This document summarizes a computational modeling study that simulated the effects of acoustic trauma on neurons in the dorsal cochlear nucleus (DCN). The study used a DCN model with auditory nerve input provided by a model of the auditory periphery that can simulate outer and inner hair cell damage. Simulating different levels of hair cell damage produced response maps of DCN neurons that resembled physiological recordings showing "tail responses" following acoustic trauma. The modeled tail responses were broadly tuned low-pass filters, consistent with recordings. This computational model provides a way to study how acoustic trauma affects neural processing in the DCN.

1. J. Vander Sloten, P. Verdonck, M. Nyssen, J. Haueisen (Eds.): ECIFMBE 2008, IFMBE Proceedings 22, pp. 2695–2698, 2008
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A Computational Modeling Study of the Effects of Acoustic Trauma
on the Dorsal Cochlear Nucleus
X. Zheng1
, A. Giang1
, S. Vetsis2
, I.C. Bruce2
, and H.F. Voigt1
1
Department of Biomedical Engineering, Boston University, Boston, United States
2
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
Abstract — The dorsal cochlear nucleus (DCN) is the most
complex part of the cochlear nucleus and has been shown to be
involved in sound localization and in some auditory perceptual
phenomenon, e.g., tinnitus. The responses of DCN neurons
have been modeled in several studies, e.g., Zheng and Voigt
2006 [1]. They simulated both DCN principal cell response
maps (RMs) and notch-noise responses. In this study, the same
DCN model is used to study the effects of acoustic trauma on
DCN neurons using the auditory periphery model of Zilany
and Bruce 2006 [2]. This model is capable of modeling the
effects of hearing loss. The RM properties of DCN model
principal cells are compared to recordings of Ma and Young
2006 [3] from unanesthetized, decerebrated cats following
exposure to trauma-producing acoustic noise. By manipulating
the DCN model parameters, similar tone responses following
acoustic trauma can be simulated. DCN model neurons show
RMs with either no response or only tail responses, depending
on the degree of acoustic trauma. This study may help to
determine the source of different tail responses, which has not
been studied in previous physiological experiments. It also
suggests that similar responses following acoustic trauma will
be found in the DCN of other animals, e.g., gerbil.
Keywords — Dorsal cochlear nucleus, acoustic trauma,
response maps
I. INTRODUCTION
Exposure over time to noise will degrade hearing and
often cause sensorineural hearing loss that arises from the
damage of cochlea hair cells. This lesion in hair cells
introduces changes in the physiological and perceptual
response properties in the central auditory pathway and the
level of hearing loss can be medium to profound, often
more severe than the conductive hearing loss that results
from the defects in the outer and middle ear. [4]
As the dorsal part of the first nucleus in the auditory
pathway, the dorsal cochlear nucleus (DCN) presents the
ability of analyzing complex sound. It has an important role
in sound localization and has been hypothesized to be the
site of tinnitus [5]. The study of the responses of DCN
neurons will give more insight about how the auditory
cortex will react after hair cell damage.
The goal of this study is to simulate physiological
plausible model responses of the DCN principal neuron
after acoustic trauma with a DCN model that has
successfully reproduced the tonal and notch noise responses
of the DCN principal neurons [1]. The Zilany and Bruce
auditory peripheral model is used to provide auditory nerve
input to the DCN model, for its capability of modeling the
outer and inner hair cell damage.
The results are compared to the response map (RM)
properties in a recent study by Ma and Young [3], in which
acoustic trauma was introduced in cat by exposure to band
pass noise. Although they did not discriminate the RM types
before the acoustic trauma, all the RMs of neurons in DCN
after exposure showed no, weak or only tail responses to the
tonal stimuli. Only the RM properties of DCN principal
cells, fusiform cells, are studied. Generally speaking, the
inhibitory area increases in the frequency vs. sound level
plane changing from a type I unit to a type V unit. Type III
and type IV units are of particular interest because they
have been associated with gerbil and cat principal cells that
send afferent axons to the inferior colliculus. Type III unit
RMs are characterized by a central V-shape excitation
region with side band inhibition. Type IV unit RMs are
characterized by an excitatory island at best frequency of
the unit at low-level sound stimuli, surrounded by an
inhibitory region at higher sound levels. The type IV-T unit
is a transient state between type III and type IV units and
the region above the excitatory island often show activity
that resembles spontaneous activity.
