Download הרצאה 12

‫עבוד אותות במערכת החושים‬
‫סמסטר א' תש"ע‬
http://www.eng.tau.ac.il/~mira/Senses2009
Lecture 12
Auditory Binaural Pathway
Neural Auditory pathway
•
•
•
•
•
•
•
Auditory Nerve
Cochlear Nucleus
Medial Superior Olive
Lateral Superior Olive
Lateral lemniscus
Inferior Colliculus
Medial geniculate
nucleus
• Primary auditory
cortex
Schematic Representation of
the Brainstem Auditory Pathway
Brainstem
MRI & fMRI Scans
Axial
Coronal
Sagittal
Auditory Pathway and Brainstem
Outline Overlapped on fMRI Scans
Binaural Stimulation
Axial cut (6/12) -SOC
Coronal cut (6/11) - AC
Coronal cut (2/11) - MGB
0.025
0
Axial cut (2/12) - CN
Axial cut (10/12) - LL
Auditory Lateralization Cues
• Interaural Time delay –
The sound reaches the
closest ear before the
other
• Interaural Level delay
– The sound at the
closest ear is louder
LATERALIZATION
Perception
Stimulus
R
ILD
L
4
3
2
1
5
6
7
8
9
R
ITD
R
ITD=ILD=0
L
L
Normal Performance
Histograms =
number of times a subject reported perceiving a position when ITD or ILD presented
Abnormal Performance
Side --Oriented
9
9
9
9
8
8
8
8
7
7
7
7
6
6
6
6
5
5
5
5
4
4
4
4
3
3
3
3
2
2
2
2
1
-1
1
- 10
1
-1
0
ITD(msec)
1
position
position
Center-Oriented
0
ILD(dB)
10
0
ITD(msec)
1
1
- 10
0
ILD(dB)
1 0
Lesion Detection
Correlation between MRI and Lateralization
Normal Lateralization
MS10
MS20
MS50
MS52
CVA23
Correlation between MRI and
Lateralization
Center-Oriented Lateralization
Correlation between MRI and Lateralization
Side-Oriented Lateralization
Correlation between MRI and Lateralization
MRI
LATERALIZATION
NORMAL
PERFORMANCE
CENTER-ORIENTED
SIDE-ORIENTED
NO
LESIONS
MS10
MS20
MS50
MS52
CVA23
TB
LESIONS
TB&LL
LESIONS
LL
LESIONS
CVA25
CVA36
MS3
MS22
CVA29
CVA30
CVA32
MS46
MS48
CVA37
CVA54
CVA44
MS7
MS11
MS15
CVA39
CVA43
Monaural & Binaural
Activation in a Right Sagittal Section
Left Ear
Stimulation
Right & Left
Ears Stimulation
Right Ear
Stimulation
Monaural & Binaural
Activation in a Left Sagittal Section
Left ear
stimulation
Both ears
stimulation
Right ear
stimulation
‫‪Binaural masking‬‬
‫צליל מונוטוני ורעש מושמעים‬
‫לשתי האוזניים‪ .‬הרעש ממסך את‬
‫הצליל‪.‬‬
‫ניתן לגרום לכך ששוב נשמע את‬
‫הצליל על ידי כך שניצור הפרש‬
‫פאזה בין הצלילים בשתי‬
‫האוזניים‪.‬‬
‫צליל מונוטוני והרעש מושמעים‬
‫לאוזן אחת‪ ,‬קשה להבדיל את‬
‫הצליל מהרעש‪ .‬הרעש ממסך‬
‫את הצליל‪.‬‬
‫מוסיפים רק רעש לאוזן השניה‬
‫–ובאורח מוזר‪ ,‬ניתן שוב‬
‫לזהות את הצליל על רקע‬
‫הרעש‪.‬‬
‫‪Cocktail party effect‬‬
‫אם יש כמה מקורות‬
‫קול מופרדים במרחב‪,‬‬
‫קל להקשיב לקול אם‬
‫הקול שאנו מעונינים בו‬
‫נמצא במיקום שונה‬
‫מקולות הרקע‪ .‬זה נובע‬
‫מכך שהצליל והממסך‬
‫יוצרים קונפיגורציה‬
‫אינטרנאורלית שונה‬
‫מזו של הממסך בלבד‪.‬‬
Find the minimum ITD/ILD
• We will conduct an experiment to measure
the minimum ILD/ITD.
tRight , LRight
tLeft , LLeft
‫‪Minimal Audible Angle - MAA‬‬
‫מהו ההפרש הקטן ביותר בשינוי מיקום הקול המאפשר הבחנה בשינוי‬
‫זה?‬
‫היכולת שלנו להבחין בהפרש‬
‫במיקום מקור הקול היא טובה‬
‫ביותר כשהקול מגיע מלפנים‬
‫בחזית הראש‪ .‬יכולת זו הולכת‬
‫ופוחתת כשמקור הקול נמצא בצדי‬
‫הראש או מאחור‪.‬‬
‫שינויים קטנים בכיוון הקול מלפנים‬
‫יוצרים הבדלים גדולים ב‪. ITD -‬‬
Superior Olive Complex
Coincidence Detection Cells
• Coincidence detection (CD) is one of the
common ways to describe the functionality
of a single neural cell.
• Correlation
• There are several type of such cells:
– Excitatory Inhibitory (EI)
– Excitatory Excitatory (EE)
– Cumulative
Neural mechanisms – EE
Type cells

