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J Neurophysiol 111: 1973–1985, 2014.
First published February 19, 2014; doi:10.1152/jn.00681.2013.
Emphasis of spatial cues in the temporal fine structure during the rising
segments of amplitude-modulated sounds II: single-neuron recordings
Mathias Dietz,1 Torsten Marquardt,1 Annette Stange,2 Michael Pecka,2 Benedikt Grothe,2
and David McAlpine1
1
University College London Ear Institute, London, United Kingdom; and 2Division of Neurobiology, Department Biology II,
Ludwig-Maximilians University, Munich, Germany
Submitted 20 September 2013; accepted in final form 13 February 2014
binaural processing; extracellular recordings; medial superior olive;
inferior colliculus
conditions, listeners perceive a
single sound image originating from its source, despite the
presence of multiple reflected copies of the sound arriving from
different locations, milliseconds later. Although reflected
sound energy may alter the width of the spatial percept, for
speech signals, only the direct sound direction has an impact on
the perceived location (“Haas effect”) (Haas 1951). This capacity to hear out the location (and other information) in
speech sounds, particularly under noisy or reverberant conditions, is key to “cocktail party listening”—the ability to
parse a conversation, even in the presence of competing
sources with greater energy than that of the source to which
a listener is attending. Nevertheless, speech—particularly re-
UNDER MANY NATURAL LISTENING
Address for reprint requests and other correspondence: M. Dietz, UCL Ear
Institute, 332 Gray’s Inn Rd., London WC1X 8EE, UK (e-mail: mathias.dietz
@uni-oldenburg.de).
www.jn.org
verberant speech—is a complex signal not easily parameterized, rendering difficult any systematic investigation of brain
mechanisms underlying phenomena, such as the Haas effect.
Consequently, many studies of reverberant listening have,
instead, used pairs of click stimuli (Litovsky et al. 1999) to
assess the neural basis of the related precedence effect (Wallach et al. 1949). In these studies, it is common to manipulate
the spatial cues of leading and lagging sounds by altering the
interaural time differences (ITDs) of each click pair to mimic
sounds arriving directly from a source and later-arriving reflections. These more easily parameterized and reproducible
stimuli facilitate investigation of neural mechanisms contributing to reverberant listening but at the expense of understanding how ongoing, modulated sounds, such as speech or music,
are processed in reverberant environments.
Recently, with the use of a novel stimulus [amplitudemodulated binaural beats (AMBBs); Fig. 1] designed to mimic
the temporal and interaural properties of reverberant speech
with a deterministic (and easily parameterized) synthetic signal, we reported that human listeners appear to “glimpse”
auditory spatial information arising during the early, rising
slope of each amplitude-modulation (AM) cycle and not at the
modulation maximum, where sound energy is greatest (Dietz et
al. 2013b). The AMBB is generated in two steps: 1) two pure
tones, one in each ear, with a small difference in frequency
(fbeat) are used to generate a binaural beat; 2) the tones in each
ear are AM, with an AM frequency (fm) equal to the beat
frequency (fbeat ⫽ fm), with the result that the interaural-phase
difference (IPD) of the binaural beat advances a full 360°
during each AM cycle. This generates a fixed-phase relationship between the beat cycle (i.e., the IPD) and the AM cycle,
which can be varied systematically by changing the start IPD
at the beginning of the modulation cycle.
Dietz et al. (2013b) reported that psychoacoustic and brainimaging [magneto-encephalography (MEG)] data were consistent
in showing a dominance of those IPDs occurring just after the
minimum in the modulation energy, essentially ignoring spatial
information conveyed later in the modulation cycle. This
preference for early-arriving spatial information in ongoing
sounds suggests the involvement of an adaptation process at
the neural level—whether spike-rate adaptation (Kiang et al.
1965) or temporal adaptation (Joris et al. 1994)—that emphasizes IPDs occurring in the rising portion of each AM cycle.
Here, with the use of single-neuron recordings from the inferior colliculus (IC) of guinea pigs and from histologically
verified recordings in the medial superior olive (MSO) of
gerbils, we demonstrate the features of a neural “readout” that
0022-3077/14 Copyright © 2014 the American Physiological Society
1973
Downloaded from http://jn.physiology.org/ by 10.220.33.4 on June 14, 2017
Dietz M, Marquardt T, Stange A, Pecka M, Grothe B,
McAlpine D. Emphasis of spatial cues in the temporal fine structure during the rising segments of amplitude-modulated sounds II:
single-neuron recordings. J Neurophysiol 111: 1973–1985, 2014.
First published February 19, 2014; doi:10.1152/jn.00681.2013.—
Recently, with the use of an amplitude-modulated binaural beat
(AMBB), in which sound amplitude and interaural-phase difference
(IPD) were modulated with a fixed mutual relationship (Dietz et al.
2013b), we demonstrated that the human auditory system uses interaural
timing differences in the temporal fine structure of modulated sounds
only during the rising portion of each modulation cycle. However, the
degree to which peripheral or central mechanisms contribute to the
observed strong dominance of the rising slope remains to be determined. Here, by recording responses of single neurons in the medial
superior olive (MSO) of anesthetized gerbils and in the inferior
colliculus (IC) of anesthetized guinea pigs to AMBBs, we report a
correlation between the position within the amplitude-modulation
(AM) cycle generating the maximum response rate and the position at
which the instantaneous IPD dominates the total neural response. The
IPD during the rising segment dominates the total response in 78% of
MSO neurons and 69% of IC neurons, with responses of the remaining neurons predominantly coding the IPD around the modulation
maximum. The observed diversity of dominance regions within the
AM cycle, especially in the IC, and its comparison with the human
behavioral data suggest that only the subpopulation of neurons with
rising slope dominance codes the sound-source location in complex
listening conditions. A comparison of two models to account for the
data suggests that emphasis on IPDs during the rising slope of the AM
cycle depends on adaptation processes occurring before binaural
interaction.
1974
EMPHASIS OF SPATIAL SOUND CUES DURING RISING SEGMENT II
A
4 Hz
...
8 Hz
...
16 Hz
...
32 Hz
...
1
0
-1
...
64 Hz
0
25
50
75
100
125
...
270°
...
225°
...
180°
...
135°
...
90°
...
45°
...
0°
...
negative IPD (ipsi leading)
positive IPD (contra leading)
1
no AM
0
-1
0
...
25
50
75
100
125
150
time (ms)
Fig. 1. Subset of the amplitude-modulated binaural beat (AMBB) stimuli used in the experiment. Only the 1st 150 ms of the 1-s stimuli are plotted. For the stimuli
in this figure, a mean carrier frequency of 288 Hz and of contralateral frequency (fcontra) ⬎ ipsilateral frequency (fipsi) was used. A: parametrical change of the
AMBB frequency (left), affecting both amplitude-modulation (AM) rate and beat rate. In this panel, the start interaural-phase difference (IPD) at modulation
minimum was kept constant at ⫺90°. B: parametrical change of the start IPD (left). The start IPD is defined as the IPD in each modulation minimum. Here, the
AMBB frequency was kept constant at 16 Hz. In addition to the fully AM binaural beats with 8 different start IPDs, a control condition with no AM was used
(bottom row).
occurs early in each modulation cycle of an ongoing, modulated sound. Whereas previous investigations of the neural
emphasis of spatial information have revealed the dominance
of the stimulus onset, i.e., the start of the stimulation (Devore
et al. 2009), the current study aims at generalizing this finding
to the ongoing portion of an amplitude-modulated stimulus.
