joseph

43 88 77 81 4 22 25 19 59 71 46 34 87 66

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Goverts, Theo Greenberg, Steven Grimault, Nicolas Hage, Steffen R. Hall, Deborah A. Hancock, Kenneth E. Hansen, Hans Hartmann, William M. Heinz, Michael G. Heise, Stephan Henning, G. Bruce Hohmann, Volker Junius, Dirk Kashino, Makio Klinge, Astrid Klump, Georg Kohlrausch, Armin Kollmeier, Birger Langemann, Ulrike Langers, Dave R.M. Langner, Gerald Leek, Marjorie R. Leijon, Arne Long, Glenis Lopez-Poveda, Enrique A. Lüddemann, Helge Lütkenhöner, Bernd Marquardt, Torsten Mauermann, Manfred McAlpine, David Meddis, Raymond Meyer, Julia Micheyl, Christophe Narins, Peter M. Neher, Tobias Nelson, Paul Palmer, Alan R. Patterson, Roy D. Plack, Christopher J.

List of Participants

11 75 2 63 67 31 50 74 32 93 53 10 24 39 72 21 35 9 7 83 15 33 62 23 30 37 68 90 6 27 40 55 94 84 54 61 78 14 42

Pressnitzer, Daniel Riedel, Helmut Roberts, Brian Rupp, Andre Schimmel, Othmar Schmidt, Erik Schoffelen, Rick Shackleton, Trevor M. Shamma, Shihab A. Shinn-Cunningham, Barbara Simon, Jonathan Z. Siveke, Ida Strahl, Stefan Trahiotis, Constantine Tsuzaki, Minoru Unoki, Masashi Uppenkamp, Stefan van Beurden, Maarten F. B. van de Par, Steven Verhey, Jesko Lars Watkins, Anthony Weber, Reinhard Wiegrebe, Lutz Winter, Ian Michael Yasin, Ifat Yost, William A. Young, Eric D. Not in photograph: Bilsen, Frans A. Culling, John F. Delgutte, Bertrand Devore, Sasha Ihlefeld, Antje Kaernbach, Christian Seeber, Bernhard U. van Dijk, Pim Tollin, Daniel J.

69 60 47 48 86 12 92 64 79 3 18 52 76 65 20 38 5 44 57 8 56 51 70 29 41 85 58

Contents

Part I

Cochlea/Peripheral Processing

1

Influence of Neural Synchrony on the Compound Action Potential, Masking, and the Discrimination of Harmonic Complexes in Several Avian and Mammalian Species . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 OTTO GLEICH, MARJORIE LEEK, AND ROBERT DOOLING

2

A Nonlinear Auditory Filterbank Controlled by Sub-band Instantaneous Frequency Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11 VOLKER HOHMANN AND BIRGER KOLLMEIER

3

Estimates of Tuning of Auditory Filter Using Simultaneous and Forward Notched-noise Masking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19 MASASHI UNOKI, RYOTA MIYAUCHI, AND CHIN-TUAN TAN

4

A Model of Ventral Cochlear Nucleus Units Based on First Order Intervals . . .27 STEFAN BLEECK AND IAN WINTER

5

The Effect of Reverberation on the Temporal Representation of the F0 of Frequency Swept Harmonic Complexes in the Ventral Cochlear Nucleus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35 MARK SAYLES, BERT SCHOUTEN, NEIL J. INGHAM, AND IAN M. WINTER

6

Spectral Edges as Optimal Stimuli for the Dorsal Cochlear Nucleus . . . . . . . . . .43 SHARBA BANDYOPADHYAY, ERIC D. YOUNG, AND LINA A. J. REISS

7

Psychophysical and Physiological Assessment of the Representation of High-frequency Spectral Notches in the Auditory Nerve . . . . . . . . . . . . . . . . .51 ENRIQUE A. LOPEZ-POVEDA, ANA ALVES-PINTO, AND ALAN R. PALMER

Part II

Pitch

8

Spatio-Temporal Representation of the Pitch of Complex Tones in the Auditory Nerve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61 LEONARDO CEDOLIN AND BERTRAND DELGUTTE

9

Virtual Pitch in a Computational Physiological Model . . . . . . . . . . . . . . . . . . . . .71 RAY MEDDIS AND LOWEL O’MARD

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10

Searching for a Pitch Centre in Human Auditory Cortex . . . . . . . . . . . . . . . . . .83 DEB HALL AND CHRISTOPHER PLACK

11

Imaging Temporal Pitch Processing in the Auditory Pathway . . . . . . . . . . . . . .95 ROY D. PATTERSON, ALEXANDER GUTSCHALK, ANNEMARIE SEITHER-PREISLER, AND KATRIN KRUMBHOLZ

Part III

Modulation

12

Spatiotemporal Encoding of Vowels in Noise Studied with the Responses of Individual Auditory-Nerve Fibers . . . . . . . . . . . . . . . . . . . . . .107 MICHAEL G. HEINZ

13

Role of Peripheral Nonlinearities in Comodulation Masking Release . . . . . .117 JESKO L. VERHEY AND STEPHAN M.A. ERNST

14

Neuromagnetic Representation of Comodulation Masking Release in the Human Auditory Cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .125 ANDRÉ RUPP, LIORA LAS, AND ISRAEL NELKEN

15

Psychophysically Driven Studies of Responses to Amplitude Modulation in the Inferior Colliculus: Comparing Single-Unit Physiology to Behavioral Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .133 PAUL C. NELSON AND LAUREL H. CARNEY

16

Source Segregation Based on Temporal Envelope Structure and Binaural Cues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .143 STEVEN VAN DE PAR, OTHMAR SCHIMMEL, ARMIN KOHLRAUSCH, AND JEROEN BREEBAART

17

Simulation of Oscillating Neurons in the Cochlear Nucleus: A Possible Role for Neural Nets, Onset Cells, and Synaptic Delays . . . . . . . . .155 ANDREAS BAHMER AND GERALD LANGNER

18

Forward Masking: Temporal Integration or Adaptation? . . . . . . . . . . . . . . . . .165 STEPHAN D. EWERT, OLE HAU, AND TORSTEN DAU

19

The Time Course of Listening Bands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .175 PIERRE DIVENYI AND ADAM LAMMERT

Part IV

Animal Communication

20

Frogs Communicate with Ultrasound in Noisy Environments . . . . . . . . . . . . .185 PETER M. NARINS, ALBERT S. FENG, AND JUN-XIAN SHEN

21

The Olivocochlear System Takes Part in Audio-Vocal Interaction . . . . . . . . .191 STEFFEN R. HAGE, UWE JÜRGENS, AND GÜNTER EHRET

Contents

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22

Neural Representation of Frequency Resolution in the Mouse Auditory Midbrain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .199 MARINA EGOROVA , INNA VARTANYAN, AND GUENTER EHRET

23

Behavioral and Neural Identification of Birdsong under Several Masking Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .207 BARBARA G. SHINN-CUNNINGHAM, VIRGINIA BEST, MICHEAL L. DENT, FREDERICK J. GALLUN, ELIZABETH M. MCCLAINE, RAJIV NARAYAN, EROL OZMERAL, AND KAMAL SEN

Part V

Intensity Representation

24

Near-Threshold Auditory Evoked Fields and Potentials are In Line with the Weber-Fechner Law . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .215 BERND LÜTKENHÖNER, JAN-STEFAN KLEIN, AND ANNEMARIE SEITHER-PREISLER

25

Brain Activation in Relation to Sound Intensity and Loudness . . . . . . . . . . . .227 DAVE LANGERS, WALTER BACKES, AND PIM VAN DIJK

26

Duration Dependency of Spectral Loudness Summation, Measured with Three Different Experimental Procedures . . . . . . . . . . . . . . . . . . . . . . . . . .237 MAARTEN F.B. VAN BEURDEN AND WOUTER A. DRESCHLER

Part VI

Scene Analysis

27

The Correlative Brain: A Stream Segregation Model . . . . . . . . . . . . . . . . . . . . .247 MOUNYA ELHILALI AND SHIHAB SHAMMA

28

Primary Auditory Cortical Responses while Attending to Different Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .257 PINGBO YIN, LING MA, MOUNYA ELHILALI, JONATHAN FRITZ, AND SHIHAB SHAMMA

29

Hearing Out Repeating Elements in Randomly Varying Multitone Sequences: A Case of Streaming? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .267 CHRISTOPHE MICHEYL, SHIHAB A. SHAMMA, AND ANDREW J. OXENHAM

30

The Dynamics of Auditory Streaming: Psychophysics, Neuroimaging, and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .275 MAKIO KASHINO, MINAE OKADA, SHIN MIZUTANI, PETER DAVIS, AND HIROHITO M. KONDO

31

Auditory Stream Segregation Based on Speaker Size, and Identification of Size-Modulated Vowel Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .285 MINORU TSUZAKI, CHIHIRO TAKESHIMA, TOSHIO IRINO, AND ROY D. PATTERSON

32

Auditory Scene Analysis: A Prerequisite for Loudness Perception . . . . . . . . .295 NICOLAS GRIMAULT, STEPHEN MCADAMS, AND JONT B. ALLEN

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Modulation Detection Interference as Informational Masking . . . . . . . . . . . .303 STANLEY SHEFT AND WILLIAM A. YOST

34

A Paradoxical Aspect of Auditory Change Detection . . . . . . . . . . . . . . . . . . . . .313 LAURENT DEMANY AND CHRISTOPHE RAMOS

35

Human Auditory Cortical Processing of Transitions Between ‘Order’ and ‘Disorder’ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .323 MARIA CHAIT, DAVID POEPPEL, AND JONATHAN Z. SIMON

36

Wideband Inhibition Modulates the Effect of Onset Asynchrony as a Grouping Cue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .333 BRIAN ROBERTS, STEPHEN D. HOLMES, STEFAN BLEECK, AND IAN M. WINTER

37

Discriminability of Statistically Independent Gaussian Noise Tokens and Random Tone-Burst Complexes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .343 TOM GOOSSENS, STEVEN VAN DE PAR, AND ARMIN KOHLRAUSCH

38

The Role of Rehearsal and Lateralization in Pitch Memory . . . . . . . . . . . . . . .353 CHRISTIAN KAERNBACH, KATHRIN SCHLEMMER, CHRISTINA ÖFFL, AND SANDRA ZACH

Part VII

Binaural Hearing

39

Interaural Correlation and Loudness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .359 JOHN F. CULLING AND BARRIE A. EDMONDS

40

Interaural Phase and Level Fluctuations as the Basis of Interaural Incoherence Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .369 MATTHEW J. GOUPELL AND WILLIAM M. HARTMANN

41

Logarithmic Scaling of Interaural Cross Correlation: A Model Based on Evidence from Psychophysics and EEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .379 HELGE LÜDDEMANN, HELMUT RIEDEL, AND BIRGER KOLLMEIER

42

A Physiologically-Based Population Rate Code for Interaural Time Differences (ITDs) Predicts Bandwidth-Dependent Lateralization . . . . . . . . .389 KENNETH E. HANCOCK

43

A p-Limit for Coding ITDs: Neural Responses and the Binaural Display . . . .399 DAVID MCALPINE, SARAH THOMPSON, KATHARINA VON KRIEGSTEIN, TORSTEN MARQUARDT, TIMOTHY GRIFFITHS, AND ADENIKE DEANE-PRATT