Spikes when inputs coincide.
E
Input E
Input 1
_
I
E(1)I (t )
EE (t )
EE
Input 2
E
 E( 2)I (t )
_
I
Input E
max( , )  
1
r
2
r
EE  t   1  t 
t
t
   t dt   t    t dt
2
t 
2
1
t 
EE Formulation

EI  t (pE )  tq( I )  
 
or t (pE )  tq( I )  0;  0  t (pE )  T ,0  tq( I )  T
NE
NI
i 0
j 0

P EI   P(nE  i ) P(nI  j ) P( EI nE  i, nI  j )
T
P(0  t
(E)
p
t
(I )
q
  nE  i, nI  j )   P(t (pE )  t nE  i ) P(t    tq( I )  t nI  j )dt
0
T
P (0  t
(E)
p
t
(I )
q
t
  nE  i, nI  j )    (t )  I* (t ')dt 'dt 
*
E
0
t 
t
 T

P( EI )  exp    E (t )  I (t ')dt 'dt 
t 
 0

0
E I
Neural mechanisms – EI
Type cells

Spikes with excitatory input unless
inhibited.
Input E
EI
Input I
 
E
r
t


EI  t   E  t  1   I   d  
 t 

EI Formulation
P ( EI )  P  nE  0  
N
 P n
E
n 1

M
 n   P  nI  m  P t (pE ) tq( I ) , 0  t (pE )  t q( I )   nE  n, nI  m
mn
T
P(0  t
(E)
p
t
(I )
q
  nE  n, nI  m)     t 
*
E
0
N
P( EI )  e  E  e  E 
 0 
n!
n 1
P ( EI )  e
 E T 
N

n 0
n
n!

  t   dt dt 
*
I
t 
  I  e

mn  m  n !
mn
M
  T  
0
t
N
I
e  E 
n 0
e
 E T   0 T 
e
0
E I
 0 
n!
n
n
t



 E  t  1
I  t ' dt ' dt


0
 t 

T
n



Complex Cells
Input 1
...
Input 2
E N L
Input M
Inhibitory input
CDE


t
t


     1    j (t ')dt '    l (t )    j (t ')dt '

 lI
L ' L I L '  jI N 
jI L ' t 
t 


L'
j l
 j IL '

N
N
L
EI Cells Signal Separation
Signal separation ability is considered as
most important in tasks such as cocktail
party, BMLD.
Spiking rate
[normalized]

S+N
300
250
200
EI
N
|fft(response)|
[normalized]
2
4
6
Time [mSec]
8
0.1
0.05
0
200
300
400
500
Frequency [Hz]
600
EE Cells spontaneous rate