Devore et al. (2009) suggested several possible neural mechanisms underlying the dominance of stimulus onset in spatial
information, which could operate before or after binaural
interaction (although they pointed out that their data did not
allow them to discriminate between these different mechanisms). Motivated by this open question, the second objective
of the current study was to assess whether adaptation before or
after binaural interaction underlies the localization dominance
of stimulus onset or, in our case, the rising portion of the
amplitude modulation. To disambiguate these two possibilities,
we recorded rate-vs.-IPD functions to static IPDs and to
AMBB stimuli. Through an analysis method similar to deconvolution, IPD readout-weighting functions were derived
from these pairs of rate-vs.-IPD functions. A comparison of
these readout-weighting functions with those derived from two
different binaural processing models demonstrates that only
adaptation before binaural interaction produces the observed
temporal weighting of spatial information. Thus, although the
ability to locate the source of a sound relies on interaural
comparison, the ability to do so in reverberant conditions is a
result of preceding sensitization of monaural inputs to the
rising slopes of ongoing, modulated sounds.
MATERIALS AND METHODS
Stimuli. The AMBB stimuli used in this study are identical to those
introduced by Dietz et al. (2013b). This section briefly repeats the
generation and the features of AMBBs.
Within a tone, each AM cycle is accompanied by a full 360°
transition in IPD (Fig. 1A). To achieve this, the AM rate was set to be
equal to the binaural beat rate, the latter determined by the frequency
difference of the sinusoidal carrier for the left and right ear stimulus.
The resulting stimulus thus contains a fixed relationship between
instantaneous IPD and the phase of the AM cycle, which was varied
systematically (Fig. 1B). The common rate of AM and binaural
beating is referred to as the AMBB frequency.
Both possible beat directions were used by choosing the larger
carrier frequency (fc) in the ipsilateral (to the recording site) or
contralateral ear. In addition to the AMBB, two different forms of
control stimuli were required: binaural beats with no AM and AM
tones with a fixed, static IPD.
Throughout, the polarity of the IPD was defined with respect to the
AMBB beat direction: positive IPD means leading on the side with the
higher carrier frequency. This nomenclature holds for start IPD,
instantaneous IPD, and also for the static IPD compared with beat
responses.
Single-neuron IC recordings. Single-neuron recordings were made
from 102 neurons in the IC of 17 adult guinea pigs (15 tricolored
guinea pigs, 96 neurons; two albinos, six neurons) under urethane
anesthesia (20% solution; 1.5 g/kg dosage ip) at the UCL Ear Institute
(London, UK). These experiments were carried out in accordance
with the Animal (Scientific Procedures) Act of 1986 of Great Britain
and Northern Ireland and under Home Office Project Licence no.
70/6826. IC recording equipment and methods were similar to those
used by Griffin et al. (2005).
Rimadyl (50 mg/ml solution; 2 mg constant dosage), a nonsteroidal, anti-inflammatory drug that also acts as an analgesic; Buprenorphine (0.3 mg/ml solution; 0.05 mg/kg dosage), an opioid; and
Colvasone (2 mg/ml solution; 2 mg/kg dosage), a corticosteroid with
an anti-inflammatory action, were administered subcutaneously at the
beginning of each experiment. Supplementary doses of Buprenorphine were administered when an experiment continued beyond 10 h.
Lignol, a local anesthetic containing lignocaine and lidocaine hydrochloride, was administered locally before any surgical incision. A
tracheal cannula was inserted, and core temperature was maintained
close to 37°C with a heating blanket (Harvard Apparatus, Kent, UK).
Animals were placed in a sound-attenuating chamber (IAC Acoustics,
Winchester, UK) and held in a stereotaxic frame with hollow ear
J Neurophysiol • doi:10.1152/jn.00681.2013 • www.jn.org
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contralateral stimulus
B
150
time (ms)
ipsilateral stimulus
315°
EMPHASIS OF SPATIAL SOUND CUES DURING RISING SEGMENT II
sinus. The dura was opened, and the underlying cerebellum was
partially aspirated to expose the floor of the fourth ventricle. The MSO
was accessed with recording electrodes that were positioned with a
micromanipulator, according to landmarks on the brain and in relation
to lambda. Specifically, the coordinates used to traverse the MSO
were 3,800 – 4,200 ␮m caudal and ⬇1,300 ␮m lateral from lambda.
The sound stimuli were delivered via earphones that were placed on
the ear canal. After recording, a lethal dose of pentobarbital (20
mg/ml) was administered, and the animal was perfused intracardially
with heparinized NaCl solution (0.9%), followed by a 4% paraformaldehyde solution. The brain was then sectioned in the coronal
plane, and sections (50-␮m thick) were stained for horseradish peroxidase (HRP) with diaminobenzidine (Adams 1977) and counterstained with Neutral red. The recording site was verified using light
microscopy (Fig. 2). In total, six recording sites were located in the
main cell band of the MSO, whereas three were located medial or
lateral to it.
Stimuli were digitally generated at a sampling rate of 50 kHz by
TDT System III, converted to analog signals (RX6; TDT), attenuated
to desired levels (PA5; TDT), and delivered to earphones (ER-2;
Etymotic Research, Elk Grove Village, IL). A good seal between the
ear canal and the output of the loudspeakers, as well as matching of
the sounds delivered to each ear, was confirmed using microphones
(ER-10B⫹; Etymotic Research) that measure the sound within the ear
canal.
Single-neuron responses were recorded extracellularly with glass
electrodes (5–15 M⍀; Harvard Apparatus), filled with 2% HRP in
10% NaCl. The recording electrode was tilted caudally by 20° and
advanced under remote control, using a motorized micromanipulator
(Digimatic; Mitutoyo, Neuss, Germany) and a PiezoDrive (Inchworm
controller 8200; EXFO Burleigh Products Group, Victor, NY). Recordings were filtered, amplified, and fed to a computer via an
analog-to-digital converter (RP2.1; TDT), running RPvdsEx (TDT).
Isolation of action potentials from single neurons was guaranteed by
visual inspection of amplitude and shape during the experiment and
by offline spike-cluster analysis (Brainware; TDT). To mark a recording site after successful recording, HRP was iontophoretically ejected
from the recording electrode (Fig. 2).
Methods identical to IC and MSO recordings. Stimulus generation
and data analysis were identical for IC and MSO recordings. The left
and right carrier frequencies were set, such that one was above and
one was below the characteristic frequency (CF) of the neuron, with
equal distances from the CF on a linear scale. Stimuli were presented
25 dB above a neuron’s estimated CF-tone threshold. Each neuron’s
CF and threshold were determined by recording a frequency-vs.-level
response area [see Griffin et al. (2005) for details]. For each of the five
different modulation frequencies (4, 8, 16, 32, and 64 Hz), 26 different
configurations were recorded as a block, which contained eight static
IPDs, eight right-beating stimuli, and eight left-beating stimuli. All
stimuli in each of these 24 conditions were amplitude modulated and
presented with eight different static IPDs (or start IPDs), spaced by
45°. Within the 24 conditions, the presentation order was randomized.
A block finished with two unmodulated beat stimuli— one left beating
and one right beating—with the beat frequency equal to the modulation frequency of the respective block. After the five modulationfrequency blocks were presented once and in random order, the entire
presentation order was reversed, and the blocks were presented again.
On average, 10 repetitions of the 130 stimulus conditions were
recorded from each neuron. An exception is MSO neuron #1, where
the 130 conditions were presented only once. Each stimulus duration
was 1 s, and the interstimulus interval was at least 500 ms. Unmodulated beats were gated with 10 ms-squared cosine ramps; AM stimuli
started and ended with AM minima. To exclude stimulus-onset
effects, neural responses within first 250 ms were excluded from the
data analysis.
Response latencies were estimated manually for each neuron by
determining the best alignment of the peaks of the cycle histograms
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speculae (modified from model 1730; David Kopf Instruments, Tujunga, CA). Before positioning the animal, the tragus of each ear was
cut to obtain clear access to the tympanic membrane. A craniotomy
was performed to expose the cerebral cortex overlying the IC, and the
covering dura was removed. The bullae were vented by insertion of
cannulae and sealed with Vaseline to equalize middle-ear and outsideair pressures. A parylene-coated tungsten microelectrode (1–2 M⍀;
World Precision Instruments, Sarasota, FL) was positioned above the
IC and advanced ventrally using a motorized micromanipulator
(model SM-5; Luigs & Neumann, Ratingen, Germany), remotely
controlled from outside of the recording booth. Stereotaxic coordinates were used, which, from experience, position the recording
electrode within the low-frequency, dorsolateral aspect of the IC.
