Effect of synchronous vs. non-synchronous recordings

Effect of synchronous vs. non-synchronous recordingsShow movie then say that is an average and show a movie based on activity from a single trial (NOISY)Then say noise could look worse if correlated noise is bad.Compare shifted and unshifted population performanceCompare IC vs. A1

Effect of Neural Correlations on Speech Discrimination

Mike KilgardAssociate Professor

University of Texas at Dallas

Cosyne08 Workshop: Real-Time Processing and the Processing of Time

Manner of Articulation

Stops Fricatives Affricates Nasals Glides Liquids

Place o

f Articu

lation

30 kHz

Time (ms)182.5949 282.5949 382.5949 482.5949 582.5949 682.5949 782.5949 882.5949

0 200 400 600 ms

Kad Gad Shad Had

Bad Fad Vad

Sad Zad

Mad Wad

Chad Jad Nad

Place o

f Articu

lation

30 kHz

Time (ms)182.5949 282.5949 382.5949 482.5949 582.5949 682.5949 782.5949 882.5949

0 200 400 600 ms

Kad Gad Shad Had

Bad Fad Vad

Sad Zad

Mad Wad

Chad Jad Nad

m/n r/l sh/s sh/ch sh/h sh/j sh/f d/t d/g d/b d/s 0

Consonant Discrimination Task

ss*********

Rats can discriminate most human speech sounds.

Data from 5 or 6 rats after ten days of training on a Go/No Go task.

Observation:

Rats can discriminate most speech sounds.

Question:

How are these sounds represented in the central auditory system?

Neurograms of 445 multi-unit recordings from anesthetized A1, 20 repeats

N = 63 A1 multi-unit recording sites, average of 20 repeats

Movie of A1 responses from one rat

Observation:

Most speech sounds evoke unique spatiotemporal activity patterns in A1.

Question:

What is the relationship between neural responses and speech discrimination ability?

Euclidean distancebetween neurogramsis well correlated with behavior only when 1 ms bins are used.

N=445 A1 multi-unit recording sites

Observation:

Sounds that evoke dissimilar spatiotemporal activity patterns are readily discriminable.

Question:

Can A1 neurons discriminate speech sounds in a single trial?

Single Trial

N = 63 A1 multi-unit recording sites

0 10 20 30 401

95% (68%)

bad dad

0 10 20 30 401

100% (63%)

0 10 20 30 401

80% (60%)

0 10 20 30 401

75% (63%)95% correct 100% correct 80% correct 75% correct

PSTH-based Classifier (Foffani & Moxon, 2004; Schnupp et al., 2006)

Single trials matched to mean PSTH templatesusing Euclidean distance.

Triangles indicate classification errors.

DadBad

0 10 20 30 401

95% (68%)

bad dad

0 10 20 30 401

100% (63%)

0 10 20 30 401

80% (60%)

0 10 20 30 401

75% (63%)95% correct 100% correct 80% correct 75% correct

DadBad

rial P

Mean of 445 sites was 90±3% when 1-10 ms bins were used.

Classifier Performance using Spike Timing

0 10 20 30 401

95% (68%)

bad dad

0 10 20 30 401

100% (63%)

0 10 20 30 401

80% (60%)

0 10 20 30 401

75% (63%)

DadBad

68% correct 63% correct 60% correct 63% correct Classifier Performance using Spike Number

a) Onsetspike timing

b) Onsetmean rate

r l y w n m j ch h z v sh s f g d b k t p

p t k b d g f s

sh v z h

m n w y l r

c) Full responsespike timing

mean rated) Full response

p t k b d g f s

sh v z h

m n w y l r

Neural discrimination using one sweep of activity from individual multi-unit clusters recorded in A1

N=445 A1 multi-unit recording sites

a) Onsetspike timing

b) Onsetmean rate

p t k b d g f s

sh v z h

m n w y l r

c) Full responsespike timing

mean rated) Full response

p t k b d g f s

sh v z h

m n w y l r

Observation:

A single trial of onset activity from A1 neurons recorded at a single site can discriminate speech sounds as well as rats.

Question:

Is this true in awake rats?

