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Communication and Cortex

Communication and Cortex. The computational neuroethology of mouse vocalizations. Robert Liu Sloan-Swartz Center for Theoretical Neurobiology University of California at San Francisco. Basic questions in neural coding. How does the brain process behaviorally-relevant stimuli?

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Communication and Cortex

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  1. Communicationand Cortex The computational neuroethology of mouse vocalizations Robert Liu Sloan-Swartz Center for Theoretical Neurobiology University of California at San Francisco

  2. Basic questions in neural coding • How does the brain process behaviorally-relevant stimuli? • Is the structure of natural stimuli “efficiently” represented by neurons?

  3. 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

  4. Auditory processing in mice • Obvious behavioral context: communication • Vocalizations are natural input to auditory system • Behavioral response provides an observable output

  5. 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

  6. 100 25 100 Frequency (kHz) 25 100 25 200 400 600 0 Time (ms) Mouse pup ultrasounds • Pup isolation calls maternal retrieval

  7. Categorical perception of pup calls • Spectral domain • Categorical perception of bandwidth-limited ultrasound noise as pup-like (Ehret & Haack, 1982) 90 Noise model Frequency (kHz) 60 30 40 80 120 0 Time (ms)

  8. Categorical perception of pup calls • Spectral domain • Categorical perception of bandwidth-limited ultrasound noise as pup-like (Ehret & Haack, 1982) Pup-like 90 Noise model Frequency (kHz) 60 Response 30 40 80 120 22.5 0 Time (ms) BW (kHz)

  9. Adult mouse encounter calls • Ultrasounds when males encounter females 100 25 100 Frequency (kHz) 25 100 25 200 400 600 0 Time (ms)

  10. 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

  11. Frequency content of natural calls • What frequencies make up a call? Whistle-like simplicity One frequency extracted as a function of time Spectrogram Histogram 100 100 75 75 Frequency (kHz) Frequency (kHz) 50 50 25 25 0 40 80 0 20 40 Time (ms) Number of 1 ms bins

  12. Pup call frequencies and durations • Frequency and duration clusters • Main: 67 kHz/59 ms • Aux: 93 kHz/30 ms 150 100 Duration (ms) 50 0 40 60 80 100 Typical frequency (kHz)

  13. 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? 150 100 Duration (ms) 50 0 40 60 80 100 Typical frequency (kHz)

  14. Natural acoustic categories • 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 150 100 Duration (ms) 50 0 40 60 80 100 Typical frequency (kHz)

  15. Call repetition periods 100 • Periods between call onsets different Pup 25 Freq (kHz) 100 Adt 25 0 100 200 300 400 500 600 Time (ms)

  16. Call repetition periods 100 • 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) Pup 25 Freq (kHz) 100 Adt 25 0 100 200 300 400 500 600 Time (ms) 10 Probability (1/s) 5 0 0 100 200 300 400 500 Repetition period (ms)

  17. 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

  18. Collaborators Jennifer Linden Michael Merzenich Kenneth Miller Christoph Schreiner Mentors

  19. 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

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