44

A p-Limit for Coding ITDs: Implications for Binaural Models . . . . . . . . . . . .407 TORSTEN MARQUARDT AND DAVID MCALPINE

45

Strategies for Encoding ITD in the Chicken Nucleus Laminaris . . . . . . . . . . .417 CATHERINE CARR AND CHRISTINE KÖPPL

Contents

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46

Interaural Level Difference Discrimination Thresholds and Virtual Acoustic Space Minimum Audible Angles for Single Neurons in the Lateral Superior Olive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .425 DANIEL J. TOLLIN

47

Responses in Inferior Colliculus to Dichotic Harmonic Stimuli: The Binaural Integration of Pitch Cues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .435 TREVOR M. SHACKLETON, LIANG-FA LIU, AND ALAN R. PALMER

48

Level Dependent Shifts in Auditory Nerve Phase Locking Underlie Changes in Interaural Time Sensitivity with Interaural Level Differences in the Inferior Colliculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .447 ALAN R. PALMER, LIANG-FA LIU, AND TREVOR M. SHACKLETON

49

Remote Masking and the Binaural Masking-Level Difference . . . . . . . . . . . . .457 G. BRUCE HENNING, IFAT YASIN, AND CAROLINE WITTON

50

Perceptual and Physiological Characteristics of Binaural Sluggishness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .467 IDA SIVEKE, STEPHAN D. EWERT, AND LUTZ WIEGREBE

51

Precedence-Effect with Cochlear Implant Simulation . . . . . . . . . . . . . . . . . . . .475 BERNHARD U. SEEBER AND ERVIN HAFTER

52

Enhanced Processing of Interaural Temporal Disparities at High-Frequencies: Beyond Transposed Stimuli . . . . . . . . . . . . . . . . . . . . . . . . .485 LESLIE R. BERNSTEIN AND CONSTANTINE TRAHIOTIS

53

Models of Neural Responses to Bilateral Electrical Stimulation . . . . . . . . . . .495 H. STEVEN COLBURN, YOOJIN CHUNG, YI ZHOU, AND ANDREW BRUGHERA

54

Neural and Behavioral Sensitivities to Azimuth Degrade with Distance in Reverberant Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .505 SASHA DEVORE, ANTJE IHLEFELD, BARBARA G. SHINN-CUNNINGHAM, AND BERTRAND DELGUTTE

Part VIII

Speech and Learning

55

Spectro-temporal Processing of Speech – An Information-Theoretic Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .517 THOMAS U. CHRISTIANSEN, TORSTEN DAU, AND STEVEN GREENBERG

56

Articulation Index and Shannon Mutual Information . . . . . . . . . . . . . . . . . . . .525 ARNE LEIJON

57

Perceptual Compensation for Reverberation: Effects of ‘Noise-Like’ and ‘Tonal’ Contexts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .533 ANTHONY WATKINS AND SIMON MAKIN

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58

Towards Predicting Consonant Confusions of Degraded Speech . . . . . . . . . .541 O. GHITZA, D. MESSING, L. DELHORNE, L. BRAIDA, E. BRUCKERT, AND M. SONDHI

59

The Influence of Masker Type on the Binaural Intelligibility Level Difference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .551 S. THEO GOVERTS, MARIEKE DELREUX , JOOST M. FESTEN, AND TAMMO HOUTGAST

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .559

1 Influence of Neural Synchrony on the Compound Action Potential, Masking, and the Discrimination of Harmonic Complexes in Several Avian and Mammalian Species OTTO GLEICH1, MARJORIE LEEK2, AND ROBERT DOOLING3

1

Introduction

An important goal of comparative auditory research is to understand the relationship between structure, mechanisms, and function. The ears of mammals and birds are quite different along many dimensions, but the hearing abilities are remarkably similar on a variety of psychoacoustic tasks (Dooling et al. 2000). However, tests involving temporal fine structure now show interesting differences between birds and humans that may permit a more penetrating analysis of the role of structural and mechanical variation among species in the processing of complex sounds. One major difference between birds and mammals related to substantial differences in cochlear dimensions is the frequency dependent cochlear response delay. In this chapter we analyze how physiological responses and psychoacoustic measures of masking and discrimination may be accounted for by an interaction of species-specific cochlear response delay and the time distribution of harmonic frequencies within these complexes.

2 2.1

Methods Stimuli

Stimuli for the studies reviewed here were harmonic complexes with equalamplitude components and component phases selected to produce complexes with monotonically increasing or decreasing frequency across each fundamental period. Complete descriptions of these stimuli may be found in Leek et al. (2005) and in Lauer et al. (2006). These complexes, generally called “Schroeder complexes”, have component frequencies from 0.2 to 5 kHz, and a fundamental frequency of 100 Hz. Variants of the original Schroeder phase 1

ENT Department, University of Regensburg, Germany, [email protected] National Center for Rehabilitative Auditory Research, Portland VA, Medical Center, USA, [email protected] 3 Department of Psychology, University of Maryland, USA, [email protected] 2

Hearing – From Sensory Processing to Perception B. Kollmeier, G. Klump, V. Hohmann, U. Langemann, M. Mauermann, S. Uppenkamp, and J. Verhey (Eds.) © Springer-Verlag Berlin Heidelberg 2007

2

O. Gleich et al.

algorithm (Schroeder 1970) include a scalar, C, ranging from ±1.0 in steps of 0.1, that serves to increase or decrease the rate of change of frequency across each fundamental period. 2.2

Physiological Measures

The procedures to record evoked cochlear potentials in response to harmonic complexes are described in detail in Dooling et al. (2001). The stimulus waveforms were those shown in Fig. 1 as well as inverted versions to cancel the cochlear microphonic response and isolate the compound action potential (CAP) as a measure of neural synchronization. The stimulus level used for the CAP measurements was set to 70 dBSPL. Physiological data were collected from three budgerigars, two canaries, one zebra finch, four gerbils and two guinea pigs. 2.3

Frequency Specific Cochlear Delay

The cochlear delay functions were derived as best fit power functions from scatter plots of published data relating response delay to frequency. These

Fig. 1 Waterfall display of three periods of the waveform for harmonic complexes with a fundamental frequency of 100 Hz created by systematically varying the scalar C in 0.1-steps from −1.0 to +1.0 as indicated by the number at the right side of each waveform. The gray lines in each trace indicate the variation of instantaneous frequency between 0.2 and 5.0 kHz over time. The greater the slope of these lines, the more rapid are the within-period frequency sweeps

Influence of Neural Synchrony on the Compound Action

3

data come predominantly from auditory nerve fiber recordings in birds (Sachs et al 1974; Gleich and Narins 1988), guinea pig (Palmer and Russell 1986) and gerbil (Schmiedt and Zwislocki 1977) and have been corrected by 1 ms to account for neural delay. Additional bird data came from mechanical measurements of basilar membrane response latency in pigeon (Gummer et al. 1987). Human data are based on the derived ABR data shown in Fig. 3d of Schoonhoven et al. (2001) and frequency specific wave V latency data presented in Table 1 of Donaldson and Ruth (1993), adjusted by 5.3 ms. The resulting best fit power functions relating frequency to cochlear delay for the different species are: human, y = 3.4138x−0.7396; gerbil, y = 0.502x−1.5836; guinea pig, y = 1.6394x−0.7496 and bird, y = 0.6813x−0.6121 with x representing frequency in kHz and y being the delay in ms.

3 3.1

Results CAP Amplitude as a Function of Scalar Value

Mean CAP amplitudes are illustrated in Fig. 2 as a function of the scalar value, demonstrating a species specific variation of the CAP amplitude. Negative scalars are on average associated with higher CAP amplitudes than positive scalars, consistent with the notion that upward frequency sweeps tend to “compensate” cochlear delay and cause a higher degree of synchronization compared to downward sweeps (e.g. positive scalars). A prediction for humans, which will be described in the next section, is illustrated as the thick black line in Fig. 2.

Fig. 2 Mean CAP amplitude as a function of scalar value is shown for bird, gerbil and guinea pig, with the number of animals in each group indicated in the legend. The thick black line shows a prediction for CAP amplitude in humans based on the regression line described in the next section

4

O. Gleich et al.

3.2 Cochlear Activation: Interaction of Stimulus Related Frequency Timing and Cochlear Delay Figure 3 illustrates that cochlear activation over stimulus periods varies considerably between species and scalars. The difference in the duration of cochlear activation by one stimulus period is more pronounced in mammals due to the long response delays at low frequencies, and the difference between positive and negative scalars decreases for scalar values close to 0. A stimulus perfectly compensating cochlear delay would result in synchronized cochlear activation across frequencies and an activation function represented by a vertical line in Fig. 3. A high degree of synchronization should result in a maximized CAP amplitude (Dau et al. 2000). Obviously, none of the harmonic stimuli perfectly compensates cochlear delay in the species studied. To obtain a quantitative measure of the degree of synchronization we determined the maximum frequency range activated by a single period within a 0.5-ms time window (i.e. around the steepest portion of the cochlear activation functions shown in Fig. 3) as a function of the scalar value for the different species. Since the frequency representation in all species can be regarded as roughly logarithmic, we used the maximally synchronized cochlear region expressed as octaves for this comparison. Figure 4 demonstrates that all species show a maximum synchronization for negative scalars

Fig. 3 Cochlear activation over three stimulus periods for four different scalars in human, gerbil, guinea pig and bird. The dotted line indicates the frequency timing within the stimulus

Influence of Neural Synchrony on the Compound Action

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Fig. 4 The left panel shows the cochlear region responding within a 0.5-ms time window, expressed in octaves, as a function of the scalar value. The right panel plots the CAP amplitude derived from the mean curves in Fig. 2 as a function of the corresponding synchronized octaves of a given scalar shown in the left panel

of −0.1 or −0.2. Overall, birds show a higher degree of synchronization when expressed in octaves. If the frequency scale is converted to physical location on the sensory epithelium the maximally synchronized region is 9 mm for a scalar of −0.2 in humans (almost one third of the organ of Corti) and 2 mm for a scalar of −0.1 in birds (corresponding to 70% of the basilar papilla). The shape of the curves illustrating the synchronized octaves as a function of scalar value (Fig. 4, left panel) is similar to the shape of the mean CAP-amplitude curves (Fig. 2). The right panel in Fig. 4 demonstrates a highly significant correlation between the physiologically determined CAP and the synchronized cochlear region responding within 0.5 ms.

4

Discussion

Based on vertebrate cochlear frequency representation, harmonic complexes with within-period frequency sweeps from low to high (negative complexes) can be expected to synchronize neural responses better than those with downward sweeping instantaneous frequencies (positive complexes) because they “compensate” cochlear delays (see also Dau et al. 2000). This is consistent with the general observation that CAP amplitudes for negative scalars tend to be higher than those in response to positive scalars (Fig. 2). Since frequency within a given period of the harmonic complex varies linearly over time (Fig. 1) and the cochlear delay shows a highly non-linear variation with frequency, the interaction results in a complex pattern of cochlear activation over consecutive stimulus periods (Fig. 3). The right diagram in Fig. 4 demonstrates that the analysis of the temporal interaction of an acoustic stimulus and cochlear delay looking at an arbitrarily selected 0.5-ms time window allows reasonable predictions of neural synchronization and CAP amplitude. The scalar dependent variation of cochlear synchronization (Fig. 4) or CAP amplitude (Fig. 2) differ substantially from the pattern of scalar dependent

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variation in the degree of masking reported by Lauer et al. (2006). In an attempt to derive a measure from the cochlear activation analysis (Fig. 2) that might be used to predict the scalar dependent degree of masking, we calculated the time of cochlear activation within one period of the harmonic complex for the frequency range between 2.6 and 3.0 kHz around the signal frequency of 2.8 kHz used by Lauer et al. (2006). The hypothesis is that longer cochlear activation around the signal frequency will cause more masking compared to shorter activation. Fig. 5 shows that there is a good correlation for positive and negative scalars in birds. Data for negative scalars in humans are similar to data from birds, but masking by positive scaled complexes appears independent of the duration of cochlear activation. In order to assess whether the differences across species regarding scalar discrimination might be reconciled by taking cochlear activation into account, the data from the Leek et al. (2005) study were replotted as a function of estimates of the difference in total duration of cochlear activation by one period of the standard and the corresponding scaled complex (Fig. 6).