Entry of the tip of the recording electrode into the central nucleus of
the IC was adjudged by the appearance of robust, neural responses,
well tuned for low sound frequencies and invariably, a preponderance
of neuron sensitivity to ITDs conveyed in the stimulus fine structure.
At the end of each experiment, animals were administered a lethal
dose of pentobarbital sodium (200 mg/ml ip).
Sounds were produced using Tucker-Davis Technologies’ (TDT;
Alachua, FL) digital signal-processing hardware. TDT’s Brainware
software controlled the sequence and parameters of the stimuli that
were computed on a RP2.1 processor (TDT), using the Real-Time
Processor Visual Design Studio (RPvdsEx; TDT). Stimuli were generated with a 48.828-kHz sampling rate and scaled such that their peak
voltages were 10 V at the digital-to-analog converter outputs. These
signals were attenuated to achieve the desired level for the experiments using PA5 (System III) modules (TDT). Sounds were delivered
by Etymotic ER-4S headphones with the common reference wire
separated to abolish crosstalk. Microphones (FG-3452; Knowles
Acoustics, Burgess Hill, UK) were used to measure the stimulus
within a few millimeters of the tympanic membrane to ensure that the
sounds delivered to each ear were well matched.
Electrical signals from the electrode were current amplified by a
headstage (RA16AC; TDT) and further amplified, filtered, and digitized by a preamplifier (Medusa RA16PA; TDT) at a 25-kHz sampling rate. The signal was conducted by a fiber-optic cable to the RX5
base station for further digital filtering (300 –3,500 Hz) and recording
onto a personal computer. Spike data were displayed in real time using
TDT’s Brainware software and selected, according to manually adjusted criteria, to ensure that the analyzed data stem from a single
neuron.
Single-neuron MSO recordings. Single-neuron recordings were
made from 18 neurons in the brain stem of five adult anesthetized
Mongolian gerbils (Meriones unguiculatus) at Ludwig-Maximilians
University, Department Biology II (Munich, Germany). Only those
nine neurons, histologically confirmed as residing within the MSO,
were used for further analysis. All gerbil experiments were approved
by the German Animal Welfare (Tierschutzgesetz; AZ 2112531-40/
01, AZ 55.2-1-54-2531-57-05), and methods were based on those
described by Pecka et al. (2008).
Gerbils were anesthetized initially with an intraperitoneal injection
(0.5 ml/100 g body wt) of a mixture of ketamine (20%) and xylazin
(2%), diluted in 0.9% NaCl solution. During surgery, a dose of 0.05
ml of the same mixture was administered subcutaneously every 30
min. During recording, animals were subcutaneously injected continuously with the same anesthetic via an automatic pump (801 syringe
pump; Univentor, Zejtun, Malta) at a pump rate of 1.6 –2.5 ␮l/min,
depending on body weight. The animal was transferred to a soundattenuated chamber and mounted in a custom-made stereotaxic frame
(Schuller et al. 1986). The animal’s position in the frame was
standardized by stereotaxic landmarks on the surface of the skull
(intersections of the bregmoid and lambdoid sutures with the sagittal
suture in horizontal alignment). Constant body temperature was maintained at 37°C using a thermostatically controlled heating blanket. To
gain access to the MSO, a craniotomy was performed just lateral to
midline of the skull and caudal to the posterior aspect of the transverse
1975
1976
EMPHASIS OF SPATIAL SOUND CUES DURING RISING SEGMENT II
static IPD-tuning curves nor the AMBB-tuning curves and is, therefore, not critical for deriving the estimated modulation-phase, IPDweighting function.
Static-tuning curves [RS(IPD); Fig. 3I] and AMBB-tuning curves
[RB(IPDstart); Fig. 3, G and K] were fitted with the function
Voltage (a.u.)
#1
R̃(IPD) ⫽ A ⫹ B sin(IPD ⫹ C) ⫹ D sin(2IPD ⫹ E)
d
500 µsec
m
200 µm
d
500 µsec
m
Five parameters (A–E) were determined, using the standard
MATLAB fit function (fit.m) with the “trust region” algorithm that
minimizes the root-mean-square (RMS) error between the fitted rate
function [R̃(IPD)] and the measured response rate [R(IPD)]. The
sin(2IPD) term was used to account for the sharply tuned or saw
tooth-shaped, IPD-tuning function, which is not fitted well by a pure
sinusoid. The magnitude of this term, specified by D, is typically
0.3– 0.5 times the magnitude of the main term B.
The bin width for the cycle histograms was 15°, resulting in 24 bins
for each histogram. Three-point averaging with a convolution kernel
of [.2 .6 .2] was applied to all histograms. The average AM cycle
histograms to static IPD stimuli (Figs. 3E– 6E) were fitted with stable
distributions (see below) to determine automatically and robustly their
peak position. Symmetric fit functions, such as Gaussian or squared
sinusoids, did not result in good fits, as the histograms were often
skewed, showing a rise steeper than the fall. The so-called stable
distributions (Nolan 2013) contain a parameter ␬ that allows the skew
of the distribution to be adjusted. To keep the number of fit parameters
to the minimal set of skew (␬), position (␮), and width (␴), the
following distribution was used
200 µm
f(␾) ⫽
1
2␲
兰
⬁
⫺⬁
dx exp关i共␮ ⫺ ␾)x
⫺ |␴x|(1 ⫹ 2i␬ sgn共x兲 log|x| ⁄ ␲)]
#8
Voltage (a.u.)
d
m
500 µsec
500 µm
Fig. 2. Examples of medial superior olive (MSO) action potential shape and
histology. The shapes of the action potentials of 3 MSO neurons are displayed
(left) as means. Shaded error bars are the SD. The respective recording site is
displayed (right). MSO neurons #1 and #8 were located in the main cell band
of the MSO, as shown histologically with horseradish-peroxidase (HRP)
staining. MSO neuron #6 was located medial (m) to the main cell band of the
MSO. Response properties of neurons #8 and #1 are shown below (see Figs.
5 and 6, respectively). Top: a large black trace is situated medial to the main
MSO cell layer, corresponding to the electrode track of a different penetration.
The HRP injections, dorsal (d) to the highlighted ones (top and middle),
indicate recording sites of different penetrations, which were distinguished
mainly by penetration depth. Bottom: the HRP injection, ventral to the
highlighted one, indicates a recording site of a different penetration. a.u.,
arbitrary units.
obtained from the unmodulated binaural beat stimuli (D and F of
Figs. 3– 6), with the peaks of the static IPD-tuning curve (redrawn
in D and F in black from I) for all beat rates, simultaneously. The
response latency includes both recording hardware and auditory
pathway components and was subtracted from the neural response
times before histograms were plotted. Note that the latency estimation is only critical for the correct positioning of the cycle
histogram within the stimulus cycle. It influences neither the recorded
(2)
where ␸ denotes the AM cycle phase. The average AM cycle histograms (e.g., Fig. 3E) were fitted with the distribution, maximizing the
correlation coefficient between the fitted and the actual histogram.
An important aspect of the single-neuron data analysis is to derive
the IPD weighting as a function of the AM phase (IPD-weighting
function). This function w(␸) quantifies how strongly the instantaneous IPDs, at each part of the AM cycle, contribute to the total
AMBB response (Fig. 3, H and J).
The following formulas describe how the IPD-weighting functions
w(␸) (e.g., Fig. 3J) can be estimated from the fitted, static-tuning
curve [e.g., R̃S(IPD); Fig. 3I] and the fitted AMBB-tuning curve [e.g.,
R̃B(IPDstart); Fig. 3K].
