Japan Advanced Institute of Science and Technology

JAIST Repositoryhttps://dspace.jaist.ac.jp/

Title聴覚生理モデルを用いた音源方向定位に関する基礎的

研究

Author(s) 伊藤, 一仁

Citation

Issue Date 1998-03

Type Thesis or Dissertation

Text version author

URL http://hdl.handle.net/10119/1133

Rights

Description Supervisor:赤木 正人, 情報科学研究科, 修士

Study on Sound Localization

using Auditory Function Model

Kazuhito Ito

School of Information Science,

Japan Advanced Institute of Science and Technology

February 13, 1998

Keywords: ITD,Je�ress's model,impulse,synaptic transmission,inhibition.

Sound localization is an auditory function to detect of sound source location using

the interaural time di�erence(ITD) and the interaural level di�erence(ILD) between ears

at which the sound waves arrive. In this study, a function model of the auditory sound

localization based on the interaural time di�erence(ITD) is presented.

Sound waves arriving at ears are decomposed into their frequency components and are

changed into impulse trains by the auditory periphery. The impulse trains keep timing

information in itselves because auditory nerves tend to �re at a certain phase of stimuli.

The system for detecting ITDs exists in medial superior olive(MSO), where the nerves

coming from left and right ears cross each other. The Je�ress model is one of model

circuits for the detection. This model has been approved because of simple theory and

structual analogy between the model and tissues in MSO. At the beginning in this study,

the Je�ress model is implemented computationally to examine its mechanism and to

improve.

The Je�ress model is represented as a circuit which consists of some coincidence de-

tectors and two nerve �bers from left and right ears. The detectors �re only when impulse

trains coming from both sides through nerve �bers arrive simultaneously. Thus the model

can caliculate ITDs with correlation between impulse trains coming from both sides(Figure

1).

When a sound source is placed in front of the head, arrival times are equal on the left

and right pathways, because the times that sound wave comes to ears and impulse trains

come to the circuit are equal. Then, the middle detector in the circuit responds most

strongly. The position of the responding detector varies as a sound source moves.

Auditory nerve �bers do not always �re at a certain phase of stimuli. Impulses uctuate

temporally and it is di�cult to detect ITDs with the correlation between those impulses.

Copyright c 1998 by Kazuhito Ito

1

sound source

left ear right ear

relay station in brain

sound source

delay lineleft ear right ear

coincidence detector

relay station in brain

delay line

coincidence detector

Figure 1: Je�ress model

Figure 2 shows the period histogram of impulse trains with large uctuation and the

output from the circuit with correlation between impulses. The ITD is not clear.

0 50 100 150 200 250 300 3500

50

100

150

200

250

300

f=3kHz

PHASE [o]

SP

IKE

S

−600 −400 −200 0 200 400 6000

100

200

300

400

ITD [µs]

coin

cide

nces

f=3kHz

*

Figure 2: period histogram and the output

Therefore, in this study, the time length and the amplitude of impulses in the circuit

are modi�ed into a reasonable shape like a sawtooth wave, to reduce errors of the detection

against uctuation of impulse trains. Figure 3 shows the histogram of the same impulse

trains as Figure 2 and the output from the circuit with sawtooth waves. The peak shows

the correct ITD clearly.

The model has another problem, related to phase ambiguity that detectors in the

circuit respond to more than one ITD, as represented �t + nT (n is an integer. T is a

period.) when the actual ITD is �t. To solve the problem of phase ambiguity, the circuits

2

0 50 100 150 200 250 300 3500

50

100

150

200

250

300

f=3kHz

PHASE [o]

SP

IKE

S

−600 −400 −200 0 200 400 6000

100

200

300

400

ITD [µs]

coin

cide

nces

f=3kHz*

Figure 3: period histogram and the output

are arranged in each frequincy band and the detectors in the circuits are grouped with

the same ITD. The most �ring group indicates the actual ITD(Figure 4). In fact, it is

found that ITD detectors in organisms are arranged systematically along frequency axis.

The model can be improved with the physiological knowledge. However, the simple

mechanism to detect the correlation of stimuli from ears, like this model, can not work

well under real environments with noise from some sources, like orgnisms do.

To investigate these issues in more detail, signal representations in orgnisms, such

as nervous impulse or synaptic transmission, are modeled computationally according to

physiological knowledge. For example, an impulse is a deviation of membrane potential

called 'action potential' with certain time duration. Additionally, synaptic transmissions

extend the width of action potentials. Then, timing information may become obscure.

These signals are applied to the coincidence detector circuits of ITD in the model. The

results of the simulation using a coincidence detector circuit show that �rings of one

coincidence detection spread over the circuits, in response to just one correlation (Figure

5). Thus, it is di�cult to determine the actual ITD using one coincidence detector circuit.

To determine ITD with more accuracy and calculate it more e�ciently, selecting by

many thresholds and inhibition are assumed. Since the coincidence detector indicating

the actual ITD tends to �re earlier than others, the �rst �ring event at the actual ITD

excits its own postsynaptic neurons and inhibits other ones(Figure 6). Consequently, the

model with inhibition can improve accuracy to detect ITDs(Figure 7).

In conclusion, nervous impulses and synaptic transmission from left and right ears

in MSO are modeled computationally and are applied to a past coincidence detecotor

circuit model to detect ITD. The results show that the past model is not able to detect

actual ITD independently. Then, the selection by many thresholds and the inhibition are

modeled to improve detection accuracy of ITD.

3

−600 −400 −200 0 200 400 6000

0.5

1

RA

TE

−600 −400 −200 0 200 400 6000

200

400

1.5k

Hz

−600 −400 −200 0 200 400 6000

200

400

3kH

z

−600 −400 −200 0 200 400 6000

200

400

4kH

z

−600 −400 −200 0 200 400 6000

200

400

5kH

z

ITD [µs]

Figure 4: solution for phase ambiguity

01

23

4

−2−1

01

2−80

−75

−70

−65

−60

−55

−50

−45

−40

−35

−30

spatial summation

TIME[ms]

broad firing

AZIMUTH(ITD)[ms]

corr

elat

ion:

EP

SP

[mV

]

Figure 5: spatial summation in the circuits

4

0.7 0.8 0.9 1 1.1

−0.50

0.5−41

−40.5

−40

−39.5

−39

TIME[ms]

EPSP

peak

AZIMUTH(ITD)[ms]

threshold

EP

SP

[mV

]

0.7 0.8 0.91 1.1

−0.5

0

0.50

0.5

1

TIME[ms]AZIMUTH(ITD)[ms]

Timing of FiringE

PS

P[m

V]

Figure 6: the di�erence in the timing of �ring

0

5

10

15

20

−1

−0.5

0

0.5

1−80

−70

−60

−50

−40

−30

with inhibition

sum of 3 types

TIME[ms]AZIMUTH(ITD) [ms]

PS

P w

/inhi

bitio

n [m

s]

Figure 7: psp with inhibition

5