Fig. 5 The diagram shows masked threshold (taken from Lauer et al. 2006) as a function of cochlear activation for the frequency range between 2.6 and 3.0 kHz. Open symbols: negative scalars, filled symbols: positive scalars

Fig. 6 The probability for a correct discrimination as a function of the absolute difference in the duration of cochlear activation between the standard (−1, +1 and 0) and the scaled complexes for humans (left diagram) and birds (right diagram)

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Discriminability of these scaled harmonic complexes from either cosinephase (i.e., scalar = 0) or from scalars of ±1.0 generally improves when the difference between the standard and the signal in the duration of cochlear activation increases (Fig. 6).

5

Conclusions

These results show that several physiological and behavioral measures of the processing of harmonic complexes are remarkably similar across a number of very diverse species when considered in terms of two simple parameters: species-specific cochlear response delay and the time distribution of harmonic frequencies within the harmonic complex. Variation in CAP amplitude across harmonic complexes correlates well with the spatial extent of cochlear activation. The duration of cochlear activation around probe frequency is consistent with masking data from birds but does not explain the reduced masking seen in humans with positive scaled harmonic complexes. Discriminability of harmonic complexes is generally related to differences in the duration of cochlear activation except for birds discriminating negative scalars from a cosine background. Acknowledgments. Supported by NIH Grants DC-00198 to RJD and DC-00626 to MRL.

References Dau T, Wegner O, Mellert V, Kollmeier B (2000) Auditory brainstem responses with optimized chirp signals compensating basilar-membrane dispersion. J Acoust Soc Am 107:1530–1540 Donaldson GS, Ruth RA (1993) Derived band auditory brain-stem response estimates of traveling wave velocity in humans. I: Normal-hearing subjects. J Acoust Soc Am 93:940–951 Dooling RJ, Lohr B, Dent ML (2000) Hearing in birds and reptiles. In: Dooling RJ, Fay RR, Popper AN (eds) Comparative hearing: birds and reptiles. Springer, berlin Heidelberg New York, pp 308–359 Dooling RJ, Dent ML, Leek MR, Gleich O (2001) Masking by harmonic complexes in three species of birds: psychophysical thresholds and cochlear responses. Hear Res 152:159–172 Gleich O, Narins PM (1988) The phase response of primary auditory afferents in a songbird. Hear Res 32:81–92 Gummer AW, Smolders JW, Klinke R (1987) Basilar membrane motion in the pigeon measured with the Mossbauer technique. Hear Res 29:63–92 Lauer AM, Dooling RJ, Leek MR, Lentz JJ (2006) Phase effects in masking by harmonic complexes in birds. J Acoust Soc Am 119:1251–1259 Leek MR, Dooling RJ, Gleich O, Dent ML (2005) Discrimination of temporal fine structure by birds and mammals. In: Presnitzer D, Cheveigne A, McAdams S, Collet L (eds) Auditory signal processing. Springer Science+Business Media, p 471–477 Palmer AR, Russell IJ (1986) Phase-locking in the cochlear nerve of the guinea-pig and its relation to the receptor potential of inner hair-cells. Hear Res 24:1–15

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Sachs MB, Young ED, Lewis RH (1974) Discharge patterns of single fibers in the pigeon auditory nerve. Brain Res 70:431–447 Schmiedt RA, Zwislocki JJ (1977) Comparison of sound-transmission and cochlear-microphonic characteristics in Mongolian gerbil and guinea pig. J Acoust Soc Am 61:133–149 Schoonhoven R, Prijs VF, Schneider S (2001) DPOAE group delays versus electrophysiological measures of cochlear delay in normal human ears. J Acoust Soc Am 109:1503–1512 Schroeder M (1970) Synthesis of low-peak-factor signals and binary sequences with low autocorrelation (Corresp.). Information Theory, IEEE Trans 16:85–89

Comment by Kohlrausch In your Fig. 5, you analyze masking properties of scaled Schroeder-phase complexes by your measure of cochlear activation (basilar-membrane response synchrony across a certain frequency range) in the spectral region of the signal. I wonder whether this property of the stimulus is primarily responsible for the amount of masking. The influence of masker phase on masking properties in such conditions has been explained quite successfully by the peakiness of the on-channel masker waveform after going through the inner-ear filter at the signal frequency (see the original publications by Smith et al. 1986 and Kohlrausch and Sander 1995, but also the recent paper by Lauer et al. 2006, all in JASA). According to this explanation, the Schroederphase masker producing the least amount of masking is the one for which the phase curvature is similar (and opposite) to the one of the relevant inner ear filter. Thus, masking depends primarily on the phase characteristic of an individual point of the basilar membrane, which is a priori independent of the place-dependent cochlear delay. For humans, the psychophysical data by Lauer et al. and by Lentz and Leek (2001) suggest that around 3 kHz, scalar values between 0.5 and +1 result in the least effective masker. For the bird condition, the least effective masker is one with a scalar value close to zero, i.e. a zero-(or sine-) phase masker. This has lead Lauer et al. to the conclusion that, around 3 kHz, the phase curvature of the corresponding inner ear filter in birds is a factor 4 to 8 smaller than the curvature in humans. Introducing a curvature in the stimulus phase spectrum (i.e., increasing the scalar from 0 to either +1 or −1) will for birds have two effects. First, and in my view most important, the energy at the output at the 2.8-kHz filter will be smeared out over a longer portion of each masker period as for the zerophase complex, leading to an increase in masking. Second, the synchrony across frequency will be reduced, because the frequency-dependent delays in the Schroeder-phase stimuli will be much larger than the place-dependent delay in the bird inner ear. For humans, on the other hand, the smearing out of the energy at the output of the corresponding inner-ear filter will only occur for negative scalar values (because stimulus phase curvature and filter phase curvature will add up to increase the resulting curvature, leading to a flat temporal envelope), but not for positive scalars (at least up to +1), for which the phase characteristics compensate each other to a certain extent.

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Such a view, based on within-channel masker waveforms, agrees with all experimental data for both humans and birds shown in the left panel of Fig. 5. References Kohlrausch A, Sander A (1995) Phase effects in masking related to dispersion in the inner ear. II. Masking period patterns of short targets. J Acoust Soc Am 97:1817–1829 Lentz JJ, Leek MR (2001) Psychophysical estimates of cochlear phase response: masking by harmonic complexes. J Assoc Res Otolaryngol 2:408–422 Smith BK, Sieben UK, Kohlrausch A, Schroeder MR (1986) Phase effects in masking related to dispersion in the inner ear. J Acoust Soc Am 80:1631–31637

Reply We are aware of, and agree with, the explanations reviewed by Dr. Kohlrausch regarding the masking data, and our data analysis is not inconsistent with the within-channel views. We were looking for a general analysis of cochlear activation patterns that could be related to various aspects of data on perception and processing of harmonic complexes. These included questions of synchronization (CAP), masking, and discrimination. The masking analyses of cochlear activation across a rough estimate of critical band around the probe frequency provides an alternative explanation for data obtained in birds that is consistent with within-channel masking in humans. These analyses reconcile masking differences across species except for the release from masking when the phase spectrum of the masker compensates for the phase characteristic of the sensory epithelium (as pointed out by Kohlrausch). They also are useful (if not perfect) explanations of the amplitudes of the compound action potentials and some aspects of discrimination across the complexes. Our goal was to find a physiological mechanism that would support all these experimental findings.

Comment by Lütkenhöner Did the scalar C affect only the amplitude of the compound action potential (CAP) or did you observe also changes in shape? In that case, it might be useful to consider alternative measures of the response magnitude, for example the area under the dominant CAP peak.

Reply The scalar not only affected the amplitude, but also the shape of the CAP waveform as illustrated for a set of typical examples in Fig. A. Despite these

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Fig. A Typical CAP waveforms in response to selected scaled harmonic complexes, collapsed across 10 periods from a gerbil (black lines) and a zebra finch (gray line). The inset in each diagram illustrates one period of the waveform of the corresponding harmonic complex

changes in the shape of the waveform, the peak to peak amplitude appears as a useful measure for the present analysis of synchronized cochlear activation (see also Fig. 4).

2 A Nonlinear Auditory Filterbank Controlled by Sub-band Instantaneous Frequency Estimates VOLKER HOHMANN AND BIRGER KOLLMEIER

1

Introduction

Functional models of basilar membrane motion have a long tradition and a wide range of applications. They usually take as input the stapes vibration and provide the excitation pattern of the inner hair cells as an output. Even though the design of these models and the psychophysical data put into the model design are based on simple signals (e.g. sinusoids and twotone complexes), these models have the advantage to be also applicable to complex sounds (such as speech). Hence, they describe the degree to which we understand the response of the human peripheral auditory system to every-day sounds. While transmission line and coupled elements models (such as, e.g., Duifhuis et al. 1985; Talmadge et al. 1998) are primarily used to describe the “effective” influence of physical parameters and mechanical properties on the basilar membrane response, filterbank models are primarily used to describe the “effective” signal processing properties of the basilar membrane at a fixed position on the BM. While single-filter approaches (both linear and – in more refined models – nonlinear filters) have been used in the past, dual resonance filter approaches (see, e.g., Goldstein 1988; Meddis et al. 2001) have been suggested more recently. They explicitly model the approximately linear response to input frequencies remote from the best frequency separately from the nonlinear, compressive response to frequencies close to the best frequency. This approach has the advantage of adequately describing the frequency-selective gain and instantaneous compression. However, it does not correctly describe suppression phenomena for configurations with high frequency separation between suppressor and suppressee: While psychoacoustic and physiological data show an increase of suppression up to 2.5 dB per dB with increasing suppressor level in lowside suppression, typical dual resonance filter models can only show a suppression rate of less than 1 dB per dB suppressor level. This originates from

Medizinische Physik, Fakultät V, Institut für Physik, Carl von Ossietzky Universität, Oldenburg, Germany, [email protected], [email protected] Hearing – From Sensory Processing to Perception B. Kollmeier, G. Klump, V. Hohmann, U. Langemann, M. Mauermann, S. Uppenkamp, and J. Verhey (Eds.) © Springer-Verlag Berlin Heidelberg 2007

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the fact that in these models the increase in suppression with suppressor level is directly coupled to the amount of compression in the on-frequency nonlinear filter. For this reason, the current paper describes a new approach that extends the idea of a dual resonance filter by including a control of the nonlinear filter gain by the sub-band instantaneous frequency. This approach enables us to control to a certain extent the amount of suppression separately from the frequency-dependent gain characteristics. The main idea is to appropriately model the observation that the auditory system yields an increasingly linear response with less gain to an on-frequency component as soon as an increasing level of off-frequency components falls into the respective on-frequency filter. Using this approach, two-tone suppression data with suppressor frequencies well below the on-frequency component (low-side suppression) will be considered in this paper as well as the different behavior of on- and off-frequency masking in simultaneous and non-simultaneous masking conditions.