For any periodically amplitude-modulated stimulus, the total response Rout of a binaural coincidence neuron/AM cycle can be
described as
Rout ⫽
兰 d␾ w(␾) · R (IPD(␾))
(3)
S
For AMBB stimuli, the instantaneous phase IPD(␸) differs from the
AM cycle phase ␸ by the offset IPDstart
IPD(␾) ⫽ IPDstart ⫹ ␾
(4)
By substituting IPD(␸) in Eq. 3, an equation for RB(IPDstart) can be
derived
R̂B(IPDstart) ⫽
兰 d␾ w(␾) · R̃ (IPD
S
start ⫹
␾)
(5)
The equation differs from a convolution integral only by the
sign of ␸ in the R̃S function. The sign is influenced by the definition
of IPD with respect to the beat direction. In principle, therefore, the
AMBB-tuning function can be expressed by a proper convolution
of the static-tuning function and an IPD-weighting function.
The IPD-weighting function w(␸) is expressed by the same type of
fit function as the cycle histograms f(␸) (Eq. 2). Since Eq. 5 cannot be
easily solved for w(␸), an exhaustive search was implemented in a
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Voltage (a.u.)
#6
(1)
EMPHASIS OF SPATIAL SOUND CUES DURING RISING SEGMENT II
A
B
cycle histograms for AMBB; fcontra < fipsi
4 Hz
8 Hz
16 Hz
32 Hz
C
AM cycle histograms; static IPD; fcontra = fipsi
4 Hz
64 Hz
8 Hz
16 Hz
64 Hz
32 Hz
135
315
270
90
270
225
45
225
180
0
180
135
-45
135
90
-90
90
45
-135
45
0
-180
10
5
0
0° 90° 180° 270° 360°
start IPD at mod minimum
H w( ); f
contra
< fipsi
0.8
0.6
0.4
0.2
0
0°
I
90° 180° 270° 360°
AM phase
F
32 Hz
64 Hz
static IPD tuning fcontra = fipsi
1
15
10
5
0
-180° -90° 0° +90° 180°
static IPD
J
beat cycle histograms - no AM
0.8
0.6
0.4
0.2
0
0°
K
w( ); fcontra > fipsi
average response (1/s)
15
1
IPD weight w (a.u.)
< fipsi
16 Hz
0
E average AM cycle histograms; static IPD
average response (1/s)
contra
IPD weight w (a.u.)
average response (1/s)
G AMBB tuning; f
8 Hz
90° 180° 270° 360°
AM phase
AMBB tuning; fcontra > fipsi
15
10
5
0
0° 90° 180° 270° 360°
start IPD at mod minimum
Fig. 3. Response overview of inferior colliculus (IC) neuron #47; characteristic frequency (CF) ⫽ 320 Hz; 10 repetitions. All panels with blue backgrounds (left)
belong to beating stimuli, with fcontra ⬍ fipsi; those with yellow backgrounds (middle) to AM tones with static IPDs; and those with red backgrounds (right) to
beats, with fcontra ⬎ fipsi. A and C: latency-corrected cycle histograms (colored areas) for the 2 different beat directions of the AMBB. The 5 different columns
of each panel represent different modulation (mod) frequencies (4, 8, 16, 32, and 64 Hz, from left to right). The 8 different rows show data for 8 different start
IPDs (left). The sinusoidal envelope of the acoustic stimulus is plotted in gray. To provide information about where in the modulation cycle the best IPD occurs,
fitted static IPD-tuning functions from I are replotted in the cycle histograms with black lines. Note that according to the start IPD of the different rows, this
function occurs shifted in steps of 45°. B: respective data for the AM tones with various static IPDs (rows). E: mean histograms from B, averaged over the 8
static IPDs. D and F: beat cycle histograms for the unmodulated, binaural beats (start IPD ⫽ 180°). As in A and C, the fitted static IPD-tuning functions from
I are replotted with black lines. I: neural responses to the AM stimuli, as a function of static IPDs, are shown for the 5 different modulation frequencies. Each
data point corresponds to the pooled activity of 1 cycle histogram in B. Line colors indicate the modulation frequency corresponding to A–F. This neuron has
its maximum response (best IPD) at ⬃40°. G and K: neural responses to the AMBB stimuli as a function of start IPD. Each data point corresponds to the pooled
activity of 1 cycle histogram in A and B, respectively. H and J: w(␸), IPD weighting as a function of the AM-phase ␸, derived from the static-tuning curves (I)
and the respective AMBB-tuning curve (G and K). G, I, and K: solid lines represent functions fitted according to Eq. 1.
discrete, three-dimensional parameter space of skew (0 ⬍ ␬ ⬍ 0.9),
position (0° ⬍ ␮ ⬍ 360°), and width (4° ⬍ ␴ ⬍ 120°): Eq. 5 was
derived for all w(␸) functions possible with the three parameters.
Then, the triplet of parameters was chosen for w(␸) that maximizes
the correlation coefficient between the fitted-tuning curve R̃B
(IPDstart) and the set of derived tuning curves R̂B(IPDstart). Typically, the maximized correlation coefficient is ⬎0.98, so the
derived IPD-weighting function can reconstruct the fitted AMBBtuning curve almost perfectly from the static-tuning curve.
Computational model. Two versions of a simple binaural model
were compared to evaluate two possible explanations for the
observed physiological results. The focus is on the effect of
adaptation—resulting in an emphasis on the rising slope of the AM
cycle—located either before or after binaural interaction. Both
model versions consisted of simplified monaural preprocessing
stages: band-pass filtering, compression, half-wave rectification,
and low-pass filtering, as commonly used as front-end monaural
components of binaural models (Bernstein and Trahiotis 1996;
Breebaart et al. 2001; Klein-Hennig et al. 2011). Our implementation of this was identical to that used by Klein-Hennig et al.
(2011), with a fourth-order gamma-tone filter centered at the mean
carrier frequency of 500 Hz, a static power-law compression with
an exponent of 0.23, half-wave rectification with subsequent
square-law expansion, and 425 Hz, fourth-order, low-pass filtering.
Binaural interaction was modeled by multiplying left and right
inputs.
The dominance in the response of the rising slope of the
modulation cycle was realized by multiplying the cycle histogram
at the input of the slope, emphasizing stage with a half-waverectified sinusoid segment, shifted so that the resulting AM cycle
had its peak at ⬃90°, i.e., at maximum slope. This simple generator
of slope emphasis does not describe any particular physiological
mechanism, such as spike-rate adaptation, but serves, rather, to
generate AM cycle histograms akin to those typically recorded in
MSO neurons in response to 32 Hz AMBBs (e.g., Figs. 5E and 6E)
and similar to the responses of MSO neurons reported in Fig. 11 in
Joris (1996). This slope-emphasis stage was introduced just before
(version 1) or just after (version 2) binaural interaction.
RESULTS
For neural recordings, the same AMBB stimuli as in human
psychophysics (Dietz et al. 2013b) were used, with the exception that rather than binaural beats being centered at 500 Hz,
neural responses were assessed, with binaural beats centered at
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D beat cycle histograms - no AM
cycle histograms for AMBB; fcontra > fipsi
4 Hz
315
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EMPHASIS OF SPATIAL SOUND CUES DURING RISING SEGMENT II
A
B
cycle histograms for AMBB; fcontra < fipsi
4 Hz
8 Hz
16 Hz
32 Hz
64 Hz
C
AM cycle histograms; static IPD; fcontra = fipsi
4 Hz
8 Hz
16 Hz
32 Hz
64 Hz
315
135
315
270
90
270
225
45
225
180
0
180
135
-45
135
90
-90
90
45
-135
45
0
-180
10
0
0° 90° 180° 270° 360°
start IPD at mod minimum
contra
< fipsi
0.8
0.6
0.4
0.2
0
0°
32 Hz
64 Hz
I
90° 180° 270° 360°
AM phase
static IPD tuning fcontra = fipsi
1
30
20
10
0
-180° -90° 0° +90° 180°
static IPD
J
beat cycle histograms - no AM
0.8
0.6
0.4
0.2
0
0°
K
w( ); fcontra > fipsi
average response (1/s)
20
H w( ); f
IPD weight w (a.u.)