Awake Auditory Cortex

50 60 70 80 90

Classifier Percent Correct

R2=0.63, P=0.004

sh/ssh/ch

d/bd/s

N = 41 A1 multi-unit recording sites

Question:

How good is speech discrimination if more or fewer A1 sites are used?

Observation:

A single trial of onset activity from awake A1 neurons recorded at a single site can discriminate speech sounds as well as rats.

1 4 16 1 4 16 64

Number of Number of Single Unit Sites Multi-Unit Sites

d/sd/bd/gd/tsh/fsh/jsh/hsh/chsh/sr/lm/n

Neural Discrimination of English ConsonantsP

Question:

Can A1 activity distinguish speech sounds from a larger set?

Observation:

A single trial of activity from several hundred A1 neurons can discriminate between pairs of English consonants with 100% accuracy.

1 4 16 1 4 16 64 256 0

Number of Number of Single Unit Sites Multi-Unit Sites

1 ms bins5 ms bins20 ms bins40 ms bin

Neural Discrimination of 20 English Consonants

Observation:

A single trial of activity from a set of ~400 A1 neurons can discriminate between 20 English consonants with 100% accuracy.

Question:

Is this an artifact of serial recordings?

10 2030 40501

95% (68%)

bad dad

10 20 30 40 501

100% (63%)

10 2030 40501

80% (60%)

10 2030 40501

75% (63%)95 % correct 100% correct 80% correct 75% correct

DadBad

rial P

DadBad

rial P

50 100 150 2001

95 % correct

0 20 40 60 80 1000

100Discrimination of 20 Consonants

Percent correct using SIMULTANEOUSLY recorded responses-20 -10 0 10 20 30 400

Improvement in Discrimination Caused by Serial Recordings

Neural Discrimination of 20 English Consonants

Observation:

A single trial of activity from a set of four simultaneously recorded A1 multi-unit clusters can discriminate between 20 English consonants almost as well as serially recorded sites.

Question:

Is this true in awake rats?

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

Check duration/bins

-20 -10 0 10 20 30 400

Improvement in Discrimination Caused by Serial Recordings

Observation:

A single trial of activity from a set of four simultaneously recorded A1 multi-unit clusters can discriminate between 20 English consonants almost as well as serially recorded sites.

Possible Explanation:

The readout mechanisms are optimized for categorization, not identification.

Place o

f Articu

lation

30 kHz

Time (ms)182.5949 282.5949 382.5949 482.5949 582.5949 682.5949 782.5949 882.5949

0 200 400 600 ms

Kad Gad Shad Had

Bad Fad Vad

Sad Zad

Mad Wad

Chad Jad Nad

50%Van Rullen & Thorpe, 2001

Image reconstructions using only temporal order of the first spike of Retinal Ganglion Cell populations

Speech Conclusions1. Rats are able to accurately discriminate most speech sounds.

2. Consonants appear to be represented by onset firing patterns.

3. A single sweep of activity is sufficient to discriminate most sounds.

4. Responses in A1 are highly correlated with behavioral discrimination.

Tomorrow at ‘Linking Auditory Neurophysiology to Perception’

1. Speech in Noise 2. Non-Primary Auditory Cortex and Inferior Colliculus3. Vowel coding4. Categorization5. Plasticity

Acknowledgements:Crystal Engineer - Speech Training and A1 physiology Claudia Perez - Speech Training and Inferior Colliculus Jai Shetake - Awake A1 Speech Physiology

Figure 7

Onset 1 ms bins

Onset 40 ms bin

Duration 1 ms bins

Duration 700 ms bin

SixteenMulti-Units

OneMulti-Unit

OneSingleUnit

SixteenSingleUnits

SixteenMulti-Units

OneMulti-Unit

SixteenSingleUnits

OneSingleUnit

Supplementary Figure 2

Neural Activity Patterns Predict Speech Discrimination Ability in Rats

Mike KilgardUniversity of Texas at Dallas

What is the relationship between neural responses and speech discrimination ability?