2

Description of the Model

The key feature of the model introduced here is the extraction of the instantaneous frequency which is well defined for AM/FM-signals, e.g., signals after peripheral bandpass filtering. The instantaneous frequency (IF) can be derived efficiently within a few waveforms from the analytical signal s(t) by computing the derivative of its phase. The assumption is that the deviation of the instantaneous frequency from the best frequency of the channel determines the amount of gain and compression. The hypothetical physiological mechanism may be the instantaneous-frequency-dependent direction of outer haircell stereocilia deflection. According to Wersäll et al. (1965) the direction of outer haircell deflection differs between on-frequency excitation and offfrequency excitation at the same place on the basilar membrane with the sensitivity of the OHC being highest for the direction of motion induced by an on-frequency signal (see also Duifhuis 1976). Hence, the gain and compression for a given best frequency is modeled to depend on the difference between instantaneous frequency and the best frequency as given exemplarily in Fig. 1. The gain characteristic is obtained from a typical BM gain response curve obtained for ∆IF=0 ERB (no deviation between best frequency and instantaneous frequency). With increasing difference, the gain characteristics is modeled to become less compressive and achieves less gain at low input levels, i.e., gain(dB)=gain_exp(∆IF) × dbgain(L) where the gain exponent gain_exp decreases linearly with ∆IF from one to zero and dbgain(L) describes the standard on-frequency compressive input/output characteristic as a function of input level L. The complete design of one frequency channel of the model is given in Fig. 2. The dual-resonance approach is achieved with a wide, linear filter F1 in combination with a more frequency-selective filter F2 which is followed by

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Fig. 1 I/O-characteristics, i.e., output level as a function of input level for deviations of the instantaneous frequency from best frequency (∆IF-values) of 0, −0.8, −1.7 and −2 ERB. At ∆IF=−2 ERB and below, response is linear and gain is zero

Fig. 2 Block diagram of one frequency-channel of the model. F1: wide band filter, F2: narrow band filter, IC: instantaneous compression, IF: instantaneous frequency estimation

a nonlinear instantaneous compression circuit (IC, right column in Fig. 2) which adds its output to the output of F1. The control of this compression is achieved by a feed-forward simulation of the dual-resonance filter with fixed IC (middle column in Fig. 2) which is taken as the input to the instantaneous frequency estimation (IF). The resulting difference ∆IF is used to compute

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the gain_exp(∆IF) (see above) which alters the gain characteristic of the IC as shown in Fig. 1. Note that instead of employing two separate IC blocks for the feedforward structure in Fig. 2, only one IC block would be sufficient for a feedback structure where the output of the complete filter is used as input to the IF-circuit. Whereas such a feedback control might be simpler and more physiologically plausible, it would produce numerical instabilities. In addition, the chosen structure from Fig. 2 has the advantage that the compressed on-frequency components are used as input to the IF which is a necessary prerequisite for predicting the correct two-tone suppression characteristic (see below). The implementation of the model employs linear bandpass filters (F1, F2) which were chosen as double exponential filters parameterized by the respective centre frequency and the lower and upper slope in dB per octave (F1: lower slope 12 dB/octave, upper slope −48 dB/octave, F2: lower slope 60 dB/octave and upper slope −60 dB/octave). The filters were implemented as FFT-based minimum phase filters with a length of 1000 sample at a sampling frequency of 22.05 kHz using a complex output to approximate the analytical signals. A linear distribution of centre frequencies was obtained on an ERBscale with two filters per ERB. The instantaneous compression stage operates on a sample-by-sample basis on the Hilbert envelope (as in Herzke and Hohmann 2005) and uses the I/O-characteristics as sketched in Fig. 1. These parameters were selected in order to fit best to a variety of psychophysical masking data. Specifically, the parameters were fitted to best produce the two-tone suppression data and the upward spread of masking data were predicted using these fixed parameter settings.

3 3.1

Results Two-tone Suppression Data

Figure 3a shows psycho-acoustical two-tone suppression data obtained by Duifhuis (1980) where the suppressor level (L2 at 400 Hz) is given on the abscissa while the pulsation threshold of a tone (1 kHz) that achieves the same “internal level” as the suppressed tone (1 kHz) is given on the ordinate. Parameter of the curve is the level L1 of the suppressee. With increasing suppressor level, the “effective” suppressee level (given here by the level of the equivalent pulsation threshold) drops at a very high rate (approximately −2.5 dB per dB suppressor level) as soon as the suppressor level exceeds a suppression threshold. In addition, the suppression threshold increases at a slope of approximately 4 dB in suppressee level per dB suppressor level. This high slope can be taken as an indicator of the effective compression of the suppressee in the control channel (such as the middle column in Fig. 2), assuming the suppressor is processed linearly. With even higher suppressor

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levels, the “effective” on-frequency excitation determining the pulsation threshold is dominated by the suppressor which explains the curve increase at the right side of the graph. Figure 3b shows the corresponding model output derived for the same stimuli and approximate levels as given for the data plot (20 dB suppressee level is missing). The plot shows the output of the on-frequency channel at 1000 Hz for suppressee levels of 30, 40, 50 and 60 dB, respectively, as a function of suppressor level and referenced to an on-frequency signal. To generate the model data, the suppressor level increased linearly from 20 dB to 100 dB within 2 s and the instantaneous output level inversely transformed across the on-frequency compressive I/O-characteristics is plotted. This inverse transformation is necessary, because the pulsation threshold is

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measured with a reference on-frequency test tone that is transformed compressively. Obviously, the general pattern is consistent with the data given in Fig. 3a while the fine structure of the total output level is generated by interference between suppressor and suppressee. The strong dips in the curves between 75 and 85 dB suppressor level, respectively, are due to interference in the signal path, i.e., where suppressor and suppressee level are approximately the same at the output. The modulation close to suppression threshold is due to interference between suppressor and suppressee in the control channel which leads to a modulation of instantaneous frequency and subsequently in overall gain. 3.2

Upward Spread of Masking

Figure 4a shows spectral masking data from Oxenham and Plack (1998) obtained with a narrowband noise as masker at different masker levels (given at the abscissa) and a short sinusoidal tone as signal to be detected either in a simultaneous masking condition (filled symbols) or a non-simultaneous masking condition (i.e., forward masking, open symbols). Squares denote on-frequency masking (masker and test tone centered at 4 kHz), where simultaneous and non-simultaneous masking data coincide quite well, whereas circles denote the off-frequency masking conditions (masker centered at 2.4 kHz, test tone at 4 kHz) where the simultaneous masking condition shows a much higher slope in masked threshold as function of masker level than the non-simultaneous condition. The difference between these curves represents the suppression of the 4-kHz-tone in the simultaneous condition (i.e., the level has to be increased considerably in order to achieve detection), whereas no suppression is exerted from the 2.4-kHz-masker in the non-simultaneous condition. Figure 4b shows the respective prediction from the model described above: To predict detection data, the output of the 4-kHz-channel was monitored and the detection threshold was assumed as soon the output level for masker plus test tone exceeds the output level for the masker alone by 1 dB. For predicting the threshold in quiet at the left-end side of the plot, an appropriate threshold criteria was assumed. For comparison, the estimation for the non simultaneous off-frequency condition is given (lower solid line), which was achieved by finding those test tone levels in the test tone-only condition that yield the same output level as the masker-alone condition in the 4-kHz-channel. In addition, the on-frequency condition for simultaneous and non simultaneous masking is plotted which was derived in the same way and which yields a 1:1 characteristic (upper solid curve). Obviously, the model predicts the average subjects data from Oxenham and Plack (1998) quite accurately even though the model parameters were not fit to this particular experimental condition. This underlines that the implementation of the suppression mechanism proposed here seems to be an adequate model of suppression effects in humans.

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4

Discussion

The model introduced here is just a prototype implementation of the newly developed idea of an instantaneous frequency detector controlling the gain and compression of specific filterbank channels or positions on the basilar membrane, respectively. In order to model exactly a variety of psychophysical and physiological data with the correct amount of suppression, frequency-specific gain and compression, a comparatively complicated model structure had to be assumed (cf. Fig. 2) where a feedback structure was avoided in order to maintain computational stability. It is not yet clear, however, if this structure or an alternative structure using basically the same elements is most appropriate for predicting also physiological experiments in the same way. Nevertheless, the basic idea of the instantaneous frequency extraction and its control of filterbank channels can be generalized and simplified for applications outside

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“pure” modeling in psychoacoustics and physiology. In speech processing, for example, an instantaneous-frequency-controlled filterbank shows a distinct enhancement of the most “relevant” frequency components of speech and may hence be used for efficient dynamic compression of speech and other wideband signals for hearing-impaired listeners. A key feature of the instantaneous frequency approach is the possibility to extract the instantaneous frequency within a few periods of the channels center frequency using the model assumption of an AM/FM signal. This may be useful for decomposing complex signals into “important” spectral components and “unimportant” ones and may thus help to derive better speech and audio coding strategies based on auditory models. Acknowledgments. Work supported by BMBF and DFG (Sonderforschungsbereich “Das aktive Gehör”).

References Duifhuis H (1976) Cochlear nonlinearity and second filter: possible mechanism and implications. J Acoust Soc Am 59:408–423 Duifhuis H (1980) Level effects in psychophysical two-tone suppression. J Acoust Soc Am 67:914–927 Duifhuis H, Hoogstraten HW, van Netten SM, Diependaal RJ, Bialek W (1985) Modelling the cochlear partition with coupled Van der Pol oscillators. In: Allen JB, Hall JL, Hubbard AE, Neely ST, Tubis A (eds) Peripheral auditory mechanisms. Springer, Berlin Heidelberg New York, pp 290–297 Goldstein JL (1988) Updating cochlear driven models of auditory perception: a new model for nonlinear auditory frequency analysing filters. In: Elsendoorn BAG, Bouma H (eds) Working models of human perception. Academic Press, London, pp 19–58 Herzke T, Hohmann V (2005) Effects of instantaneous multi-band dynamic compression on speech intelligibility. EURASIP JASP 2005(18):3034–3043 Meddis R, O’Mard LP, Lopez-Poveda EA (2001) A computational algorithm for computing nonlinear auditory frequency selectivity. J Acoust Soc Am 109:2852–2861 Oxenham AJ, Plack CJ (1998) Suppression and the upward spread of masking. J Acoust Soc Am 104:3500–3510 Talmadge C, Tubis A, Long GR, Piskorski P (1998) Modeling otoacoustic emission and hearing threshold fine structures in humans. J Acoust Soc Am 104:1517–1543 Wersäll J, Flock A, Lundquist P-G (1965) Structural basis for directional sensitivity in cochlear and vestibular sensory receptors. Cold Spring Harbor Symp Quant Biol 30:115–132

3 Estimates of Tuning of Auditory Filter Using Simultaneous and Forward Notched-noise Masking MASASHI UNOKI, RYOTA MIYAUCHI, AND CHIN-TUAN TAN