1
F
average AM cycle histograms; static IPD
average response (1/s)
AMBB tuning; fcontra < fipsi
30
IPD weight w (a.u.)
average response (1/s)
G
16 Hz
0
E
beat cycle histograms - no AM
8 Hz
90° 180° 270° 360°
AM phase
AMBB tuning; fcontra > fipsi
30
20
10
0
0° 90° 180° 270° 360°
start IPD at mod minimum
Fig. 4. Response overview of IC neuron #98. Same format as Fig. 3; CF ⫽ 736 Hz; 8 repetitions. Rising slope dominance in both the period histograms panels
(B and E) and in the IPD-weighting function (H and J) is strongest for 16 Hz, followed by 8 and 32 Hz, and only weak for 4 and 64 Hz.
the neuron’s CF. CFs ranged from 96 to 1,312 Hz, with an
average of 480 Hz.
A total of 102 single-neuron recordings was made from the
IC of 17 guinea pigs. Figure 3 shows responses of a typical IC
neuron to static IPDs and AMBBs, presented at CF (320 Hz).
Figure 3B shows cycle histograms of action potentials to AM
tones with various static IPDs and for AMBB stimuli with
various start IPDs [Fig. 3A, contralateral frequency (fcontra) ⬍
ipsilateral frequency (fipsi); Fig. 3C, fcontra ⬎ fipsi]. For example,
the stimuli shown in Fig. 1A are those used in Fig. 3C (270°
start IPD). Those shown in Fig. 1B correspond with 16 Hz
(Fig. 3C).
The AM cycle histograms for static IPDs (Fig. 3B) reveal
that for 8 Hz and 16 Hz, the neuron responds maximally during
the rising portion of the modulation cycle. They also reveal a
strong binaural tuning, with a preference for positive IPDs
(leading at the ear contralateral to the recorded IC).
Figure 3I shows the static IPD-tuning curve RS(IPD), revealing this neuron’s best IPD to be 40°. For this neuron, the static
IPD-tuning curves are largely independent of the AM frequency. The total response to AMBB stimuli of both beat
directions as a function of start IPD [RB(IPDstart)] is shown in
Fig. 3, G and K. Even though these stimuli contain all possible
IPDs, there is a considerable change in the response rate as a
function of start IPD. Note that the shapes differ between the
two beat directions, to some extent explained by the asymmetries in the frequency-tuning functions when swapping the
carrier frequencies, fc, between the ears to change the direction
of the binaural beat. As described in MATERIALS AND METHODS,
IPD-weighting functions (Fig. 3, H and J) were derived from
the static-response functions and the responses to the AMBB to
determine the region of IPD emphasis within the AM cycle.
Data from a second exemplary IC neuron are shown in Fig.
4. Compared with the example in Fig. 3, this neuron has its
maximum IPD weighting slightly earlier in the modulation
cycle (Fig. 4, H and J). Furthermore, the static AM cycle
histograms (Fig. 4, B and E) are more heavily skewed toward
the rising slope than for the example in Fig. 3. All cycle
histograms of one neuron are scaled by the same factor. The
factor is set so that the highest peak of all histograms fills the
available space.
In addition to the 102 IC neurons, recordings were made
from nine histologically verified neurons, recorded in the MSO
of anesthetized gerbils. These methodologically more-challenging experiments were conducted to determine whether the
principle findings from the IC are also observed at this primary
site of binaural interaction. Responses from the MSO neurons
(see Figs. 5 and 6 for two examples) indicate that primary
binaural neurons also have their maximum IPD weighting
during a short and early segment of the modulation cycle.
Population analysis was performed on those conditions
where the recorded data fulfilled selection criteria necessary to
estimate robustly and automatically all of the response parameters. The empirically determined criteria were: 1) a clear,
phase-locked neural response to the AM cycle [fitted to the
average AM cycle histogram (Figs. 3E– 6E) with ␴ ⱕ 90°; see
Eq. 2]; 2) an IPD-weighting function (Figs. 3, H and J– 6, H
and J) with a pronounced peak (with ␴ ⱕ 90°); 3) clear
binaural tuning to static IPDs [measured as vector strength,
VSstatic ⬎ 0.2, as described by van Hemmen (2013)]; and 4) an
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D
cycle histograms for AMBB; fcontra > fipsi
4 Hz
EMPHASIS OF SPATIAL SOUND CUES DURING RISING SEGMENT II
A
B
cycle histograms for AMBB; fcontra < fipsi
4 Hz
8 Hz
16 Hz
32 Hz
C
AM cycle histograms; static IPD; fcontra = fipsi
8 Hz
4 Hz
64 Hz
16 Hz
32 Hz
315
135
315
270
90
270
225
45
225
180
0
180
135
-45
135
90
-90
90
45
-135
45
0
40
20
0
0° 90° 180° 270° 360°
start IPD at mod minimum
contra
< fipsi
I
static IPD tuning fcontra = fipsi
80
0.8
0.6
0.4
0.2
0
0°
32 Hz
64 Hz
90° 180° 270° 360°
AM phase
1
60
40
20
0
-180° -90° 0° +90° 180°
static IPD
J
beat cycle histograms - no AM
0.8
0.6
0.4
0.2
0
0°
K
w( ); fcontra > fipsi
average response (1/s)
60
H w( ); f
IPD weight w (a.u.)
1
F
average AM cycle histograms; static IPD
average response (1/s)
E
AMBB tuning; fcontra < fipsi
80
IPD weight w (a.u.)
average response (1/s)
G
16 Hz
90° 180° 270° 360°
AM phase
AMBB tuning; fcontra > fipsi
80
60
40
20
0
0° 90° 180° 270° 360°
start IPD at mod minimum
Fig. 5. Response overview of MSO neuron #8; CF ⫽ 608 Hz; 8 repetitions. Same format as Figs. 3 and 4 (see Fig. 2 for the corresponding histology).
average response rate, R ⬎ 6 spikes/s, at best static IPD. In
total, 205 conditions from 53 different IC neurons and 64
conditions from nine MSO neurons fulfilled all of these criteria. Many of the 4-Hz conditions were rejected, because they
failed the two ␴ ⱕ 90° criteria.
It might be assumed that selecting neurons with a relatively
narrow IPD-weighting function (criterion 2) potentially favors
neurons with an earlier maximum position. This assumption
was tested by deriving the correlation between the width (␴) of
the IPD-weighting function and the cycle position of the
maximum IPD weighting. Practically no correlation (r ⫽
⫹0.02) was found in the IC data. A related, second hypothesis
concerns a correlation between the width of the static AM
cycle histogram (criterion 1) and the cycle position of the
maximum IPD weighting. Here, a correlation of r ⫽ ⫹0.18
(R2 ⫽ 0.035) was found. Specifically, the grand average of
“cycle position of the maximum IPD weighting” changes from
126° to 121° in the IC data when applying the filter for
criterion 1. This small bias within the population data is
appraised to be acceptable for filtering robust conditions. In the
MSO data, all neurons have a narrow IPD weighting and a
narrow AM cycle histogram anyway, with the result that the ␴
criteria filter out only very few conditions.
Importantly, in 198 of 205 IC conditions (Fig. 7A, ordinate)
and 61 of 64 MSO conditions (Fig. 7D), the maximum IPD
weighting was found during the rising portion of the modulation cycle (0 –180°). One hundred forty-two of 205 (69.3%) IC
and 50 of 64 (78.1%) MSO conditions have a maximum in the
interval (0° ⫺ 135°) and are therefore classified as showing
sensitivity to IPDs during the rising slope of the modulation
cycle. The other 63 IC and 14 MSO conditions have a maximum in the interval (135° ⫺ 225°) and are classified as peak
sensitive. No falling slope (225° ⫺ 360°) condition was found.
The AM phase with the maximum IPD weighting is positively
correlated, with the peak position in the AM cycle histogram.