0 200 400 600 8000

Mean IC PSTH

0 100 200 300 400 500 600Time (msec)

N=187 sites, 6 rats

0 100 200 300 400 500 600

Medium

0 100 200 300 400 500 600Time (msec)

SadN=187 sites, 6 rats

Mean IC PSTH’s

Inferior Colliculus Neurograms N=187 sites, 6 rats

Spatial Temporal

N=441 sites, 11 rats A1 Neurograms

0 100 200 300 400 0 100 200 300 400

Sad Dad

Medium

Time (milliseconds)

0 100 200 300 400 0 100 200 300 400

Speech Discrimination by Rats

0 100 200 300 400 0 100 200 300 400

Rad Lad

Medium

Time (milliseconds)

Impossible

0 100 200 300 400 0 100 200 300 400

Bad Dadvs.

Medium

Impossibleusing mean rateor Easy using spike timing?

Rat Consonant Discrimination

*********

N=11 rats

N=441 A1 sites, 6 rats Onset Neurograms

10000 15000 20000 25000

m/nr/l

sh/chsh/h

d/td/g

d/bd/s

R2=0.66, P=0.002

Euclidean Distance Between Neurogram Pairs

50 60 70 80 90 100

m/nr/l

sh/h sh/j

sh/fd/t

d/gd/b

R2=0.66, P=0.002

tR= 0.81 P= 0.002

Neural Discrimination Predicts Behavioral Discrimination

Speech Class

SpikeReadout

Inferior Colliculus

Primary Auditory Cortex

Awake Primary Auditory Cortex

Consonants Spike Timing 0.82 0.81 0.82

Mean Rate - - -

Vowels

Temporal Patterns

MeanRate

0.91 - 0.71

10000 15000 20000 25000

m/nr/l

sh/chsh/h

d/td/g

d/bd/s

R2=0.66, P=0.002

50 60 70 80 90 100

m/nr/l

sh/h sh/j

sh/fd/t

d/gd/b

R2=0.66, P=0.002

Correlation Between Neural and Behavioral Discrimination

/ a / / ea / / u / / ee / / oo /

500100 200 300 400 6000 500100 200 300 400 6000 500100 200 300 400 6000 500100 200 300 400 6000 500100 200 300 400 6000

Time (ms)

40 kHz

Sad Sead Sud Seed Sood

Dad Dead Dud Deed Dood

40 kHz

Vowel Spectrograms

a ea u ee oo

10 20 30 40 50 60 70 80 90

100Vowel Discrimination

Distance from CS+in F1-F2 space

Vowel Discrimination

N=14 rats

Speech Class

SpikeReadout

Inferior Colliculus

Mean Rate - - -

Vowels Spike Timing - - -

Mean Rate 0.91 - 0.71

Correlation Between Neural and Behavioral Speech Discrimination

50 60 70 80 90 100

Classifier Performance

mad nad

rad lad

shad sad

shad chad

shad had

shad jad

shad fad

dad tad

dad gad

dadbad

dad sad

R=0.824, P=0.00183

50 60 70 80 90 100

dad dead

sad sud

dad dud

sad sood

sad seed

sad sead

dad dood

dad deed

dad dad low pitch

R=0.915, P=0.000532

50 60 70 80 90 100

mad nad

rad lad

shad sad

shad chad

shad had

shad jad

shad fad

dad tad

dad gad

dadbad

dad sad

R=0.562, P=0.0721

50 60 70 80 90 100

dad dead

sad sud

dad dud

sad sood

sad seed

sad sead

dad dood

dad deed

dad dad low pitch

R=0.562, P=0.115

0 150 300 450 6000

Dad IC Dad A1 Dad A1 AwakeConsonant Spike Timing Consonant Rate Vowel Spike Timing Vowel Rate

Inferior Colliculus

Are the rats learning to categorize speech sounds or simply to associate two sounds

with different responses?

Female

Time (ms)100 300 500 700

0 200 400 600 ms

30 kHz

gGender

DT Day 1 DT Day 9 MT Day 1 MT Day 9-0.5

Pitch Day 1 Pitch Day 9 Gender Day 1 Gender Day 9-0.5

Rats generalize rapidly

Dad vs. TadSingle Speaker

Low vs. High Pitch

Day 1 Day 9 Day 1 Day 9

Dad vs. TadSix Speakers

Male vs. Female

-20 0 20 40 60 80 10050

Background Noise (dB SPL)

D vs. T (n=7)

Female vs. Male (n=2)

-50 0 50 100 150 200 -50 0 50 100 150 200 -50 0 50 100 150 200

Dad Tad

Time (ms) Time (ms)Time (ms)

50 100 150 200 250 300 350 400

100 150 200 250 300 350 400 450 5000

A1 PAF

Conclusions1. Rats are able to accurately categorize many speech sounds.