II. METHOD
A. The DCN model
The computational DCN model has the same architecture
as in reference [1]. As shown in Fig. 1, four neuron groups
are arranged in a logarithmic frequency axis with even steps
of 0.005 octaves. There are 800 neurons available for each
neuron group with their characteristic frequencies (CFs, for
auditory nerve fibers) or best frequencies (BFs, for other
neurons) varying from 1.25 kHz to 20 kHz. P-cells
represent principal cells in DCN; I2-cells represent narrow-
band interneuron inhibitors in DCN,which show type II unit
RM properties and provides inhibition to P-cells; W-cell
represents wide-band inhibitors, which provides wide-band

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Fig. 1 The DCN model is organized in a tonotopic manner with frequency
steps of 0.005 octaves. Five neuron groups are involved in this model.
Excitatory and inhibitory connection relationships among them are shown
in open and filled circle, respectively. Reprinted from [1].
inhibition to P-cells and I2-cells; AN-fibers are the auditory
nerve fibers; Non-specific afferents provide the source of
the excitation to P-cells to maintain its spontaneous activity
even after cochlear ablation, and are implemented as a
Poisson process with rate parameter of 1800 per second. P-
cells in this study have fixed BFs at 5 kHz.
Individual P-cell, I2-cell and W-cell are modeled by a
MacGregor neuromine [6], which is a lumped electrical
circuit model with each parallel branch representing
membrane capacitance and leakage conductance. All the
parameters specific to the property of each neuron (intrinsic
parameters) are kept the same through the simulations as
in [1].
For each connection relationship in Fig. 1, N numbers of
source cell A (NA→B) that were chosen from a band of
source cells (BWA→B) send axons to a target cell B. These
two parameters together with the strength of the connection
(σA→B) are important and varying them gives different
response properties of P-cells (see Fig. 2). Three of the
connection strength parameters will be changed compared
to [1] to obtain more physiological plausible RMs after
switching the AN inputs from the Carney AN model to the
Zilany and Bruce AN model [2].
B. The auditory nerve model
Carney’s 1993 model was replaced by the Zilany and
Bruce’s 2006 model [2], in which the hair cell damage can
be specified by two coefficients, CIHC and COHC, which are
ranging from 0 to 1, representing completely damaged to
normal inner and outer hair cells.
The Zilany and Bruce model is an improved version of
Carney model. It has more accurate BM threshold tuning
curves and improved BM tuning for high-level stimuli. The
input to Zilany and Bruce model is pure tone waveforms
and the output of the model is spike times.
C. Simulation protocols
Four sets of 800 auditory nerve fibers, with CFs shown in
Fig. 1, were simulated using the pairs of hair cell coefficient
to be CIHC = 1.0 COHC=1.0, CIHC = 1.0 COHC=0.5, CIHC = 1.0
COHC=0 and CIHC = 0 COHC=0. The sound stimuli were 31
slices of single tones with the frequencies varying within a
three-octave range centered at 5 kHz and evenly stepped at
0.1 octaves. The sound levels ranged from 0 dB SPL to 90
dB SPL in 6 dB steps. The tone stimuli were presented once
per second for 200 ms, with a 10 ms onset delay and 5 ms
ramp at two ends of the 200 ms stimulus.
The DCN model was simulated with most of the
parameters untouched as in the nominal parameter set in [1].
The RMs in Fig.2 were plotted as in [1]. The RMs in Fig. 3
were a combination of 2D RMs and rate vs. sound level
plots. The spontaneous activity (SpAc) and driven rates
were smoothed by a 3×3 low-pass filter with FIR
coefficients 1/9. Driven rates of two standard deviations
above and below the mean SpAc were regarded as
excitatory and inhibitory responses, respectively. In Fig. 3,
for sounds from 6 dB SPL to 78 dB SPL, the rate vs. sound
frequency plot was implemented into the frequency-sound
level plane. This plot showed more details of the exact
firing rate (after three-point average filtering) and was
comparable to the results of Ma and Young [3].