The correlation coefficient between these two very different
measures is r ⫽ ⫹0.66 for the IC (Fig. 7A), and r ⫽ ⫹0.82 for
the MSO conditions (Fig. 7D).
In Fig. 7, C and F, each neuron’s preferred static IPD
(extracted from the data shown in Figs. 3I– 6I) was plotted
as a function of its best start IPD obtained from the AMBBtuning curve (Figs. 3, G and K– 6, G and K). The dashed,
black diagonals in Fig. 7 plot the expected position of data
points if the energy maxima of the AM would dominate the
IPD weighting. A vertical offset from this diagonal is a
simple estimate of how much the IPD dominance deviates
from the energy maximum. The downward shift, observed in
all cases, corresponds to dominance of IPDs within the
rising segment of the AM. The colored diagonals indicate
the average difference between best static IPD and best start
IPD for each modulation frequency. Although this method is
not as accurate a means of determining the dominance
region as the estimation the IPD-weighting function using
Eq. 5 and does not provide a measure of its width, this
display is similar to that used for plotting the psychoacoustic
data in Dietz et al. (2013b) (Fig. 3). In line with the
psychoacoustic data, most neural data points are below the
main diagonal at 180°, indicating that the neural response is
dominated by IPDs occurring during the rising slope of the
AM cycle.
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D
8 Hz
0
-180
beat cycle histograms - no AM
cycle histograms for AMBB; fcontra > fipsi
4 Hz
64 Hz
1979
1980
EMPHASIS OF SPATIAL SOUND CUES DURING RISING SEGMENT II
A
B
cycle histograms for AMBB; fcontra < fipsi
4 Hz
8 Hz
16 Hz
32 Hz
64 Hz
C
AM cycle histograms; static IPD; fcontra = fipsi
4 Hz
8 Hz
16 Hz
32 Hz
64 Hz
315
135
315
270
90
270
225
45
225
180
0
180
135
-45
135
90
-90
90
45
-135
45
-180
50
0
0° 90° 180° 270° 360°
start IPD at mod minimum
0.8
0.6
0.4
0.2
0
0°
contra
32 Hz
64 Hz
I
< fipsi
90° 180° 270° 360°
AM phase
static IPD tuning fcontra = fipsi
1
100
50
0
-180° -90° 0° +90° 180°
static IPD
J
K
w( ); fcontra > fipsi
0.8
0.6
0.4
0.2
0
0°
beat cycle histograms - no AM
average response (1/s)
100
H w( ); f
F
average AM cycle histograms; static IPD
average response (1/s)
1
16 Hz
0
E
AMBB tuning; fcontra < fipsi
IPD weight w (a.u.)
average response (1/s)
G
8 Hz
90° 180° 270° 360°
AM phase
AMBB tuning; fcontra > fipsi
100
50
0
0° 90° 180° 270° 360°
start IPD at mod minimum
Fig. 6. Response overview of MSO neuron #1; CF ⫽ 544 Hz; only 1 run. Same format as Figs. 3–5. The neuron has fully modulated-tuning curves, even for
the AMBB stimuli. Compared with the other MSO example (Fig. 5), it has a higher rate of ⬎100 spikes/s. It also has a high response rate to unmodulated, binaural
beats (D and F). All of the above mentioned also occurs in some of the other 7 MSO neurons (see Fig. 2 for the corresponding histology).
The strong bias toward the rising slope of the AM cycle
suggests that rapid adaptation is likely to reduce neural responses over the remainder of the cycle. The question remains
whether adaptation before or after binaural interaction underlies the observed weighting of spatial information. A simple
model considering both processing schemes illustrates that
only adaptation occurring before binaural integration can explain the observed data (Fig. 8): whereas pre- and postbinaural
adaptation influences the static AM cycle histograms [Fig. 8H1
is similar to Fig. 8H2, consistent with the data of Ingham and
McAlpine (2004)], only adaptation arising before binaural
integration shifts the maximum IPD weighting toward the
rising slope (Fig. 8G1 differs from Fig. 8G2), so that in
accordance with the neural data, the IPD-weighting function
and the static AM cycle histogram have a similar peak position
(peak of Fig. 8G1 identical to peak in Fig. 8H1).
DISCUSSION
Our data indicate that ITD-sensitive neurons in the brain
stem and midbrain show evidence of a readout window for ITD
cues during each cycle of an ongoing, modulated sound. At
least some neurons appear to glimpse spatial cues conveyed by
the temporal fine structure during the rising portion of the
modulation cycle, ignoring spatial information conveyed in
later, higher-energy components of the ongoing waveform.
This is likely beneficial for spatial listening in reverberant
listening conditions, where late-arriving signals may contain
spurious (and rapidly changing) spatial information. These
single-neuron data from small mammals are consistent with
recent behavioral and human-imaging data, indicating a readout window for spatial information during the early, rising
portion of each modulation cycle comprising an ongoing,
complex sound.
Relation to human data from AMBBs. With the comparison
of data from the current study with the psychoacoustic and
MEG data recorded with AMBB stimuli (Dietz et al. 2013b),
all four data sets (gerbil MSO, guinea pig IC, human MEG, and
human psychoacoustics) indicate a strong dominance of IPDs
during the rising portion of each modulation cycle (Table 1).
The earliest dominance within the modulation cycle is observed at low modulation rates in the human psychoacoustic
data (37° at 4 Hz and 40° at 8 Hz). There is then a systematic
increase of the dominant cycle position with increasing modulation rate. At 64 Hz, the dominant position lies at 115° for the
human psychoacoustic data and 138° for the human MEG data.
The single-neuron recordings concur better with the MEG
data in that the most dominant region observed with both of
these methods occurs slightly later than in the psychophysical
data but still during the rising slope. In contrast to the human
data, the mean single-neuron data appear not to depend systematically on modulation frequency. Although many neurons
show a strong IPD emphasis during the rising slope, there were
also many neurons consistently showing an emphasis between
135° and 195° at all modulation frequencies. To quantify this
finding, the data were, once more, analyzed in terms of slopesensitive and peak-sensitive conditions. Table 1 shows the 10th
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D
beat cycle histograms - no AM
IPD weight w (a.u.)
