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Models of Neural Encoding in the Auditory System. Robert Turetsky [email protected] LabROSA. Raul Rodriguez-Esteban [email protected] Comet Lab (we hope). Time Encoding – Prof. Lazar, Spring 2003. Talk Overview. The problem of hearing Physiology: Transduction in the ear

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models of neural encoding in the auditory system

Models of Neural Encoding in the Auditory System

Robert Turetsky

[email protected]

LabROSA

Raul Rodriguez-Esteban

[email protected]

Comet Lab (we hope)

Time Encoding – Prof. Lazar, Spring 2003

talk overview
Talk Overview
  • The problem of hearing
  • Physiology: Transduction in the ear
  • Biological Signal Processing
    • Sound Profile Extraction
    • Pitch Detection
    • Spatial Localization
  • Conclusion
audition the fifth sense
Audition: The fifth sense
  • Hearing was the last of our senses to evolve
  • Detects pressure changes in air: motion and contact
  • We can hear things that we can’t see:
    • Movement of large objects at great distance
    • Motion of objects when vision is occluded, darkness
  • Communication (e.g. speech, music)
analogy bregman s lake
Analogy: Bregman’s Lake

“Imagine two narrow channels dug up from the edge of a lake, with handkerchiefs stretched across each one. Looking only at the motion of the handkerchiefs, you are to answer questions such as: How many boats are there on the lake and where are they?” (after Bregman’90)

talk overview5
Talk Overview
  • The problem of hearing
  • Physiology: Transduction in the ear
  • Biological Signal Processing
  • Conclusion
dataflow perception to cognition
Dataflow: Perception to Cognition

Outer ear: Pressure waves collected

Middle ear: Pressure -> Mechanical Energy -> Hydraulic Energy

Inner ear: Hydraulic Energy -> Neural Impulses

Midbrain: Feature extraction

Cortex: ???

the cochlea as transducer
The Cochlea as Transducer

 Thickness in BM   resonant frequency along length

Spatial Coding along BM

Vibrating BM trigger hair cells  neuron fires ‘near’ peak at resonant frequency

Temporal Coding (<= 4kHz)

Firing is probabilistic – may fire at peak or somewhere near it.

~3500 hair cells in humans.

the cochlea as filterbank
The Cochlea as Filterbank
  • Groups of neurons are connected to hair cells which respond to different frequencies/areas of BM
  • Approximately log-scale responses made up of 40-150 neurons per band (Fleisher)
  • Evidence of Fourier-like analysis in inner ear -> filtering
auditory nerve where to next
Auditory Nerve: Where to next?
  • Sounds must be processed: grouping, identification, localization
  • Hard/impossible to probe living auditory cortex non-invasively
  • Mathematical models of function (must be biologically plausible)
mathematical models of neural processing in the auditory system
Mathematical Models of Neural Processing in the Auditory System
  • The problem of hearing
  • Physiology: Transduction in the ear
  • Biological Signal Processing
    • Sound Profile Extraction
    • Pitch Detection
    • Spatial Localization
  • Conclusion
early audition block diagram
Early Audition: Block Diagram

Sound Profile Extraction

Timbre

Spatial Localization

Loc.

(to intermediate auditory system)

Cochlea (Filterbank)

Auditory Nerve

Frequency Periodicity

Pitch

Temporal Periodicity

Meter

model of the cochlea freq response
Model of the Cochlea: freq. response
  • Usually a filterbank (Log, Mel scales)
  • Filterbank model does not capture all of the information:

Onset detection

Intensity?