2. Responses in IC and A1 are highly correlated with behavioral

discrimination and generalization of complex sounds.

3. Consonants appear to be represented by firing patterns.

4. Vowels appear to be represented by mean firing rate.

5. Anesthesia alters sustained, but not transient, A1 responses to

speech sounds.

Acknowledgements:Amanda Puckett - Frequency Discrimination Poster Sunday Morning # 174.16Crystal Engineer - Speech Generalization Poster Sunday Morning # 174.15Claudia Perez - Speech Training and Inferior Colliculus Jai Shetake - Awake A1 Speech PhysiologyRob RennakerVikram JakkamsettiRyan CarrawayHelen Chen

Onset NeurogramsFemale

-50 0 50 100 150 200 250 300 350 400

/Bad/ - site 1

/Bad/ - site 2

/Dad/ - site 1

/Dad/ - site 2

Time (ms)Time (ms)

25 50 75 100 50

tA) Multi-Unit

1 ms bins

25 50 75 100

B) Multi-Unit 40 ms bin

25 50 75 100

C) Single Unit 1 ms bins

25 50 75 100

D) Single Unit 40 ms bin

D/SD/B

Sh/JSh/HSh/Ch

R/LM/N

Euclidean

Distance

City block

Distance

Chebychev

Distance

Mean Single Trial MeanSingle Trial

MeanSingle Trial

Temporal OnsetR2=0.75

P=0.0006

R2=0.66

P=0.002

R2=0.59

P=0.005

R2=0.74

P=0.0007

R2=0.39

P=0.04

R2=0.63

P=0.003

Rate OnsetR2=0.046

R2=0.14

R2=0.08

P=0.41

R2=0.15

P=0.24

R2=0.01

P=0.75

R2=0.14

P=0.25

Stops Fricatives Affricatives Nasals Glides Liquids

Place o

f Articu

lation

0-600 ms0-30 kHz

Kad Gad Shad Had

Bad Fad Vad

Sad Zad

Mad Wad

Chad Jad Nad

Consonant Spectrograms

Natural speech shifted up one octave with the STRAIGHT vocoder (Kawahara, 1997)

*********

N=11 rats

Sensory inputs direct neural plasticity.

Attention regulates plasticity in adults.- Recanzone, Merzenich, Ahissar, Weinberger, Suga, etc.

0 200 400 600 8000

Mean IC PSTH

0 100 200 300 400 500 600Time (msec)

N=187 sites, 6 rats

0 100 200 300 400 500 600

Medium

0 100 200 300 400 500 600Time (msec)

SadN=187 sites, 6 rats

Mean IC PSTH’s

Stops Fricatives Affricatives Nasals Glides Liquids

Place o

f Articu

lation

0-600 ms0-30 kHz

Kad Gad Shad Had

Bad Fad Vad

Sad Zad

Mad Wad

Chad Jad Nad

Consonant Spectrograms

Natural speech shifted up one octave with the STRAIGHT vocoder (Kawahara, 1997)

Spatial Temporal

0 100 200 300 400 0 100 200 300 400

Sad Dad

Medium

Time (milliseconds)

0 100 200 300 400 0 100 200 300 400

Rad Lad

Medium

Time (milliseconds)

Impossible

0 100 200 300 400 0 100 200 300 400

Bad Dadvs.

Medium

Impossibleusing mean rateor Easy using spike timing?

*********

N=11 rats

10000 15000 20000 25000

m/nr/l

sh/chsh/h

d/td/g

d/bd/s

R2=0.66, P=0.002

50 60 70 80 90 100

m/nr/l

sh/h sh/j

sh/fd/t

d/gd/b

R2=0.66, P=0.002

tR= 0.81 P= 0.002

Neural Discrimination Predicts Behavioral Discrimination

Speech Class

SpikeReadout

Inferior Colliculus

Mean Rate - - -

Vowels

Temporal Patterns

MeanRate

0.91 - 0.71

10000 15000 20000 25000

m/nr/l

sh/chsh/h

d/td/g

d/bd/s

R2=0.66, P=0.002

50 60 70 80 90 100

m/nr/l

sh/h sh/j

sh/fd/t

d/gd/b

R2=0.66, P=0.002

Correlation Between Neural and Behavioral Discrimination

/ a / / ea / / u / / ee / / oo /

500100 200 300 400 6000 500100 200 300 400 6000 500100 200 300 400 6000 500100 200 300 400 6000 500100 200 300 400 6000