1

Introduction

The frequency selectivity of an auditory filter system is often conceptualized as a bank of bandpass auditory filters. Over the past 30 years, many simultaneous masking experiments using notched-noise maskers have been done to define the shape of the auditory filters (e.g., Glasberg and Moore 1990; Patterson and Nimmo-Smith 1980; Rosen and Baker, 1994). The studies of Glasberg and Moore (2000) and Baker and Rosen (2006) are notable inasmuch as they measured the human auditory filter shape over most of the range of frequencies and levels encountered in everyday hearing. The advantage of using notched-noise masking is that one can avoid off-frequency listening and investigate filter asymmetry. However, the derived filter shapes are also affected by the effects of suppression. The tunings of auditory filters derived from data collected in forward masking experiments were apparently sharper than those derived from simultaneous masking experiments, especially when the signal levels are low. The tuning of a filter is commonly believed to be affected by cochlear nonlinearity such as the effect of suppression. In past studies, the tunings of auditory filters derived from simultaneous masking data were wider than those of filters derived from nonsimultaneous (forward) masking data (Moore and Glasberg 1978; Glasberg and Moore 1982; Oxenham and Shera 2003). Heinz et al. (2002) showed that a tuning is generally sharpest when stimuli are at low levels and that suppression may affect tuning estimates more at high characteristic frequencies (CFs) than at low CFs. If the suggestion of Heinz et al. (2002) holds, i.e., if suppression affects frequency changes, comparing the filter bandwidths derived from simultaneous and forward masking experiments would indicate this. In this study we attempt to estimate filter tunings using both simultaneous and forward masking experiments with a notched-noise masker to investigate how the effects of suppression affect estimates of frequency selectivity across signal frequencies, signal levels, notch conditions (symmetric and asymmetric), and signal delays. This study extends the study of Unoki and Tan (2005). School of Information Sience, Japan Advanced Institute of Science and Technology, Japan, [email protected], [email protected], [email protected] Hearing – From Sensory Processing to Perception B. Kollmeier, G. Klump, V. Hohmann, U. Langemann, M. Mauermann, S. Uppenkamp, and J. Verhey (Eds.) © Springer-Verlag Berlin Heidelberg 2007

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2 Simultaneous and Forward Masking with Notched-Noise Masker 2.1

Methods

A diagram of the stimulus used in our masking experiments is shown in Fig. 1. The signal frequencies (ƒc) were 0.5, 1.0, 2.0, and 4.0 kHz. The notchednoise masker consisted of two bands of white noise where each bandwidth was fixed as 0.4 × ƒc. Under five conditions, the notch was symmetrically placed about ƒc. The values of ∆ƒc / ƒc under these conditions were 0.0, 0.1, 0.2, 0.3, and 0.4 (Fig. 1a). Under four asymmetric conditions, the combinations of the lower and upper ∆ƒc / ƒc s were (0.3, 0.1), (0.4, 0.2), (0.1, 0.3), and (0.2, 0.4), as shown in Fig. 1b,c. In the masking experiments we used three time conditions: the onsetinterval between the notched-noise masker and probe was 150, 300, and 305 ms, labeled A, B, and C in Fig. 1. Time condition A corresponded to the simultaneous masking, while B and C corresponded to the forward masking. At a fixed probe level, Ps (10, 20, and 30 dB SL), the masker levels, N0, at the masked thresholds were measured for a brief 10-ms signal (5-ms raisedcosine ramps, no steady state) in the presence of a 300-ms masker gated with 15-ms raised-cosine ramps. Fifteen normal-hearing listeners, aged 21–33, participated in the experiments. Six, seven, and six of them participated in the experiments with time conditions A, B, and C. Four participated under two conditions. The absolute thresholds of all subjects, measured through a standard audiometric tone test using a RION AA-72B audiometer, were 15 dB HL or less for both ears at octave frequencies between 0.125 and 8.0 kHz. All subjects were given at least 2 h of practice.

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Estimates of Tuning of Auditory Filter

21

All stimuli were re-generated digitally at a sampling frequency of 48 kHz and presented via a Tucker-Davis Technologies (TDT) system III real-time processor (TDT RP2). The masker and signal were separately attenuated by two programmable attenuators (TDT PA5) before they were mixed (using TDT SM5) and passed through a headphone buffer (TDT HB7) for presentation. The stimuli were presented monaurally to the subjects in a doublewalled sound-attenuating booth via Etymotic Research ER2 insert earphone. The level of the stimuli were verified using a B&K 4152 Artificial Ear Simulator with a 2-cm3 coupler (B&K DB 0138) and a B&K 2231 Modular Precision Sound Level Meter. Masked thresholds were measured using a three-alternative forced-choice (3AFC) three-down one-up procedure that tracks the 79.4% point on the psychometric function (Levitt 1970). Three intervals of stimuli were presented sequentially using a 500-ms inter-stimulus interval in each trial. Subjects were required to identify the intervals that carried the probe signals using numbered push-buttons on a response box. Feedback was provided by lighting up the LEDs corresponding to the correct interval on the response box after each trial. A run was terminated after twelve reversals. The step size was 5 dB for the first four reversals and 2 dB thereafter. The threshold was defined as the mean signal level at the last eight reversals. All data in which the masker level at the threshold was over 90 dB SPL were eliminated because they were affected by the compression effect. 2.2

Results and Discussion

∆

∆

∆

∆

The mean masked thresholds for signal frequencies of 1.0 and 2.0 kHz in the three time conditions are plotted in Fig. 2 as functions of the signal levels. Those for 0.5 and 4.0 kHz are omitted here, but the trends of all data were similar. The abscissas of the plots in this figure show the smaller of the two values of ∆ƒc / ƒc. The circles denote the mean masked thresholds under the symmetric notched-noise conditions (Fig. 1a). The triangles pointing to the right ( ) denote the mean masked thresholds in the asymmetric notched-noise conditions where ∆ƒc / ƒc for the upper noise band was 0.2 greater than that for the lower noise band (Fig. 1b). The triangles pointing to the left ( ) denote the mean masked thresholds under the asymmetric notched-noise conditions where ∆ƒc / ƒc for the lower noise band was 0.2 greater than that for the upper noise band (Fig. 1c). We found that the masked threshold increased as the notch width was increased. We also found that the s were consistently higher than the s. This suggests that the auditory filter shapes were asymmetric, with a steeper high frequency slope. The slopes of the growth of the masking functions (the variability of the masker level at the threshold in terms of signal levels from 10 to 30 dB SL) for 1.0 and 2.0 kHz under the three time conditions (A, B, and C) are shown

M. Unoki et al.

Masker level at threshold (dB SPL) Masker level at threshold (dB SPL)

22 (a) Sim. A, 1 kHz

(b) Fwd. B, 1 kHz

(c) Fwd. C, 1 kHz

80

80

80

60

60

60

40

40

40

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20

(d) Sim. A, 2 kHz

(e) Fwd. B, 2 kHz

(f) Fwd. C, 2 kHz

80

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20 0

0.1

0.2

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0.4

Relative notch width, D fc / fc

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0.2

0.3

0.4

0

0.1

0.2

0.3

0.4

Relative notch width, D fc / fc Relative notch width, D fc / fc

Slope of masking func. (dB/dB)

Fig. 2 Mean masked thresholds in masking experiments with three time conditions. A, B, and C, for a–c 1 kHz and d–f 4 kHz. Signal levels were 10, 20, and 30 dB SL

(a) 1 kHz

(b) 2 kHz

2

2

1.5

1.5

1

1

0.5

A

B

C

0.0

5.0 (ms)

0.5

A

B

C

0.0

5.0 (ms)

Fig. 3 Mean slope of growth of masking function under three conditions: simultaneous (A) and forward masking (B and C) for a 1.0 kHz and b 2.0 kHz

∆

∆

∆

∆

in Fig. 3. The thick, medium, and thin solid lines show the slopes under the symmetric notch conditions (º) of 0.0, 0.1, and 0.2. The dotted and dashed lines show the slope under the asymmetric conditions ( and ) of 0.1. The slope under the -notch condition is greater than that under the -notch condition. In addition, the slope in C is greater than those in A and B. These results suggest that filter non-linearity such as compression tended to occur

Estimates of Tuning of Auditory Filter

23

as the signal was delayed under the three time conditions (A, B, and C) and that the decayed lower notched-noise components still might affect the masking as suppressive masking in the condition C.

3

Estimation of the Filter Tuning

The most common method for estimating auditory filter shape is the roex filter model based on the power spectrum model of masking. The current form was proposed by Glasberg and Moore (2000). This model can be used to account precisely for simultaneous masking. It may be used to estimate the filter shape from forward masking as a pilot test. However, it does not suitably account for forward masking with a complex or noise masker because it cannot separately deal with excitatory and suppressive masking (Wojtczak and Viemeister 2004). We used the parallel roex filter (Unoki et al. 2006) to estimate the filter shape and tuning under the three time conditions as an alternative method. Because this model consists of a passive tail roex and an active tip roex with the schematic I/O function used by Glasberg and Moore (2000), this can deal with the above problem. The internal level, Prxp, is determined as the output of the passive tail filter (t), and then the active tip filter (p) varies with this level. The parallel roex filter is characterized by five parameters (tl, tu, pl, pu, and Gmax). Another two non-filter parameters (efficiency, K, and absolute threshold, P0) are used in the power spectrum model. These parameters are represented as a function of the normalized ERBN-rate, Eƒ = ERBN - rate(ƒ)/ERBN - rate(1 kHz) − 1, and were determined by using the refined fitting procedure used by Unoki et al. (2006) on masking data with the three time conditions. This fitting procedure also included the outer and the effect of transmission in precochlear processing, MidEar correction (Glasberg and Moore 2000) and the effect of off-frequency listening (Patterson and Nimmo-Smith 1980). In this fitting procedure, as the revised point, we incorporated a decay function (a leaky integrator) into the level estimator. Because the masker level (N0) that approached to the signal position should be decayed drastically in the forward masking (B and C in Fig. 1), whereas the masker level was constant at the signal position in the simultaneous masking (A in Fig. 1). Thus, the reduction of the masker levels under time conditions B and C were 16.3 dB and 42.0 dB by the decay function. These values were, then, used in the power spectrum model. The parameters were optimized by minimizing the root mean square (rms) error between the masked thresholds and the predicted thresholds. The optimized values for the five parameters of the parallel roex auditory filters and the rms errors, fitted to masking data corrected under the three time conditions, are shown in Table 1. The thresholds predicted using the parallel roex filter are plotted in Fig. 2 (solid lines for circles, dashed lines for

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Table 1 Filter coefficients of parameters and rms errors in fit Condition

tl

tu

Gmax

pl

pu

rms (dB)

A. Simulta-neous

10.8

81.6

29.1 −0.988Eƒ

33.8 +0.006Pprx

25.8

2.38

B. Forward (0 ms)

11.8

82.7

24.1 −7.11Eƒ

26.2 +0.149 Pprx

48.8 −0.140 Pprx

2.80

C. Forward (5 ms)

9.05

132

19.8 +0.121Eƒ

34.8 −0.050 Pprx

72.8 −0.227 Pprx

2.27

10

Filter gain (dB)

0

10 dB SL

−10 −20 −30 −40 −50 10

Filter gain (dB)

0

30 dB SL

−10 −20 −30 −40 −50

Simult. masking (A) Forward masking (B) Forward masking (C) 500

1k

2k

4k

Frequency (Hz)

Fig. 4 Auditory filter shapes with center frequencies between 0.5 and 4.0 kHz, derived from mean threshold data in three masking experiments (A, B, and C)

∆

∆

s, and dotted lines for s). The shapes of the derived filters centered at the signal frequencies of 0.5, 1.0, 2.0, and 4.0 kHz are plotted in Fig. 4 as a function of the signal level (10 and 30 dB SL). All the parallel roex filters can be excellently fitted to the simultaneous and forward masking data. Under the three time conditions, we found that the skirts of filters on the higher side for B and C are somewhat steeper than those of A. However, the tail slopes on the

Estimates of Tuning of Auditory Filter

25

Table 2 Means of the filter bandwidths of parallel roex filter at lower levels Condition \ ƒc (Hz)

500

1000

2000

4000

ERBN (Glasberg and Moore 1990)

79

133

241

456

A. Simultaneous masking

81

136

248

471

B. Forward masking (no silence)

66

112

204

392

C. Forward masking (5 ms delay)

61

97

175

330

lower side of B and C are somewhat shallower than those of A. Remaining lower notched-noise components may affect this. The mean equivalent rectangular bandwidth (ERB)s of the derived auditory filter shape as shown in Fig. 4 under the three conditions are shown in Table 2. The results show that the tuning of the derived filter using forward masking (B and C) is somewhat sharper than that using simultaneous masking (A). The ratios of ERBN (Glasberg and Moore 1990) to the ERBs in C for 0.5–−4.0 kHz are 1.30, 1.37, 138, and 1.38. The tuning of the derived filter from forward masking have became sharper as the signal frequency was increased and/or the signal was delayed (A, B, and C). In addition, when the signal level was increased in dB SL, the ERBs estimated from the forward masking data are still sharper.