III. RESULTS
A. Parameters searching to obtain more physiological
plausible RMs using the Zilany and Bruce AN model
as input
When using the nominal parameter set in [1] to simulate
the Zilany and Bruce model fed DCN model, most units in
the 10 by 10 matrix of Figure 4 in [1] had RMs that were
not physiological plausible. The new simulated RMs had
wider band of excitation that had more weight at the low
frequency side of the exciting V shape, lower threshold that
was even close to 0 dB SPL, and much less of an inhibitory
region.
To obtain RMs similar to Figure 4 in [1], the inhibitory
inputs to P-cells were reinforced. We achieved this by
modifying three parameters: σAN→I2, σAN→W and σW→P.
σAN→I2 was increased from 0.55 to 0.8. σAN→W was
increased from 0.06 to 0.08. σW→P was increased from 0.5
to 1. By increasing one of the parameter or even
combinations of two of the parameters gave less desirable
RMs, which might lack side inhibition at the low frequency
side of Type III units, and not have enough inhibition at
high frequency region for type IV units, etc. This
modification of the three parameters gave a nice 10 by 10

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matrix that containing multiple types of RMs in
considerable numbers. Type III, type IV and type IV-T units
were found in the 10 by 10 matrix that was not shown in
this paper. Nevertheless, three of those were picked up for
more detailed display in Fig. 2 and 3. Typical type III, type
IV and type IV-T units are shown.
B. The RMs simulated with different levels of hair cell
impairments
As shown in Fig. 2, the decrease of hair cell coefficients
had profound effect on the P-cell response maps. When
COHC was decreased to 0, as shown in the third row from top
in Fig. 2 and the right column in Fig. 3, the RMs of all three
units show broad tuned excitatory region, with no or much
less inhibitory region left. The thresholds of the units were
significant higher than normal. When both CIHC and COHC
were decreased to 0, which was the most severe hair cell
damage status, the inhibitory region disappeared and only
excitatory responses at high-level sound were observed.
However, decreasing COHC to 0.5 only influences the RMs
in a limited scope without changing the types of the RMs.
Fig. 2 Type III, type IV and type IV-T unit response maps of three P-cells
are shown with auditory input simulated with different hair cell
coefficients. Each column from left to right represents type III, type IV and
type IV-T units, respectively. Each row from top to bottom represents the
responses of healthy unit (CIHC = 1.0, COHC=1.0), outer hair cell partially
damaged unit (CIHC = 1.0, COHC=0.5), outer hair cell completely damaged
unit (CIHC = 1.0, COHC=0) and both inner and outer cell completely
damaged unit (CIHC = 0, COHC=0), respectively. For each panel one
response map is shown with blue represents excitatory region, red
inhibitory region and gray spontaneous activity region.
Fig. 3 RMs with rate vs. sound level plots of the same unit in Fig. 2. A,
Type III unit; B, type IV unit and C, type IV-T unit. The left column
corresponds to the top row in Fig. 2 (CIHC = 1.0, COHC=1.0), and the right
column corresponds to the third row in Fig. 2. (CIHC = 1.0, COHC=0). For
each sound level, mean SpAc rate was plotted as a line. The driven rate
region above and below the mean SpAc rate line was filed with blue and
red, respectively, representing excitatory and inhibitory responses. Rate
axis was cut at 150 spikes per second. All P-cells had BF at 5 kHz.

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In Fig. 3, the entire right column RMs lacked the sharp
excitatory responses at BF of the units. Instead, most
excitatory responses spread out from the low frequency to
higher than the BF with raised spike rates and thresholds,
which was much more obvious in B and C. The driven
rates in A were slightly decreased at levels above 66 dB
SPL, while the activities under sound level of 66 dB SPL
were non-specific. All three RMs with severe outer hair cell
damage showed “tail responses” as defined by Ma and
Young [3], which were characterized by “poorly-tuned
responses extending broadly to low frequencies, resembling
the tail of an auditory nerve fiber tuning curve”.