0
cycle histograms for AMBB; fcontra > fipsi
4 Hz
EMPHASIS OF SPATIAL SOUND CUES DURING RISING SEGMENT II
5
10
7
15
6
5
180°
4
3
90°
2
1
A
0°
0°
90°
180°
270°
360°
360°
90°
ic27
27
4
4
180°
3
2
90°
1
D
0°
0°
90°
180°
270°
360°
peak of AM cycle histogram (static)
c 49
69
c 70
i 92
c 81 i 47
i 81
70 i 81 i 70 i 96
cc
70
i
30
c 98
57 i 70 cc81
53
96
i 57
cc
30
i 96
c 56
c 96 i 70 c 49 c 47
47
i 98 c47
i 87
i 18
i 18 i i61
c 57
i 98
c 37
ic56
c 22
58
26
ic18
96
i 58
ic37
i26
22
c 56
i 57 i 54
45c 54
0
c 96
c 69
c 70 i 92
i 30
i 81
i 81
87
i 58
i 61
c 26
B
90°
c 30
c 92 i 70
i 47 i 18
c 37
i 57i 70
i 22 c 54i 45
c 49
i 57
i 96
i 18
i 98 i 56
i 96
i 98
c 49i 69
i 70
i 47
i 96
-90°
c 22
180°
270°
c 98
c 96
c 56c 57
c 22
cc47
53
70 c 57 c 70c 45
i 37 i 20
c 18 i 54
c 47
0°
c
ci26
45
i 20
i 26
360°
peak of IPD weighting function
peak of IPD weighting function
2
c 92
c 81c 81
c 56
c 22
c 27
c 26
C
±180
360°
0°
90°
180°
270°
360°
best start IPD at mod mimimum (beat)
±180
MSO; Nn eur = 9; Nc on d = 32
270°
MSO; Nn eur = 9; Nc on d = 32
+90°
c6
i6
180°
i6
ic7
c3
11
i 5 c9
c2
c9 i 9
i2
c8
i7
88 i 1 i 2
ic
ic
i2
11 c 2 c 1
i8
c4
c2
i4
ic64
c
2
i2
90°
E
90°
c 1c 9 c 9
i2
c1
c3 c2
i1
8
i8
i2
1
2
c8
c8
i7
i5
i2 i1
c2
i7
i1
i9
0°
c4
i2
i4
6
i6
i6
-90°
0°
0°
c4
c6
180°
270°
360°
peak of AM cycle histogram (static)
F
±180
0°
90°
180°
270°
360°
best start IPD at mod mimimum (beat)
Fig. 7. Summarizing data. A–C: IC data; D–F: MSO data. A and D: maximum IPD-weighting position (peak of the IPD-weighting function in Figs. 3, H and
J– 6, H and J) against the peak position of the static AM cycle histograms (Figs. 3E– 6E). The vertical histogram insets contain a summation over the peak position
of the static AM cycle histograms, leaving the maximum IPD-weighting positions as the only parameter of interest. They are separated by modulation frequency
(color-coded as in Figs. 3– 6). B and E: same as A and D but now with more restrictive criteria 1 ⫹ 2 [width (␴) ⬍ 60°], showing, therefore, fewer conditions
(Ncond). This restriction was applied only to allow for the labeling of individual data points: modulation frequency is color-coded as in Figs. 3– 6; the number
identifies the neuron (Nneur), and letters “i” and “c” indicate the beat direction (i for fcontra ⬍ fipsi; c for fcontra ⬎ fipsi). C and F: peak of the AMBB-tuning curves
(Figs. 3, G and K– 6, G and K) against the peak of the static IPD-tuning curve of the corresponding condition (Figs. 3I– 6I). The labels of the data points and
the threshold criteria were identical to B and E. The format is analogous to that of the psychoacoustic data (Dietz et al. 2013b) (Fig. 3) to demonstrate the
similarity between the psychophysical and physiological findings. The bold, dashed, diagonal lines indicate an offset of 180° between the static IPD and
AMBB-tuning curves. This characterizes a hypothetical dominance of the IPD at the energy maximum. The colored, diagonal lines have an offset according to
the across-neuron average of the maximum IPD-weighting position/modulation frequency (color codes as in previous figures). Data from the IC example neurons
in Figs. 3 and 4 can be found in B and C marked “47” and “98,” and data from the MSO examples in Figs. 5 and 6 can be found in E and F marked “1” and
“8”. Note that static IPDs are defined with respect to the beat direction (i.e., sign inverted when fcontra ⬍ fipsi). Therefore, data from the same neuron are plotted
at 2 different best static IPD values (if both fulfill the criteria). For instance, IC neuron #81 (C: best IPD 88° contralateral leading) is either plotted at a best IPD
of ⫹88° (c81) or at ⫺88° (i81).
and the 90th percentiles of the maximum IPD-weighting positions for each modulation frequency, indicating that for the
slope-sensitive conditions (10th percentile), the maximum
IPD-weighting position indeed increases systematically with
modulation frequency. Notably, these values are similar to the
(mean) human behavioral data. This is especially true for 8 –32
Hz for both MSO and IC data. The maximum IPD-weighting
positions at 4 Hz in the IC data and at 4 Hz and 64 Hz in the
MSO data are different than the maximum IPD weightings
observed in humans. The trend of the position change with
changing AMBB frequency is also not apparent. We suggest
that these modulation frequencies are a little beyond the
frequency range for which “glimpsing” of IPD information
during the rising slope is effective for these species. Similarly, these frequencies have been reported to be close to the
limits of glimpsing in the human data (Dietz et al. 2013b),
but for the current single-neuron data, the frequency range
appears even more restricted. At the other end of the
distribution (90th percentile), the maximum IPD-weighting
positions are close to 180° and show no systematic frequency dependence.
Based on the variety of neural response types and the
similarities of slope-type conditions and human perception, it
might be speculated that slope-sensitive neurons contribute to
the perceptual lateralization of sounds, whereas—perhaps even
more speculatively—peak-sensitive neurons might be more
important in encoding other features of the acoustic space, such
as interaural coherence, from which subjects can obtain information about the acoustic scene (i.e., room size, number of
sources, etc.). This dichotomy might also be conceptualized as
coding of foreground vs. coding of background spatial
information.
Relation to previous physiological studies. Most previous
studies of binaural hearing have focused their endeavors either
on unmodulated, low-frequency stimuli or on high-carrier
frequency AM stimuli [reviewed by Joris et al. (2004)], be-
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5
0
i 69
180°
0°
6
270°
+90°
peak of AM cycle histogram (static)
MSO; Nneur = 9; Nc o nd = 64
i 27
c 58
270°
peak of AM cycle histogram (static)
360°
IC; Nn eur = 23; Nc ond = 61
i 26 i 26
0°
0
±180
IC; Nn eur = 23; Nc ond = 61
best static IPD
0
270°
8
peak of IPD weighting function
peak of IPD weighting function
IC; Nneur = 53; Nc ond = 205
best static IPD
9
360°
1981
1982
EMPHASIS OF SPATIAL SOUND CUES DURING RISING SEGMENT II
A1
A2
adapt.
X
adapt.
X
adapt.
B1
C1
D1
B2
C2
D2
270°
90°
0°
response
F1
1
.5
AMBB tuning curve
0
0° 90° 180° 270° 360°
start IPD at mod minimum
.3
0
0°
H1
90° 180° 270° 360°
AM phase
.9
.6
.3
0
0°
90° 180° 270° 360°
AM phase
1
.5
static IPD tuning curve
0
-180° -90° 0° +90°+180°
static IPD
F2
1
.5
AMBB tuning curve
0
0° 90° 180° 270° 360°
start IPD at mod minimum
G2
weighting
.6
E2
.9 IPD weighting w(
.6
.3
0
0°
H2
response
static IPD tuning curve
0
-180° -90° 0° +90°+180°
static IPD
.9 IPD weighting w(
response
.5
G1
weighting
1
response
response
E1
0° 90° 180°270° 0° 90° 180°270° 0° 90° 180°270° 360°
AM phase
AM phase
AM phase
response
0° 90° 180°270° 0° 90° 180°270° 0° 90° 180°270° 360°
AM phase
AM phase
AM phase
90° 180° 270° 360°
AM phase
.9
.6
.3
0
0°
90° 180° 270° 360°
AM phase
Fig. 8. Comparison of 2 processing schemes with an auditory model. Left: prebinaural adaptation (adapt.); right: postbinaural adaptation. Note that the processing
stages do not refer to specific brain nuclei. Prebinaural can be, for example, in the auditory nerve, the cochlear nucleus, or the synaptic transmission at the MSO
neurons. Postbinaural means the MSO output. A1 and A2: the 2 processing schemes. B1 and B2: 4 AMBB input stimuli with different start IPDs (blue represents
the left and red the right ear’s signal). C1 and C2: AM cycle histograms at an intermediate processing stage (C1: after peripheral processing and adaptation but
before binaural interaction; C2: after peripheral and binaural processing but before adaptation). D1 and D2: AM cycle histograms at the model output
(corresponding to the data presented in Figs. 3C– 6C). E1 and E2: normalized static IPD-tuning curve of the model (corresponding to the data presented in Figs.
3I– 6I). The model does not include an internal, interaural delay compensation, so its best IPD is 0. F1 and F2: normalized AMBB-tuning curve as a function
of start IPD (corresponding to the data presented in Figs. 3K– 6K). G1 and G2: IPD-weighting function derived from the 2 tuning curves in E1 and E2 and F1
and F2 (corresponding to the data presented in Figs. 3J– 6J). H1 and H2: average AM cycle histogram (corresponding to the data presented in Figs. 3E– 6E).