4

Conclusions

We estimated filter tuning using both simultaneous and forward masking with a notched-noise masker as functions of signal frequencies (0.5, 1.0, 2.0, and 4.0 kHz), signal levels (10, 20, and 30 dB SL), notched-noise conditions (five symmetric and four asymmetric), and the time conditions (A, B, and C in Fig. 1). Auditory filter shapes were derived under these conditions using the parallel roex filter. The results suggest that the tunings of the auditory filters derived from the forward masking data were considerably sharper than those derived from the simultaneous masking data. The tunings of the auditory filters became much sharper as the center frequency was increased (ratios of 1.30 to 1.38). However, the difference between the tunings of the auditory filters at lower center frequencies in using both maskings tended to be smaller than that at higher center frequencies. It may be affected by remaining effects of suppression due to the decayed lower notched-noise components below the signal frequency. Acknowledgments. This work was supported by special coordination funds for promoting science and technology (supporting young researchers with fixed-term appointments).

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References Baker RJ, Rosen S (2006) Auditory filter nonlinearity across frequency using simultaneous notched-noise masking. J Acoust Soc Am 119:454–462 Glasberg BR, Moore BCJ (1982) Auditory filter shapes in forward masking as a function of level. J Acoust Soc Am 71:946–949 Glasberg BR, Moore BCJ (1990) Derivation of auditory filter shapes from notched-noise data. Hear Res 47:103–138 Glasberg BR, Moore BCJ (2000) Frequency selectivity as a function of level and frequency measured with uniformly exciting noise. J Acoust Soc Am 108:2318–2328 Heinz MG, Colburn HS, Carney LH (2002) Quantifying the implications of nonlinear cochlear tuning for auditory filter estimates. J Acoust Soc Am 111:996–1101 Levitt H (1970) Transformed up-down methods in psychoacoustics. J Acoust Soc Am 49:467–477 Moore BCJ, Glasberg BR (1978) Psychophysical tuning curves measured in simultaneous and forward masking. J Acoust Soc Am 63:524–532 Oxenham AJ, Shera CA (2003) Estimates of human cochlear tuning at low levels using forward and simultaneous masking. J Assoc Res Otolaryngol 4:541–554 Patterson RD, Nimmo-Smith I (1980) Off-frequency listening and auditory filter asymmetry. J Acoust Soc Am 67:229–245 Rosen S, Baker RJ (1994) Characterising auditory filter nonlinearity. Hear Res 73:231–243 Unoki M, Tan C-T (2005) Estimates of auditory filter shape using simultaneous and forward notched-noise masking. Forum Acust Budapest 1497–1502 Unoki M, Irino T, Glasberg BR, Moore BJC, Patterson RD (2006) Comparison of the roex and gammachirp filters as representations of the auditory filter. J Acoust Soc Am 120:1474–1492 Wojtczak M, Viemeister NF (2004) Mechanisms of forward masking. J Acoust Soc Am 115:2599(A)

4 A Model of Ventral Cochlear Nucleus Units Based on First Order Intervals STEFAN BLEECK1 AND IAN WINTER2

1

Introduction

In the presence of a constant stimulus the arrival time of a spike has little meaning except in the context of the time of the preceding spike. Electrophysiological studies reveal a surprising degree of variability in neurons firing behaviour which can be described by the probability distribution function of consecutive spikes, the inter-spike intervals (ISI). A histogram (ISIH) can be constructed to represent the ISI-distribution (ISID) of intervals and a probability density function (PDF) of the intervals can be modelled by fitting it to the histogram. The continuous nature of the PDF is attractive since it avoids an arbitrary resolution of spike quantisation. There is no generally accepted PDF to model ISIHs. Interval distributions that were fitted, with more or less success, to ISIHs of constant stimuli include normal, log normal, exponential, gamma, Weibull, bimodal, multimodal. With the exception of the normal distribution, these functions are asymmetric (due to their positive skewness) reflecting the tendency of slower firing neurons to show greater variability. These functions are all empirical fits and cannot be fully justified physiologically – exponential PDFs (which are a result of a Poisson process) can only fit parts of the curve and gamma functions (which are a result of a coincidence detector) ignore the decay times of EPSPs – and consequently there are many other possible candidates for empirical fitting functions. Neuronal processing and all models based on physiological and biophysical bases include many noise sources that jitter the occurrences of spikes. These noise sources include the number and size of input synapses; the precise arrival time of input spikes and the location of synapses on dendrites and the thickness of dendrites which lead to differences in amplitude, width and time of arrival of EPSPs and IPSPs at the axon hillock. It is very difficult, if not impossible, to measure the influence of all these noise sources experimentally; therefore we have to rely on assumptions and simulations to investigate them in detail.

1

Institute of Sound and Vibration Research, University of Southampton, UK, [email protected] Department of Physiology, Development and Neuroscience, University of Cambridge, UK, [email protected] 2

Hearing – From Sensory Processing to Perception B. Kollmeier, G. Klump, V. Hohmann, U. Langemann, M. Mauermann, S. Uppenkamp, and J. Verhey (Eds.) © Springer-Verlag Berlin Heidelberg 2007

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The idea that is developed in this chapter provides a possible solution to this problem. We demonstrate here that the huge number of noise sources can actually reduce the complexity of the system behaviour. The “central limit theorem” states that any sum of many independent identically distributed random variables will be distributed normally (independent of the original distribution). The according theorem for probability distributions for our argument was formulated by Fischer and Tippett (1928) as the “central limit theorem of extreme value distributions”: the extreme values of many independent identically distributed random variables will take the form of an “extreme value distribution” (EVD) (independent of the original distribution). The EVD is a valid model under the following conditions: The membrane potential is affected in a complicated manner by many random processes. The event when the membrane potential exceeds the threshold can be seen as “extreme”, when the threshold is high compared to the noisy resting potential. This can be shown by intracellular recordings (Calvin and Stevens 1967). Furthermore, the resting potential and the underlying probability density function for spike generating must be stationary. To apply the EV theorem we do not have to know or assume any (physiologically motivated) models of the underlying spike generating process. The resulting spike generating process then takes on the form of a Markov process in which the probability of the next spike explicitly depends on its immediate predecessor but is conditionally independent of all other preceding spikes (Tuckwell 1988). The shape of the PDF of such a process is determined by the EVD.

2

Methods

All physiological recordings were obtained from the cochlear nucleus of the anaesthetized guinea pig (Cavia porcellus). The procedures are described in detail in Bleeck et al. (2006). The normalised cumulative first order interval distributions of half the number of all intervals between stimulus onset and offset was fitted by a maximum likelihood method with the generalised extreme value distribution: Z V]] R log (x) - a W S F (x | a, b, ζ ) = exp [- S1 + ζ f pW b W ]] S X \ T

1

_ b ` b b a

ζb

where τ is the time since the last spike (ISI), a the its location, b the scale and ζ the shape of the distribution. The shape parameter ζ governs the tail behaviour of the distribution, and the sub-families defined by ζ>0, ζ→0 and ζ 10 kHz) are characteristic. The model in Fig. 1B provides an explanation for DCN response maps (Blum and Reed 1998). It consists of excitatory

Spectral Edges as Optimal Stimuli for the Dorsal Cochlear Nucleus

45

input from auditory nerve fibers (ANF, dark gray) with strong inhibitory inputs from so-called type II neurons (light gray) and weaker inhibition from a second source (WBI; Nelken and Young 1994). The BF of the type II inhibitory input is shifted to a frequency below the neuron’s (and the ANF’s) BF and the inhibitory input has a higher threshold (Voigt and Young 1990), resulting in the major excitatory and inhibitory features in the response map. Figure 1A shows a typical cat head-related transfer function with a spectral notch positioned at the BF of the model (dashed line). This is the spectrum at the eardrum for a broadband noise presented in free field from 15˚ azimuth, −15º elevation. Because of the offset in their BFs, this notch would activate the excitatory input without activating the inhibitory input, leading to a strong response from the model. These features of the response map suggest that the upper edge of a notch might be a strong stimulus for DCN type IV neurons. Responses to spectral notches of a type IV neuron are shown in Fig. 2 in the form of discharge rate (ordinate) as the notch is moved in frequency (abscissa; Reiss and Young 2005). The abscissa is the frequency of the rising edge of the stimulus in each case. Figure 2A shows that notches of various

1

A

0.125 oct. 0.25 oct. 0.5 oct. 1 oct.

100 Rate, sp./s.

0.5

80

B 13 dB spect. level

80

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0

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30 20 Upper edge freq., kHz

3 dB 13 dB 23 dB 33 dB

100

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0.5 octave notchwidth

20 30 Upper edge freq., kHz

40

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Rate, sp./s.

100 80 60

0.5 octave bandwidth 3 dB 13 dB 23 dB 33 dB

40 20 0

10

20 Lower edge freq., kHz

30

Fig. 2 Rate responses of a DCN neuron to notches (A,B) or noise bands (C) moved in frequency. Abscissae show the frequency of the rising edge of the notch or band. Passbands were 30 dB above stopbands. Sound levels are passband spectrum level, dB re 20 µPa/Hz1/2. The spectra giving maximum rate are shown above the plots. Horizontal dashed line is average spontaneous rate, vertical dashed line is BF

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widths (see the legend) produce a strong excitatory response when the upper-frequency edge of the notch is near BF (vertical dashed line) and inhibition when the notch is centered on BF (when the upper edge frequency is just above BF). This pattern of response remains across a range of sound levels (Fig. 2B, for 1/2 octave notches). It is also observed when the stimulus is a noise band, but in this case is associated with the lower-frequency edge of the band, on the abscissa in Fig. 2C.