model of peak detector in cochlea17
Model of Peak Detector in Cochlea

Time Encoding

High pass filtering

model of peak detector in cochlea18
Model of Peak Detector in Cochlea

sum of tones

π/2 delay

π delay

coincidence detection
Coincidence Detection

mechanism

timing comparative

coincidence detection20
Coincidence Detection

avians

mammals

mathematical models of neural processing in the auditory system23
Mathematical Models of Neural Processing in the Auditory System
  • The problem of hearing
  • Physiology: Transduction in the ear
  • Biological Signal Processing
    • Sound Profile Extraction
    • Pitch Detection
    • Spatial Localization
  • Conclusion
the problem of pitch perception
The problem of pitch perception
  • Three types of pitch percepts:
    • Spectral: Pitch evoked from sinusoidal signals
    • Periodicity (incl. missing fundamental): Low order, spectrally resolved harmonic tone complexes (e.g. notes from musical instruments)
    • Residue: Assuming a pitch from unresolved high order harmonics (aka virtual pitch)

Log spectrum of pitched sound

Resolving multiple voices?

theories of periodic pitch perception
Theories of Periodic Pitch Perception
  • Place Theory (Helmholtz): Pure tone vibrates specific area of BM.
    • Problem: Complex tone w/missing fundamental does not induce vibration at fundamental (Licklider’s experiments)
  • Spectral pattern recognition: Response from cochlear filterbank compared against templates of all possible fundamentals
    • Problem: Where are the templates stored?
      • Not learned (infants have sense of pitch)
    • Does not account for residue, missing fundamental
theories of periodic pitch perception26
Theories of Periodic Pitch Perception
  • Temporal (e.g. Meddis and Hewitt): each channel is processed independently and then summed together, e.g. summary autocorrelation
    • Problem: No physiological evidence for mechanism
  • Dual Model: Spatio-temporal makes use of BM location and frequency of AN firing
  • THE PLAN: Improve the spatial aspect of the model
    • Develop a biologically plausible algorithm that can generate pitch templates
    • Account for residue, missing fundamental
the shamma and klein model
The Shamma and Klein model
  • Spectral sharpening: Lateral Inhibition
  • Temporal Sharpening: Enhance synchrony b/w channels
  • Coincidence Matrix: compare responses from all channels across the array
shamma system block diagram
Shamma: System Block Diagram

h(n;1)

.

h(n;2)

s(t)

.

.

h(n;3)

  

  

.

.

.

h(n;X)

filter bank

hair cells

spectral sharpening

temporal sharpening

coincidence detection

+

signal

shamma lateral inhibition
Shamma: Lateral Inhibition
  • Equivalent to simple/complex cells in the visual system
  • Goal is to enhance salient frequency peaks
  • Can be modeled as difference in frequency
  • Probably integral to ASA (Auditory Scene Analysis)
coincidence matrix creating harmonic templates
Coincidence Matrix: Creating harmonic templates

After LIN

After temporal enhancement

Coincidence Matrix

Templates created regardless of input (e.g. harmonics, noise, click track)

what s next scene analysis
What’s next: Scene Analysis
  • Model of visual system (Sajda 1995) shows that retinotopically patterned LINs can detect Gestalt rules like good continuity
  • Maybe the same exists in the AN?
mathematical models of neural processing in the auditory system32
Mathematical Models of Neural Processing in the Auditory System
  • The problem of hearing
  • Physiology: Transduction in the ear
  • Biological Signal Processing
    • Sound Profile Extraction
    • Pitch Detection
    • Spatial Localization
  • Conclusion
spectro temporal response
Spectro-Temporal Response
  • The most important physical correlate of timbre
  • Superposition principle
spectro temporal response34
Spectro-Temporal Response

- MGB: rate selectivity units

selected references
Selected References
  • A. S. Bregman. Auditory Scene Analysis. MIT Press 1990.
  • D. P. W. Ellis. Lecture notes from Speech and Audio Processing and Recognition 2002.
  • B. Gold, N. Morgan. Speeh and Audio Signal Processing 2000.
  • D. Oertel et al. Detection of synchrony in the activity of auditory nerve fibers by octopus cells of the mammalian cochlear nucleus 2000.
  • S. Shamma, D. Klein. The Case of the Missing Pitch Templates: How Harmonic Templates emerge in the early auditory system 1999.
  • S. Shamma. On the role of space and time in auditory processing 2001.
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