Time (ms)

40 kHz

Vowel Spectrograms

a ea u ee oo

10 20 30 40 50 60 70 80 90

Distance from CS+in F1-F2 space

Vowel Discrimination

N=14 rats

Speech Class

SpikeReadout

Inferior Colliculus

Mean Rate - - -

Vowels Spike Timing - - -

Mean Rate 0.91 - 0.71

Correlation Between Neural and Behavioral Speech Discrimination

50 60 70 80 90 100

mad nad

rad lad

shad sad

shad chad

shad had

shad jad

shad fad

dad tad

dad gad

dadbad

dad sad

R=0.824, P=0.00183

50 60 70 80 90 100

dad dead

sad sud

dad dud

sad sood

sad seed

sad sead

dad dood

dad deed

dad dad low pitch

R=0.915, P=0.000532

50 60 70 80 90 100

mad nad

rad lad

shad sad

shad chad

shad had

shad jad

shad fad

dad tad

dad gad

dadbad

dad sad

R=0.562, P=0.0721

50 60 70 80 90 100

dad dead

sad sud

dad dud

sad sood

sad seed

sad sead

dad dood

dad deed

dad dad low pitch

R=0.562, P=0.115

0 150 300 450 6000

Dad IC Dad A1 Dad A1 AwakeConsonant Spike Timing Consonant Rate Vowel Spike Timing Vowel Rate

Inferior Colliculus

Neural Discrimination Using Spike Patterns or Rate

Are the rats learning to categorize speech sounds or simply to associate two sounds

with different responses?

Female

Time (ms)100 300 500 700

0 200 400 600 ms

30 kHz

gGender

DT Day 1 DT Day 9 MT Day 1 MT Day 9-0.5

Pitch Day 1 Pitch Day 9 Gender Day 1 Gender Day 9-0.5

Rats generalize rapidly

Dad vs. TadSingle Speaker

Low vs. High Pitch

Dad vs. TadSix Speakers

Male vs. Female

Physiological Consequencesof Speech Training

Speech training could:

1. Increase response to CS+ but not CS-

2. Increase response to CS+ and CS-

3. Increase response to all speech sounds

-50 0 50 100 150 200 250 300 -50

Target Sound: Dad

NaiveDT TrainedMultiple TrainedStd Error

-50 0 50 100 150 200 250 300 -50

Non-Target Sound: Tad

-50 0 50 100 150 200 250 300 -50

Time (msec)

Untrained Sound: Mad

Enhanced response to

TARGET is consistent with

Hypotheses #1, #2, and #3

9.5±0.7 vs. 5.9±0.5 and 5.6±0.2, p<0.001

NON-TARGET is consistent

with Hypotheses #2 and #3

10.1±0.5 vs. 6.4±0.4 and 7.0±0.3, p<0.001

NOVEL is consistent with

Hypothesis #3

7.1±0.7 vs. 5.3±0.4 and 4.9±0.2, p<0.0001

Time (ms)

0 500 1000 15000

Time (ms)

0 500 1000 15000

10000The dog growled at the neighbors - Original

The dog growled at the neighbors – Four Channels

Neural Correlates of Discriminationof Stimuli on a Continuum

MTBritten, Shadlen, Newsome, & Movshon (1992)

A1Walker, Ahmed, & Schnupp (2007)

S1Luna, Hernandez, Brody & Romo (1994)

Correlation is determined by the: 1) Neurons Included 2) Temporal Precision Allowed

Frequency-Specific Map PlasticityLasts >20 Days

N = 16 rats; 880 A1 sites

2 4 8 16 32

Tone Frequency (kHz)