3

Finding the Optimal Stimulus

A useful approach to understanding the sensory representation by nonlinear neurons is to search for optimal stimuli (e.g. deCharms et al. 1998; O’Connor et al. 2005). The characteristics of a neuron’s optimal stimulus provide a functional definition of the signal processing being done by the neuron. The optimum can be the stimulus giving the highest discharge rate or it can be the stimulus about which the neuron provides the most information in some sense. In this chapter the optimum is the maximum discharge rate. The problem of finding the optimum is not well defined and must usually be limited to some class of stimuli. Here, the stimulus class is random spectral shape stimuli (RSS; Yu and Young 2000; Young and Calhoun 2005) and the optimum spectral shape is sought. RSS stimuli consist of sums of random-phase tones spaced at 1/64 octave intervals over a several-octave frequency range; the tones are gathered into sets of 8 in 1/8 octave bins. The total power in each bin, in dB relative to a reference stimulus, varies pseudo-randomly with an approximately Gaussian distribution and a standard deviation of 1.5–12 dB. These stimuli have minimal envelope fluctuations and the effects of the temporal envelope are not considered. Figure 3A shows examples of the spectra of RSS stimuli. The optimization proceeds by changing the spectral shape iteratively, guided by the Fisher information matrix F of the responses (Cover and Thomas 1991). The i–jth term in the Fisher matrix is Fij = E > 22q ln p (r ; q) 22q ln p (r ; q)H . i

j

(1)

where p(r; q) is the pdf of discharge rate r given the stimulus parameters q, the amplitudes (dB) of the stimulus in the 11 RSS bins centered on BF. Fij is the sensitivity of the neuron’s rate response to simultaneous changes in the stimulus amplitude in the ith and jth bins, in the sense that the inverse of the Fisher matrix is the covariance matrix of the minimum-variance unbiased estimator of q based on r (the Cramér-Rao bound). The Fisher matrix can be computed from rate data using the following approximation (Johnson et al. 2001): D ( p (r; q + dq) || p (r; q)) .

1 dqT F dq 2 ln 2

(2)

Spectral Edges as Optimal Stimuli for the Dorsal Cochlear Nucleus

B

Level, dB atten.

A

Largest eigenvector

C

47

*

40

*

* *

50 −0.5

0

0.5

−0.5

0

0.5

1

D

−0.5

0

30

0.5

*

0.5 40

0 −0.5

50

−1

−0.5

0

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−0.5

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* −0.5

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Octaves re BF Rate (sp/s)

E 200

100

0

−8

−4

0

4

8

−8

−4

0

4

8

Eigenvector multiplier, dB

Fig. 3A–E Finding the optimal stimulus shape. The abscissae in A–D are frequency, in octaves re BF. The ordinate scale in D is level in dB attenuation as in A,B

where D( ) is the so-called KL distance between the pdfs of the rate response to stimulus vectors q + δ q and q and the approximation is good for small δ q. The change δq in the stimulus that gives the largest change in the KL distance in Eq. (2) is parallel to the eigenvector with the largest eigenvalue emax, i.e. δqmax = Aemax, where A is a constant. It can be shown from a model of RSS responses that, for small δ q, this also gives the largest change in discharge rate. Thus the rate optimization proceeds by estimating F in the vicinity of a reference stimulus q, then finding the δ q that produces the largest rate change by empirically finding the value of A (limited to ±8 dB) such that δ qmax = A emax gives the largest rate change. The reference stimulus is then changed to q + δ qmax and the process is repeated. The process terminates when the reference q is a rate maximum, as judged from a local quadratic model of the dependence of rate on δ q. This process is done on-line and typically requires ~1 h and three iterations. The Fisher matrix is estimated from rate responses r to a large number of different perturbations δq around the reference stimulus, giving many simultaneous linear algebraic equations like Eq. (2) with the terms of F as the unknowns. The KL distance is computed from the mean rates, assuming that r is Poisson.

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Figure 3 shows the steps in computing an optimum shape. The left column shows the first iteration step. The initial reference q was an RSS stimulus with 0 dB in all bins, shown by the horizontal line in Fig. 3A. The dashed line shows an example of an RSS perturbation δ q. The eigenvector emax is shown in Fig. 3C and the discharge rates for stimuli with spectral shape q + Aemax are shown in Fig. 3E, plotted as a function of A. The open circle shows the rate in response to the 0-dB reference stimulus and the filled circle shows the maximum rate, over the 16-dB range tested. The error bars are the SD of 10 repetitions of each stimulus. The second column shows the second iteration. The reference in this case (solid line in Fig. 3A) has the shape of emax from the first iteration. emax and the rates for δ q= Aemax are shown in Fig. 3C,E as before. In this case the maximum rate occurred at the spectral shape shown in Fig. 3B. This stimulus is a rate maximum for all directions δ q and so terminated the iterations. Close inspection shows that the maximum rate after the second iteration was slightly smaller than the maximum rate after the first iteration. This occurred because of a systematic rate change in the neuron, sometimes seen in DCN principal cells. Essentially the rate decreased by 18% during the first iteration, as shown by rates in response to a control stimulus (not shown). Thus the rate maximum after the second iteration was indeed an overall rate maximum at this time. The optimization process only constrains the amplitudes at frequencies to which the neuron is sensitive. The bins marked by asterisks in Fig. 3B account for 80% of the rate change across a set of RSS stimuli. Those are also the bins that changed significantly during the iteration; note that the remaining, nonasterisk, bins stayed near their initial values. Thus the optimal stimulus should be considered to consist of the four asterisked bins.

4

The Optimal Stimulus for DCN Neurons

The optimal stimulus for the example neuron in Fig. 3B is a rising spectral slope centered on BF. Figure 3D shows the outcome of the optimization process for a second type IV neuron, whose optimal stimulus is a sharp spectral edge at BF. The results of the optimization process thus correspond to the organization of excitatory and inhibitory inputs in Fig. 1 and the rate peaks observed in Fig. 2. It is important to emphasize that not all DCN principal cells show the notch edge sensitivity of the examples shown here, presumably because of different arrangements of the inhibitory inputs (Reiss and Young 2005). The method of Sect. 3 provides a general way to find optimal spectral shapes that is applicable to neurons in all parts of the auditory system. It is fast and can be made faster by initiating the search with a reference stimulus that produces the highest discharge rate across an RSS set. Its major limitation is that it does not incorporate temporal aspects of the stimulus. Acknowledgements. This work was supported by NIH grants DC00115 and DC05211.

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49

References Blum JJ, Reed MC (1998) Effects of wide band inhibitors in the dorsal cochlear nucleus. II. Model calculations of the responses to complex tones. J Acoust Soc Am 103:2000–2009 Cover TM, Thomas JA (1991) Elements of information theory. Wiley-Interscience, New York Davis KA, Miller RL, Young ED (1996) Effects of somatosensory and parallel-fiber stimulation on neurons in dorsal cochlear nucleus. J Neurophysiol 76:3012–3024 deCharms RC, Blake DT, Merzenich MM (1998) Optimizing sound features for cortical neurons. Science 280:1439–1443 Eggermont JJ, Aertsen AMHJ, Johannesma PIM (1983) Prediction of the responses of auditory neurons in the midbrain of the grass frog based on the spectro-temporal receptive field. Hearing Res 10:191–202 Johnson DH, Gruner CM, Baggerly K, Seshagiri C (2001) Information-theoretic analysis of the neural code. J Comput Neurosci 10:47–69 Kanold PO, Young ED (2001) Proprioceptive information from the pinna provides somatosensory input to cat dorsal cochlear nucleus. J Neurosci 21:7848–7858 Machens CK, Wehr MS, Zador AM (2004) Linearity of cortical receptive fields measured with natural sounds. J Neurosci 24:1089–1100 May BJ (2000) Role of the dorsal cochlear nucleus in the sound localization behavior of cats. Hearing Res 148:74–87 Middlebrooks JC (1992) Narrow-band sound localization related to external ear acoustics. J Acoust Soc Am 92:2607–2624 Nelken I, Young ED (1994) Two separate inhibitory mechanisms shape the responses of dorsal cochlear nucleus type IV units to narrowband and wideband stimuli. J Neurophysiol 71:2446–2462 Nelken I, Kim PJ, Young ED (1997) Linear and non-linear spectral integration in type IV neurons of the dorsal cochlear nucleus: II. Predicting responses using non-linear methods. J Neurophysiol 78:800–811 O’Connor KN, Petkov CI, Sutter ML (2005) Adaptive stimulus optimization for auditory cortical neurons. J Neurophysiol 94:4051–4067 Reiss LAJ, Young ED (2005) Spectral edge sensitivity in neural circuits of the dorsal cochlear nucleus. J Neurosci 25:3680–3691 Rouiller EM (1997) Functional organization of the auditory pathways. In: Ehret G, Romand R (eds) The central auditory system. Oxford University Press, New York, pp 3–96 Shore SE (2005) Multisensory integration in the dorsal cochlear nucleus: unit responses to acoustic and trigeminal ganglion stimulation. Eur J Neurosci 21:3334–3348 Voigt HF, Young ED (1990) Cross-correlation analysis of inhibitory interactions in dorsal cochlear nucleus. J Neurophysiol 64:1590–1610 Young ED, Calhoun BM (2005) Nonlinear modeling of auditory-nerve rate responses to wideband stimuli. J Neurophysiol 94:4441–4454 Young ED, Davis KA (2001) Circuitry and function of the dorsal cochlear nucleus. In: Oertel D, Popper AN, Fay RR (eds) Integrative functions in the mammalian auditory pathway. Springer, Berlin Heidelberg New York, pp 160–206 Yu JJ, Young ED (2000) Linear and nonlinear pathways of spectral information transmission in the cochlear nucleus. PNAS 97:11780–11786

Comment by Langner According to your Fig. 1 the spectral notches in cat head-related transfer functions show up around 10 kHz, which would suggest a functional role for units with an inhibitory area close to or at their CF around 10 kHz. However,

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the tuning curves of type IV neurons are similar not only around 10 kHz but for all center frequencies. Therefore my question is: What is your opinion about the functional role of type IV neurons outside the 10-kHz range? Reply We have noted previously that type IV neurons with notch sensitivity do not seem to be limited to BFs where the cat’s ear shows spectral notches (Young and Davis 2001, Fig. 5.13). The present chapter, along with the results of Lina Reiss (Reiss and Young 2005), provide an alternative view of DCN notch sensitivity as sensitivity to rising frequency edges. During the meeting, an interesting suggestion was made by B. Delgutte: because the acoustic environment is usually low-pass in its spectral content, DCN neurons may be tuned to unusual acoustic features which are high-pass, by contrast to the usual spectra. This corresponds well to our previous suggestions that the DCN may serve to detect potentially important acoustic events and report them to the auditory system (Nelken and Young 1996). References Nelken I, Young ED (1996) Why do cats need a dorsal cochlear nucleus? Rev Clin Basic Pharmacol 7:199–220 Reiss LAJ, Young ED (2005) Spectral edge sensitivity in neural circuits of the dorsal cochlear nucleus. J Neurosci 25:3680–3691 Young ED, Davis KA (2001) Circuitry and function of the dorsal cochlear nucleus. In: Oertel D, Popper AN, Fay RR (eds) Integrative functions in the mammalian auditory pathway. Springer, Berlin Heidelberg New York, pp 160–206

7 Psychophysical and Physiological Assessment of the Representation of High-frequency Spectral Notches in the Auditory Nerve ENRIQUE A. LOPEZ-POVEDA1, ANA ALVES-PINTO1, AND ALAN R. PALMER2