It can be seen that the model with prebinaural adaptation has its highest response with start IPD ⫽ 270° (D1, top), whereas the postbinaural adaptation model
has its maximum response with start IPD ⫽ 180° (D2, 2nd from top). This results in different IPD-weighting function with rising-slope dominance only observed
with prebinaural adaptation (left), whereas the average AM cycle histograms of both models are very similar with peaks at 90°.
cause perceptually, the lateralization cue of pure envelope ITDs in
low-frequency AM stimuli is very weak compared with ITDs in
the temporal fine structure (Bernstein and Trahiotis 1985). A few,
more recent, physiological studies, examining neural sensitivity to
high-frequency envelope ITDs, also focused on potential differences between the rising and the falling portions of the ongoing
envelope of high carrier-frequency stimuli (Dietz et al. 2013a;
Nelson and Takahashi 2010) and report a much stronger influence
of the rising segment.
In contrast to the studies using high carrier-frequency AM
stimuli, however, it is not the envelope ITD under investigation
here but, rather, the influence of an in-phase envelope on
dynamic ITD processing in the temporal fine structure. Sterbing et al. (2003) also investigated the influence of an in-phase
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180°
EMPHASIS OF SPATIAL SOUND CUES DURING RISING SEGMENT II
1983
Table 1. Summary of the maximum IPD-weighting position within the AM cycle for all methods
Method
MSO
IC
Psycho
MEG
Criterion
4 Hz
8 Hz
16 Hz
32 Hz
64 Hz
Cell mean
10th Percentile
90th Percentile
Cell mean
10th Percentile
90th Percentile
6 Subject mean
7 Subject mean
112°
68°
160°
137°
55°
192°
37°
98°
56°
138°
99°
45°
160°
40°
122°
89°
206°
113°
72°
149°
62°
123°
86°
174°
143°
98°
193°
83°
108°
136°
105°
161°
125°
85°
166°
115°
138°
A dominance of the rising portions (0 –180°) in the ongoing envelope is found throughout all methods and modulation frequencies. A detailed explanation is
given in the main text. IPD, interaural-phase difference; AM, amplitude modulation; MSO, medial superior olive; IC, inferior colliculus; Psycho, psychoacoustic;
MEG, magneto-encephalography.
be an important measure, but we are unaware of any published
data to this effect.) However, even without latency correction,
the skewed response of auditory nerve fibers to sinusoidally
amplitude-modulated (SAM) tones (Joris and Yin 1992) suggests a strong dominance of the rising slope, resulting from
spike-rate adaptation. Furthermore, recordings to a train of
short tone pulses in fibers of the trapezoid body, which leave
the cochlear nucleus to innervate the binaural nuclei, indicate
that adaptation reduces the spike rate to approximately one-half
of its maximum, ⬍10 ms after the onset of a tone pulse (Fig.
7B in Joris et al. 1994). With tone-pulse repetition rates of 10
Hz, the temporal properties are indeed comparable with the
current SAM cycle histograms at 8 Hz or 16 Hz. These data
also suggest some form of temporal adaptation, in that action
potentials generated earlier during the pulse train show higher
temporal precision than those generated later during the pulse
train. In terms of binaural coincidence detection, this would
result in fewer coincidences at best IPD later during the
pulse train or, here, occurring later within each modulation
cycle. Thus both spike-rate adaptation and temporal adaptation likely contribute to the biasing of ITD sensitivity
toward the rising portion of the AM cycle. This is consistent
with Kuznetsova et al. (2008), who reported that at least in
the avian auditory system, temporal adaptation in the cochlear nucleus plays a critical role in enhancing sensitivity
to ITDs at stimulus onset.
Nevertheless, cellular mechanisms, contributing to enhanced
slope sensitivity, remain unclear. Although low-threshold potassium channels are reported to shape response sensitivity for
the rising stimulus slope in MSO neurons, thereby resetting the
membrane potential during each stimulation cycle (Gai et al.
2009), these channels act on very short time scales. It seems
unlikely that these channels contribute to the adaptation effects
that we observed during AMBB stimulation, which by contrast,
accumulated over multiple cycles of the temporal fine
structure.
Implications for models of binaural interaction. Many existing binaural models that account for perceived lateralization
lack a realistic stage of preprocessing that includes the prebinaural adaptation necessary to model any dominance of the
rising segments and are therefore unable to explain the perceived lateralization of the AMBB stimuli (Dietz et al. 2009;
Stern and Shear 1996). Even models that include adaptation
(Breebaart et al. 2001; Dau et al. 1996; Klein-Hennig et al.
2011) do not generate a significant imbalance between the
rising and the decaying portions of the stimulus envelope.
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AM on the IC response but only on (quasi-) static IPD
processing. Consistent with the current data, they reported a
maximum response early in the modulation cycle and that
static IPD-tuning functions of unmodulated and modulated
stimuli are similar in shape and best IPD position. Additionally, they reported that some neurons show a higher firing rate
for unmodulated stimuli—an observation not reflected in the
current data set. A likely explanation for this difference is that
these authors measured neural responses for sounds with constant peak amplitude (i.e., the unmodulated tones had a higher
RMS level), whereas the current study measured neural responses for a constant RMS level. Studies investigating binaural processing at the level of single neurons often use
binaural beats of very low beat frequencies, usually 1 Hz
(Fitzpatrick et al. 1997; Joris 1996; Sterbing et al. 2003; Yin
and Chan 1990; Yin and Kuwada 1983). The slow beats have
been used as quasistatic IPD, to obtain complete IPD-tuning
curves efficiently with one continuous stimulus. In combination with AM (Sterbing et al. 2003), these stimuli are, in fact,
slow binaural beats with an amplitude modulation but should
not be confused with those of the current study, because the
beating in their stimuli was not synchronized with the AM.
Binaural beats with high beat rates but without a synchronized AM have also been used in single-neuron recordings in
the brain stem (Siveke et al. 2008), as well as at the level of the
IC (Fitzpatrick et al. 2009), and reveal a high temporal resolution of the binaural system at these subcortical stages. In line
with these studies, the unmodulated binaural beat control
conditions in the current study also show a phase-locking at the
highest tested beat frequency of 64 Hz in some neurons (e.g.,
Figs. 3, D and F, and 6, D and F).
Our data demonstrate that the position within the AM cycle
that shows the maximum IPD weighting is correlated with the
position of the response peak to static IPDs conveyed with an
AM cycle. This can be interpreted as a linear and instantaneous
coincidence detection (in line with, for example, van der
Heijden et al. 2013) in primary binaural neurons (e.g., in the
MSO): each neuron codes the IPD with a weighting, according
to the characteristics of its response to the AM cycle. However,
the main outcome from the current study is not only that these
factors are correlated but also that both quantities are strongly
dominated by IPDs within the rising slope of the AM cycle.
No existing physiological data are available to quantify the
strength of adaptation to AM stimuli that occurs before the
stage of binaural integration. (Latency-corrected, modulationcycle histograms of bushy cells of the cochlear nucleus would
1984
EMPHASIS OF SPATIAL SOUND CUES DURING RISING SEGMENT II
ACKNOWLEDGMENTS
We thank Dr. Philip X. Joris for suggesting the possible additional influence
of temporal adaptation. M. Dietz thanks Dr. Roberta Donato for her invaluable
help and guidance during the first experimental recordings in guinea pigs.
GRANTS
Support for this research was provided by the U.K. Medical Research
Council Programme (grant G1002267 to D. McAlpine). Support for M. Dietz
was provided by the Alexander von Humboldt Foundation with a Feodor
Lynen Fellowship.
DISCLOSURES
The authors declare that there are no conflicts of interest.
AUTHOR CONTRIBUTIONS
Author contributions: M.D., T.M., and D.M. conception and design of
research; M.D. and A.S. performed experiments; M.D. and T.M. analyzed
data; M.D., T.M., and D.M. interpreted results of experiments; M.D., T.M.,
and A.S. prepared figures; M.D. drafted manuscript; M.D., T.M., A.S., M.P.,
B.G., and D.M. edited and revised manuscript; M.D., T.M., A.S., M.P., and
D.M. approved final version of manuscript.
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