Percent of A1 Responding

Difference 1 day after 19kHz + NB stim

Difference 20 days after 19kHz + NB stim

Difference 100 days after 19kHz + NB stim20

Onset NeurogramsFemale

F1 F2 F3 M1 M2 M3

102030405060708090

100Gender Last 2 Days

Female or Male Speaker

F1 F2 F3 M1 M2 M3

102030405060708090

100DT Multi Last 2 Days

Female or Male Speaker

N=10 rats N=11 rats

Gender

0 100 200 300 4000

100R2=0.9, P=2.2708e-006

Peak Rate

FD1FD2FD3

MD1MD2 MD3

MT2MT3

Peak Firing Rate16-32 kHz sites

Peak Firing Rate1-2 kHz sites

Voicing

0 100 200 300 400 500 6000

High, Medium, and Low Responses to Dad

IC A1 Awake A1

Time (msec)

Plasticitya) Developmentalb) Also works awake but must direction attentional mechanisms

1) Clinical picture – neither preventable or curablea) We have the tools but do not know how to use them.

2) EE – alters anatomy and neurochemistry and aids rehab but the connection between the two is difficult to identify.a) Physiology

I) AnesthetizedII) AwakeIII) In vitro

b) But not very targeted3) NB

a) Arousal and attention activates ACh releaseb) Stimulating NB paired with different sounds alters subcortical, primary and non-primary auditory cortex in a long-lasting and specific manner

I) MapsII) Selectivity III) SensitivityIV) Temporal processingV) SequencesVI) Synchrony

c) What is the relation to behavior? What is plasticity good for?4) NB-improves behavior, just like developmental plasticity does.

a) But patients don’t want deep brain stimulating electrodes5) Pharmacology to open the critical period

a) Rolipramb) May be useful for map based disorders, but lacks temporal specificityc) Aberrant temporal processing has been observed in many (perhaps most) neurological and psychiatric conditions.

• Speech processing • Explain hypothesis multiple articulatory features different more accurate

No hypothesis for single articulatory featureSuggest that sounds which evoke similar neural representations will be confusable

8) How to quantify neural similarity? Rate vs. Temporal codeIC

Vowels = Rate Consonants = Temporal

A1Consonants = Temporal

Awake A1Vowels = Rate (Show Rennaker RRTFs)Consonants = Temporal

Are these patterns learned categorically or memorized?compression, DT speaker, and gender generalization

Show neural correlates 9) Plasticity

Time (ms)100 300 500 700

0 200 400 600 ms

30 kHz

100 90 80 70 60 50 40 30 20 10%

100 90 80 70 60 50 40 30 20 10

102030405060708090

100DT Compression Last 2 Days

Percent of Original Stimulus Length

DadTad

DT compression correlation between behavior and peak rate (CF between 1 & 2 kHz)

0 200 400 6000

100R2=0.68, P=8.6481e-006

Peak Rate

itD100D90 D80 D70D60D50

D40D30 D20

T100T90

T80T70

T50 T40T30

T20T10

Awake vs. Anesthetized A1 responses

-100 0 100 200 300 400 5000

100 sad

Time (ms)

-100 0 100 200 300 400 5000

100 dad

Time (ms)

AwakeAnesthetized

Awake vs. Anesthetized A1 responses

-100 0 100 200 300 400 5000

50100150200250300

100150200250300

Time (ms)

A1 AwakeA1 AnesthetizedIC Anesthetized

“The isomorphism should be sought --- not in the first-order relation Between (a) an individual object, and (b) its internal representation --- but in the second-order relation between (a) the relations among alternative objects, and (b) the relations among their internal representations.

Thus, although the internal representation for a square need not itself be square, it should (whatever it is) at least have a closer functional relation to the internal representation for a rectangle than to that, say, for a green flash or the taste of a persimmon.” - Shepard and Chipman (1970)

Representation and Generalization

DT Multi correlation between behavior and Euclidean distance (all CFs)

1 1.2 1.4 1.6 1.8 2

100R2=0.31, P=0.061044

Euclidean Distance

DF2DF3

DM1DM2

TF2TF3

500 1000 1500 2000 2500 30003000

DadDead

SadSead

First Peak

a ea u ee oo

10 20 30 40 50 60 70 80 90

Inferior Colliculus Codes Speech Sounds Using Both

Spike Rate and Timing

Inferior Colliculus Codes Speech Sounds using Both Spike Rate and TimingIC uses temporal code for consonants and rate code for vowels