The three tail responses shown in Fig. 3 can be
categorized into the most common class A neuron, which
behaves like a low-pass filter that shows broadly-tuned
excitation at and below an upper cutoff frequency (here near
the 5 kHz). Another common feature of a class A neuron is
that a narrowly-tuned region residing within 10 to 20 dB
SPL of threshold can also clearly seen in these three tail
RMs. The corresponding frequency at the narrow-tuned
region is defined as the BF of the tail responses. In these
three units tested, the BF of tail responses were still at the
normal BF of the unit.
IV. DISCUSSION
It is reasonable to assign the BF to the tail responses at
the narrow-tuned region near the cutoff frequency of the
“low-pass filter”, since in our simulations the BFs of the tail
responses were near the BF for normal RMs.
Class B tail responses have an additional excitatory
region above BF at high sound levels. Class C tail responses
show only an inhibitory region. These latter two tail
responses are not shown and it may be possible to simulate
the Class B tail responses by shifting the center BF of I2-
cells by -0.2 to P-cell, which will decrease the inhibition
above cutoff frequency; this offset is also physiological
plausible. Simulation of Class C tail responses will need
more inhibition at whole band of frequencies, which may be
hard to realize with the current two inhibitory sources: I2-
cells and W-cells. An additional lateral inhibitor may
contribute to it.
Some studies showed that the spontaneous activity of
principal cells elevated after acoustic trauma induced
cochlear damage and this hyperactivity was associated with,
and might be the reason of, tinnitus [5]. Nevertheless,
another study reported that the spontaneous activities of
type III units increase after trauma but no obvious change in
spontaneous rates of type IV units was observed. The
mechanism of this unit-specific change of spontaneous
activity is still unclear. A new mechanism, e.g., the parallel
fiber system, is required to explain the spontaneous rates
increase with cochlear damage, which receive somato-
sensory and central nerve system efferent inputs.
The spontaneous rates for the three shown units were
almost the same after inducing the hair cell damage.
The inner hair cell damage influences the responses of P-
cells in a similar trend as the outer hair cell, while they
differ slightly in the way of reorganizing the excitatory and
inhibitory regions in the RMs. These data were not shown.
Since the inner and outer hair cells have different functional
roles in hearing, it may be interesting to look into the
differences that they affect the RMs of P-cell.
Type II units and other units show tail responses after
acoustic trauma. This may be studied by exploring the
responses of I2-cells and W-cells in the model.
V. CONCLUSION
The DCN model combined with the Zilany and Bruce
AN model [2] is capable of modeling the normal and tail
responses of the principal cells in DCN before and after
acoustic trauma. Moreover, by arrange the AN fibers that
connect to healthy or damaged hair cells, it may enable us to
simulate the DCN neurons across the whole frequency band
after different noise-induced acoustic trauma.
REFERENCES
1. Zheng, X. and H.F. Voigt (2006) Computational model of response
maps in the dorsal cochlear nucleus. Biol Cybern 95(3): p. 233-42.
2. Zilany, M.S. and I.C. Bruce (2006) Modeling auditory-nerve
responses for high sound pressure levels in the normal and impaired
auditory periphery. J Acoust Soc Am 120(3): p. 1446-66.
3. Ma, W.L. and E.D. Young (2006) Dorsal cochlear nucleus response
properties following acoustic trauma: response maps and spontaneous
activity. Hear Res. 216-217: p. 176-88.
4. Salvi, R.J., J. Wang, and D. Ding (2000) Auditory plasticity and
hyperactivity following cochlear damage. Hear Res 147(1-2): p. 261-
74.
5. MacGregor, R.J. (1987) Neural and Brain Modeling, ed. R.F.
Thompson. San Diego: Academic Press.
6. Kaltenbach, J.A. (2007) The dorsal cochlear nucleus as a contributor
to tinnitus: mechanisms underlying the induction of hyperactivity.
Prog Brain Res 166: p. 89-106.
Corresponding author:
Author: Herbert F. Voigt
Institute: Boston University
Street: 44 Cummington Street
City: Boston
Country: United States
Email: hfv@bu.edu