1

Introduction

Destructive interference between sound waves within the pinna produces notches in the stimulus spectrum at the eardrum. Some of these notches have a center frequency that depends strongly on the relative vertical angle between the sound source and the listener (e.g. Lopez-Poveda and Meddis 1996). Therefore, it is not surprising that they constitute useful cues for judging sound source elevation (reviewed by Carlile et al. 2005). The auditory nerve (AN) is the only transmission path of acoustic information to the brain. Single fibers encode the physical characteristics of the sound in at least two ways: in their discharge rate and in the time at which their spikes occur (reviewed by Lopez-Poveda 2005). Because spectral notches due to the pinna occur at frequencies beyond the cut-off of phase locking, the common view is that the AN representation of these notches must be based on the discharge rate alone, i.e. temporal representations do not contribute (Rice et al. 1995). In other words, the brain would infer the stimulus spectrum from a representation of the discharge rate of the population of AN fibers as a function of their characteristic frequencies (CFs). This representation is known as the rate profile. On the other hand, evidence exists that the apparent quality of the rateprofile representation of high-frequency spectral notches degrades as the sound pressure level (SPL) of the stimulus increases (Rice et al. 1995; LopezPoveda 1996). Almost certainly this is due to the low threshold and the narrow dynamic range of AN fibers with high-spontaneous rate (HSR), which are the majority, and to the progressive broadening of their frequency tuning with increasing SPL. Although low-spontaneous rate (LSR) fibers have higher thresholds and wider dynamic ranges, they are a minority. Furthermore, the broadening of basilar membrane tuning at high levels makes it unlikely that they can convey high-frequency spectral notches in their rate profile equally well at low and high levels (Carlile and Pralong 1994; Lopez-Poveda 1996). 1 Instituto de Neurociencias de Castilla y León, Universidad de Salamanca, Avda. Alfonso X El Sabio s/n, 37007 Salamanca, Spain, [email protected], [email protected] 2 MRC Institute of Hearing Research, University Park, Nottingham, NG7 2RD, UK, [email protected]

Hearing – From Sensory Processing to Perception B. Kollmeier, G. Klump, V. Hohmann, U. Langemann, M. Mauermann, S. Uppenkamp, and J. Verhey (Eds.) © Springer-Verlag Berlin Heidelberg 2007

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Consistent with this, one would expect that to discriminate between a flatspectrum noise and a similar noise with a spectral notch centered at a high frequency (say 8 kHz) would be increasingly more difficult as the overall stimulus level increases. However, contrary to this expectation we have shown (Alves-Pinto and Lopez-Poveda 2005) that the ability to discriminate between flat-spectrum and notched noise stimuli is a nonmonotonic function of level for the majority of listeners. Specifically, discrimination is more difficult at levels around 70–80 dB SPL than at lower or higher levels. Here we report on our efforts at understanding the nature of this paradoxical result. Our approach consists in predicting the limits of psychophysical performance in the spectral discrimination task of Alves-Pinto and Lopez-Poveda (2005) based on the statistical analysis of experimental AN responses. The results contradict the common view that high-frequency spectral notches are conveyed to the central auditory system in the AN rate profile. Instead, they suggest that spike rates over narrow time windows almost certainly convey useful information for discriminating between noise bursts with and without high-frequency spectral notches.

2

Methods

The activity of guinea pig AN fibers was recorded in response to the same bursts of broadband (20–16,000 Hz) frozen noise that we used in our previous psychophysical study. Two types of noise were considered. One had a completely flat spectrum while the spectrum of the other had a rectangular notch between 6000 and 8000 Hz with a depth of 3 dB re. noise spectrum level. Responses were measured for overall noise levels between 40 and 100 dB SPL in steps of 10 dB. The noise bursts had a duration of 110 ms, including a 10-ms rise ramp (no fall ramp was applied), and were presented every 880 ms. Details on the noise generation procedure are given elsewhere (Alves-Pinto and Lopez-Poveda 2005). Responses were recorded for a sample of 106 AN fibers (from 16 animals) with CFs spanning a range from 1000 to 19,000 Hz. Of the fibers, 31 had spontaneous rates less than 18 spikes/s. The method of recording of physiological responses was virtually identical to that described in Palmer et al. (1985). The response of any given fiber was measured at least five times for each stimulus condition. 2.1

Statistical Analysis of Auditory Nerve Responses

The psychophysical just-noticeable difference (JND) in a given stimulus parameter, ∆αJND, can be predicted from the instantaneous discharge rate of the population of AN fibers as follows (Siebert 1970; Heinz et al. 2001): Da JND = */ # i

0

T

2

- 0.5

2ri (t, a) 1 dt ri (t, a) < 2a F 4

,

(1)

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where t denotes time, and ri(t, α) the instantaneous discharge rate of the i-th fiber in response to the stimulus with parameter α. In our context, α corresponds to the notch depth. Hence, Eq. (1) allows predicting the threshold notch depth for discriminating between a flat-spectrum noise and a noise with a spectral notch based on the experimental AN responses. Equation (1) was derived on the assumption that the times at which AN spikes occur follow a Poisson distribution (i.e., that spikes occur at times that are independent of each other). Furthermore, it was derived on the assumption that psychophysical discrimination thresholds reflect optimal use of every bit of information available in the activity of the population of fibers. Neither of these two conditions apply here (see Heinz et al. 2001); thus we do not expect the resulting ∆αJND values to match the psychophysical thresholds directly. However, it is reasonable to assume that the error in using Eq. (1) for predicting the psychophysical thresholds will be similar for all SPLs. Therefore, Eq. (1) remains useful for predicting the shape of the threshold notch depth vs level function, as reported previously by us (Alves-Pinto and Lopez-Poveda 2005). It is noteworthy that Eq. (1) predicts the threshold notch depth for spectral discrimination using the instantaneous discharge rate of the population of AN fibers. This contrasts with the rate-place model described in the Introduction that only considers the information conveyed in the overall discharge rate of the fibers. For obvious reasons, in applying Eq. (1) we had to consider a discrete version of the instantaneous discharge rate, ri(∆t, α) rather than the continuoustime ri(t, α). Note that ri(∆t, α) may be interpreted as a mean-rate post-stimulus time histogram for a bin width duration of ∆t. Instead of deciding on an arbitrary value for ∆t, we computed ∆αJND for different bin widths (or sampling periods), ∆t, from 0.333 to 110 ms. Note that in the extreme case that ∆t equals the stimulus duration, the resulting ∆αJND corresponds to performance based on a rate-place code only. In Eq. (1), the term between square brackets denotes the change in instantaneous discharge rate for an incremental change in parameter α. It was calculated as the instantaneous difference in discharge rate for the flat-spectrum, (α = 0 dB) and the notched (α = 3 dB) noises. ∆αJND becomes unrealistically equal to zero when the discharge rate of any fiber is equal to zero for any bin. To prevent this artifactual result, a small, arbitrary constant of 0.1 spikes/s was added to the measured discharge rate in all bins of all fibers. The actual value of this constant did not alter the results significantly.

3

Results

The results are illustrated in Fig. 1. The series denoted by the open circles (left ordinate) illustrates the ∆αJND values based on the experimental AN responses. These will be hereinafter referred to as the physiological JNDs.

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100 110 ms

22 0.1

10 S1

9

8

7 4 0.01

Psychophysical JND (dB)

Physiological JND (dB)

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1 2

0.005 40

50

60

70

80

90

0.5 100

Level (dB SPL) Fig. 1 Predicted threshold notch depth values from auditory nerve responses (circles, left ordinate) for different bin widths (as denoted by the numbers next to each trace). Also shown for comparison is an example psychophysical function (squares, right ordinate) taken from AlvesPinto and Lopez-Poveda (2005)

Each series illustrates the results for a different bin width, ∆t, as indicated by the numbers next to each trace. The series denoted by filled squares (right ordinate) illustrates a particular example of a psychophysical threshold notch depth vs level function taken from Alves-Pinto and Lopez-Poveda (2005; Fig. 3, listener S1). Notice that the scales of both ordinate axes are logarithmic (after Alves-Pinto and Lopez-Poveda 2005) and span a comparable range of values in relative terms, but not in absolute terms. In general, for any given SPL, the physiological-JND values increase as the sampling period ∆t increases, suggesting that discrimination benefits from the information conveyed by the timing of spike occurrences. The most striking result is that the shape of the physiological-JND vs level functions varies largely depending on the time window ∆t. Only for ∆t values within the range from 4 to 9 ms are the physiological-JND functions nonmonotonic with a peak at or around 80 dB SPL, thus resembling the shape of the psychophysical threshold notch depth vs level function (squares). In absolute terms, however, the physiological-JND values are about two orders of magnitude lower than the psychophysical ones (for the listener considered in Fig. 1). This may reflect differences in cochlear

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13 12 11 10 9 8 7 6 5 4 3

1

**

**

0.8 0.6 0.4 0.2 0

S1

S2

S3

S4

Correlation for best binwidth

Best binwidth (ms)

processing between human and guinea pig, and/or that humans do not behave as “optimal” spectral discriminators; otherwise the absolute values would match. The shape of the psychophysical threshold notch depth vs level function varies among listeners (Alves-Pinto and Lopez-Poveda 2005). Similarly, the shape of the physiological-JND vs level function depends on the value of the bin width ∆t (Fig. 1). Kendall’s τ correlation coefficient (Press et al. 1992) was used to quantify the degree of correlation between the shapes of the psychophysical function for each one of five listeners (S1 to S5) considered by Alves-Pinto and Lopez-Poveda (2005) with the physiological-JND vs level functions for different values of ∆t. Figure 2 shows the ∆t values (circles; left ordinate) that yielded the highest correlations for each listener, as well as the corresponding correlation values (squares; right ordinate). The degree of correlation varies considerably across listeners, but the ∆t that yields the highest correlations is similar across listeners (mean ± s.d. =8.66 ± 0.36). In Fig. 1, the series for ∆t equal to the stimulus duration (110 ms, top trace), shows the predicted physiological-JND values taking only the overall average discharge rate. The shape of this function clearly differs from that of the psychophysical function and matches overall the prediction of the rate-only theory. That is, threshold notch depths are lowest for overall levels around 60 dB SPL (corresponding to a spectrum level of 18 dB SPL) and increase progressively with increasing SPL. The level for which the physiological-JND is

S5

Listener Fig. 2 Binwidths (circles, left ordinate) for which maximum correlation occurred between the shapes of the physiological and the psychophysical threshold notch depth vs level functions for the five listeners considered by Alves-Pinto and Lopez-Poveda (2005). Squares (right ordinate) illustrate the actual degree of correlation. Two asterisks denote highly significant ( p< 0.01) correlations

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Physiological JND (dB)

0.4 0.3 0.2 0.1

HSR LSR HSR+LSR 0.01 40

50

60

70

80

90

100

Level (dB SPL) Fig. 3 Physiological JND (in dB) vs SPL based on the information conveyed by fiber groups of different types

lowest corresponds to an effective level of approximately 28 dB SPL (assuming a fiber with a CF = 8000 Hz and an effective bandwidth of 1000 Hz), which approximately falls at the center of the dynamic range of HSR fibers. Figure 3 compares the physiological-JND vs level function (for ∆ t = 8.33 ms) for three cases: using the information conveyed by all 106 fibers (circles) – this is the case considered so far; using only the information conveyed by the 75 fibers with SRs≥18 spikes/s (HSR, triangles); and using only the information from the 31 fibers with SRs