Simultaneous use of Temporal and Rate Code for Speech in Inferior ColliculusTemporal and Rate Coding of Speech Sounds in Inferior Colliculus

bad fad vad mad wad

1.53.26.47.39.9

11.314.115.718.222.6

dad sad zad chad jad nad rad lad

1.53.26.47.39.9

11.314.115.718.222.6

Time (ms)0 200

Time (ms)

Group A: Started with /DVD/ then moved into /SVD/

Group B: Started with /SVD/ then moved into /DVD/

1)Learning occurs over first 10 days (n=8). h=1/p=0.0035

Performance on day 1 is not above chance. h=0/p=0.0644

2) Learning occurs over second 10 days(n=8). h=1/p=0.0021

Performance on day 11 is above chance (though significantly above chance). h=1/p=0.0024

3)Learning does not occur over last (20) days of training. (n=8) h=0/p=0.0863

Group A

Group B

Behavior

/D/ /S/ /Noise/

a ea u ee oo

10 20 30 40 50 60 70 80 90

a ea u ee oo

10 20 30 40 50 60 70 80 90

a ea u ee oo

10 20 30 40 50 60 70 80 90

500 750 1000 1250 1500 1750 2000 2250 25003000

DadDead

SadSead

First Peak

kCut consonant /200 msec vowel

Performance correlated with Peaks

0 0.5 1 1.5 250

Need Nood

Deed Dood

Distance from CS+

R=0.855, P=0.00682

NVddVdsVd

0 0.5 1 1.5 250

Need Nood

Deed Dood Sead

Seed Sood

Distance from CS+

R=0.765, P=0.00377

NVddVdsVd

500 1000 1500 2000 2500 30003000

DadDead

SadSead

First Peak

kPerformance correlated with Formants

Cut consonant /200 msec vowel

NEW SLIDE

Performance correlated with Formants

ADD PITCH

NEW SLIDE

MU:Vowel 300 ms

Rate Class

Temporal Class

50 60 70 80 90 100

dad dead

sad sud

dad dud

sad sood

sad seed

sad sead

dad dood

dad deed

dad dad low pitch

R=0.915, P=0.000532

50 60 70 80 90 100

dad dead

sad sud

dad dud

sad sood

sad seed

sad sead

dad dood

dad deed

dad dad low pitch

R=0.562, P=0.115

0 10 20 30 40 50

Mean Difference in Spike Rate (Hz)

dad dead

sad sud

dad dud

sad sood

sad seed

sad sead

dad dood

dad deed

dad dad low pitch

R=0.913, P=0.000594

NEW SLIDE

MU/Consonant 50ms

Rate Class

Temporal Class

50 60 70 80 90 100

mad nad

rad lad

shad sad

shad chad

shad had

shad jad

shad fad

dad tad

dad gad

dadbad

dad sad

R=0.562, P=0.0721

50 60 70 80 90 100

mad nad

rad lad

shad sad

shad chad

shad had

shad jad

shad fad

dad tad

dad gad

dadbad

dad sad

R=0.824, P=0.00183

0 50 100 150

Mean Difference in Spike Rate (Hz)

mad nad

rad lad

shad sad

shad chad

shad had

shad jad

shad fad

dad tad

dad gad

dadbad

dad sad

R=0.327, P=0.326

NEW SLIDE

Figure: CF influences classifier performance based on consonants Temporal:50msec

based on vowel rate:300msec

NEW SLIDE

MU/Consonant 300ms

Rate Class

Temporal Class

Not ShownNEW SLIDE

SU:Vowel 300 msRate

Rate Class

Temporal Class

Not ShownNEW SLIDE

SU Consonant 50 ms

Rate Class

Temporal ClassNot Shown

NEW SLIDE

SU Consonant 300 ms

Rate Class

Temporal Class

Not ShownNEW SLIDE

1 2 4 8 16 32-35

Frequency (kHz)

sudsood

1 2 4 8 16 32-35

Frequency (kHz)

