Communicationand Cortex

The computationalneuroethology of mouse vocalizations

Robert LiuSloan-Swartz Center for Theoretical Neurobiology

University of California at San Francisco

Basic questions in neural coding

• How does the brain process behaviorally-relevant stimuli?

• Is the structure of natural stimuli “efficiently” represented by neurons?

Computational neuroethology

• Study organism in natural contexts (etho-) Look for strong stimulus-behavior links What are the properties of the stimulus?

• Determine relevant neural areas (neuro-) How do neurons represent stimulus properties?

• Use behavior to constrain neural codes• Study coding algorithms (compu-)

Use info theory to probe efficiency of neural codes

Auditory processing in mice

• Obvious behavioral context: communication Vocalizations are natural input to auditory

system Behavioral response provides an observable

output

Auditory processing in mice

• Obvious behavioral context: communication Vocalizations are natural input to auditory

system Behavioral response provides an observable

output

• Why the mouse? Opportunities to employ genetic techniques Extensive research on peripheral and non-

cortical central auditory system Rich ultrasound communication behaviors

• Pup isolation calls maternal retrieval

Mouse pup ultrasounds

Time (ms)0 200 400 600

25

10025

10025

100

Fre

quen

cy (

kHz)

QuickTime™ and a Motion JPEG B decompressor are needed to see this picture.

• Spectral domain Categorical perception of bandwidth-limited

ultrasound noise as pup-like (Ehret & Haack, 1982)

Categorical perception of pup calls

Time (ms)0 40 80 120

30

90

Fre

quen

cy (

kHz)

60

Noise model

• Spectral domain Categorical perception of bandwidth-limited

ultrasound noise as pup-like (Ehret & Haack, 1982)

Categorical perception of pup calls

Time (ms)0 40 80 120

30

90

Fre

quen

cy (

kHz)

60

BW (kHz)22.5

Res

pons

eNoise modelPup-like

Adult mouse encounter calls

• Ultrasounds when males encounter females

Time (ms)0 200 400 600

25

10025

10025

100

Fre

quen

cy (

kHz)

QuickTime™ and a Motion JPEG A decompressor are needed to see this picture.

Computational neuroethology

• Study organism in natural contexts (etho-) Look for strong stimulus-behavior links What are the properties of the stimulus?

• Determine relevant neural areas (neuro-) How do neurons represent stimulus properties?

• Use behavior to constrain neural codes• Study coding algorithms (compu-)

Use info theory to probe efficiency of neural codes

• What frequencies make up a call?

One frequency extractedas a function of time

Frequency content of natural calls

Time (ms)

0 40 80

Number of 1 ms bins

0 20 4025

100

Fre

quen

cy (

kHz)

50

75

Spectrogram Histogram

25

100

Fre

quen

cy (

kHz)

50

75

Whistle-likesimplicity

Typical frequency (kHz)40 60 80 100

50

0

Dur

atio

n (

ms)

150

100

Pup call frequencies and durations

• Frequency and duration clusters Main: 67 kHz/59

ms Aux: 93 kHz/30 ms

Typical frequency (kHz)40 60 80 100

50

0

Dur

atio

n (

ms)

150

100

Pup call frequencies and durations

• Frequency and duration clusters Main: 67 kHz/59

ms Aux: 93 kHz/30 ms

• Main cluster <22.5 kHz bandwidth for categorization Natural distribution

contributes to category formation?

Natural acoustic categories

Typical frequency (kHz)40 60 80 100

50

0

Dur

atio

n (

ms)

150

100

• Adt: 80 kHz/23 ms• Pup and adult calls

clearly separate ROC: 91% correct

• Adult call category to be distinguished from pup calls?

• Perhaps other cues also necessary to categorize

Call repetition periods

• Periods between call onsets different

0 100 200 300 400 50025

600

100

Time (ms)

Fre

q (k

Hz)

25

100

Pup

Adt

Call repetition periods

• Periods between call onsets different

• Adult calls repeat more quickly than pup calls 100 ms vs. 180 ms ROC: 97% correct

(frequency, duration, and period)

Repetition period (ms)0 100 200 300 400 500

5

0

Pro

babi

lity

(1/s

)

10

0 100 200 300 400 50025

600

100

Time (ms)

Fre

q (k

Hz)

25

100

Pup

Adt

Conclusions

• Study organism in natural contexts (etho-) What are the properties of the natural calls?

• Spectral and temporal clustering of pup and adult calls

• Determine relevant neural areas (neuro-) How do neurons represent vocalization

properties?• Stimulus-locked neural oscillations reflect pup call

periods

• Use behavior to constrain neural codes The peak spike count in auditory cortex may

support a categorical distinction

Collaborators

Jennifer Linden

Michael MerzenichKenneth Miller

Christoph Schreiner

Mentors

Electrophysiology

• Experiments on recent CBA/CaJ mothers Ketamine and medetomidine anesthesia Multiunit activity recorded via tungsten

electrodes inserted 400-600 microns below the surface

Targeted areas with ultrasound responses Two free field speakers (low frequency range

from 3 kHz to 40 kHz; high frequency range from 20 kHz to 100 kHz)

TDT System II equipment used to play out stimuli and record responses