The spontaneous rate of cells
that results from external noise
reduced at higher levels
ITD Mean Rate
From Agmon-Snir et al.(1998),
Nature 293,268-272
Model Predictions
EE
EIL
0.8
0.6
0.4
0.2
EIR
1
1
0.99
0.99
Normalized mean rate
VS = 0.64
VS = 0.47
VS = 0.22
Normalized mean rate
Normalized mean rate
1
0.98
0.97
0.96
0.95
0.94
0
-180
-90
0
IPD [ ]
90
180
0.93
0.98
0.97
0.96
0.95
0.94
-180
-90
0
IPD [ ]
90
180
0.93
-180
-90
0
IPD [ ]
90
180
ILD Mean Rate
EIL
EE
EIR
1.2
1.2
0.8
0.6
1
Normalized mean rate
Normalized mean rate
0.8
0.6
0.4
VS = 0.64
VS = 0.46
VS = 0.22
1
0.8
0.6
0.4
0.4
-10
-5
0
ILD [dB]
5
0.2
-10
10
-5
0
ILD [dB]
5
0.2
-10
10
200
-5
0
ILD [dB]
Saturation Rate
Tollin&Yin (2004) Data
EI  ipsi 1  contra 
  200  sec
M  60
M
Mean Rate [spikes/sec]
Normalized mean rate
1
Theoretical Fit
150
100
50
Spontaneous Rate
0
-30
-20
-10
0
ILD [dB]
10
20
30
5
10
JND(ITD)
All Information: Rate & Timing
EIL
1.06
1.05
1.04
1.03
1.02
1.01
1
-180
-90
0
IPD [ ]
90
3
Normalized JND (IPD) LB
1.07
EIR
1.025
Normalized JND (IPD) LB
Normalized JND (IPD) LB
EE
VS = 0.64
VS = 0.47
VS = 0.22
1.02
1.015
1.01
1.005
1
180
-180
-90
0
IPD [ ]
90
2.5
2
1.5
1
180
-180
-90
0
IPD [ ]
90
180
90
180
Rate Only
EE
EIL
10
-180
-90
0
IPD [ ]
90
180
Normalized JND (IPD) LB
VS = 0.64
VS = 0.47
VS = 0.22
100
1
EIR
1000
1000
Normalized JND (IPD) LB
Normalized JND (IPD) LB
1000
100
10
1
100
10
1
-180
-90
0
IPD [ ]
90
180
-180
-90
0
IPD [ ]
Phase Delay in EE Cells Inputs
L  t     S L  t ,   exp B  S L  t ,   sin(2 ft   L     L  f ) 

R  t     S R  t ,   exp B  S R  t ,   sin(2 ft   R     R  f )
65
60
2.8
2.6
50
2.4
2.2
CRLB [ normalized]
Optimal phase [ ]
55
45
40
35
2
1.8
1.6
1.4
1
2
3
4
Sin amplitude
5
6
1.2
1
-100
-50
0
Rel phase [ ]
50
100
Prediction of JND(ITD) from EE Cell
N Excitatory Inputs
E
E
N=6
EE
N Excitatory Inputs
N=20
Binaural EE
Normalized JND (IPD) LB
1
0.95
0.9
N=6
0.85
0.8
0.75
VS = 0.64
VS = 0.47
VS = 0.22
0.7
0.65
-180
-90
0
IPD [ ]
90
180
N=20
Prediction of JND(ILD) from EI Cell
6
Mills
1 Excitatory Inputs
E
Level JND [dB]
5
EI
E
M Inhibitory Inputs
4
3
M=3
2
0
0
VS = 0.64
VS = 0.46
VS = 0.22
1.15
2
4
6
Frequency [kHz]
8
10
1.1
6
1.05
1
0.95
-10
Hershkowitz and Durlach
Hershkowitz and Durlach
5
-5
0
ILD [dB]
5
10
• Rate Coding and All Information Coding
provided similar results
Level JND [dB]
Normalized JND (ILD) LB
1
4
M=3
3
2
1
M=15
0
0
20
40
Level [dB SPL]
60
80