duddood

Powerspectrum of vowel only

200msec

Supplemental

of Articu

lation

LiquidsGlidesNasalsAffricatesFricativesStops

of Articu

lation

LiquidsGlidesNasalsAffricatesFricativesStops

Time (ms)100 300 500 700

Kad Gad Shad Had

Bad Fad Vad

Sad Zad

Mad Wad

Chad Jad Nad

0 200 400 600 ms

30 kHz

Figure 1

?Spatial SpatiotemporalTemporal

Spatial

n r l y w n m j ch h z v sh s f g d b k t p

p t k b d g f s

sh v z h

m n w y l r

p t k b d g f s

sh v z h

m n w y l r

Figure 6

A) OnsetSpike Timing

B) OnsetMean Rate

C) DurationSpike Timing

D) DurationMean Rate

Figure 7

Onset1 ms bins

Onset40 ms bin

Duration1 ms bins

Duration700 ms bin

SixteenMulti-Units

OneMulti-Unit

OneSingleUnit

SixteenSingleUnits

SixteenMulti-Units

OneMulti-Unit

SixteenSingleUnits

OneSingleUnit

Onset 1 ms bins

Onset 40 ms bin

Duration 1 ms bins

Duration 700 ms bin

Onset 1 ms bins

Onset 40 ms bin

Duration 1 ms bins

Duration 700 ms bin

Onset 1 ms bins

Onset 40 ms bin

Duration 1 ms bins

Duration 700 ms bin

Onset 1 ms bins

Onset 40 ms bin

Duration 1 ms bins

Duration 700 ms bin

AWAKE A1

40 awake A1 Vowels – Rate

50 60 70 80 90 100

dad dead

sad sud

dad dud

sad sood

sad seed

sad sead

dad dood

dad deed

R=0.713, P=0.0471

40 awake A1 Vowels – temporal

50 60 70 80 90 100

dad dead

sad sud

dad dud

sad sood

sad seed

sad sead

dad dood

dad deed

R=0.0336, P=0.937

40 awake A1 Consonants – Temporal

50 60 70 80 90 100

mad nad

rad lad

dad dad low pitch

shad sad

shad chad

shad had

shad jad

shad fad

dad tad

dad gad

dadbad

R=0.818, P=0.0021

40 awake A1 Consonants – Rate

50 60 70 80 90 100

mad nad

rad lad

dad dad low pitch

shad sad

shad chad

shad had

shad jad

shad fad

dad tad

dad gad

dadbad

R=0.404, P=0.217

Figure 3

A) Spike Timing

B) Mean Rate

p t k b d g f s

sh v z h

m n w y l r

Neural Correlates of Frequency Discrimination

0 0.2 0.4 0.6 0.8 1 1.2 1.440

Octaves from CS+

LateEarly

1 site

2 sites

5 sites

10 sites30 sites T

nNumber Of Sites

Behavioral Threshold

Sunday Morning Poster #174.16

Peter Heil - Leibniz Institute, Magdeburg

Towards a unifying basis of absolute auditory thresholds

Lutz Wiegrebe – University of Munich

Psychophysical and physiological evidence for fast binaural processing

Tony Zador - Cold Spring Harbor Laboratory

Millisecond spike timing can guide behavior in auditory cortex

Kerry Walker – University of Oxford

A spike pattern based neurometric analysis for the discrimination of natural sounds

Kamal Sen - Boston University

Discrimination of Complex Natural Sounds in Songbirds: Neurons & Behavior

Mike Kilgard - University of Texas

Cortical Activity Patterns Predict Speech Discrimination Ability

Jennifer Bizley – University of Oxford

The Neural Basis of Pitch Perception

Shihab Shamma - University of Maryland

Encoding task rules and performance in auditory and frontal cortex of the ferret

Yale Cohen – Dartmouth University

Auditory attention and auditory categorization in primate ventrolateral prefrontal cortex

Inferior Colliculus

Speech Class

SpikeReadout

MUN=187

SUN=12

MUN=441

AwakeN=40

SUN=16

Consonants

Temporal Patterns

0.82 0.63 0.81 0.82 -

MeanRate

- - - - -

Vowels

Temporal Patterns

- - - - -

MeanRate

0.91 0.74 - 0.71 -

Neural Correlates of Consonant and Vowel Discrimination

0 100 200 300 400 0 100 200 300 4000 100 200 300 400 0 100 200 300 400

Rad Lad ??or

Impossible

Effect of synchronous vs. non-synchronous recordings

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Transcript of Effect of synchronous vs. non-synchronous recordings

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