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NPB163-L9

4 min read

📗 -> 04/28/25: NPB163-L9

End of Slideshow 1
Slideshow 2

🎤 Vocab

❗ Unit and Larger Context

Spent time learning about visual system, now we move onto audition!

Refresher

vision:

  • Light enters retina
  • Photoreceptors there
  • Maps to thalamus
  • Then to primary visual cortex
    We’ve noticed the mapping, the sensory epithelium
  • Retinotopic map

✒️ -> Scratch Notes

Slideshow 1

Audition

Step 1: Transducing sound in the inner ear
… Review
Traveling waves along the basilar membrane primarily excite hair cells at a frequency-specific location:

  • Low frequency closer to the tip
  • High frequency closer to the oval window
  • Mixture of frequencies wil excite along multiple parts of the membrane

Cochlear hair cells are tuned to a frequency:

  • At their preferred frequency, you need a much lower magnitude of sound to excite them
  • Selective place coding

Transmission steps:

  • Ear

    • The outer ear alters the effective sound spectrum depending on sound location. This allows for vertical perception of sound later on

  • cochlear nuclei

  • Superior olivary nuclei

    • First step to combine ears
    • Delay between time in both ears (inter-aural time difference, ITD) is used for horizontal position here
    • The ITD is calculated by coincidence detectors, where the place where sound from each ear arrives together is their ITD, “Delay Lines”

  • Inferior colliculus

  • Thalamus

  • Auditory cortex

    • Difference in loudness (inter-aural intensitiy/level/loudness, ILD) is used here.

Primate Auditory Cortex

We see a similar division of processing as visual system (The what/where division in ventral/dorsal processing streams)

  • The “where” pathways meet in parietal lobe, PPC (posterior parietal cortex)
  • The “what” pathways meet in temporal lobe

Primate Auditory cortex shows Tonotopic Map

  • Auditory fields organized according to tonotopy (frequencies of sounds they respond to)

Cortical Responses to Sound Stimuli

Similar to noise variability in visual cortex, we see large variability in spiking in auditory system. Similar to a Poisson distribution.

Slideshow 2

Sensory Systems – Feedback, Predictive Coding, Anticipating sensory consequences of own actions

Predictive Coding

Traditionally, hierachical processing is thought as feed-forward


  • However, in reality they are bideractional

What gives? What is transmitted backwards? What does this do for sensory signals?
This gives rise to Predictive Coding

Each higher level is continuosly making prediction, and using the signals it receives from prior levels as a feedforward error signal. It then sends back predictions as feedback

  • Forwarding: Not the representation of the lower level explicitly but only in what way this representation deviates from the predicton provided by the higher level
  • Feedback: Used to send a prediction of expected features to the lower level based on the current representation at the higher level
    In a hierarchical context:
  • Lower level explanations explain what they can, and if they can’t figure it out send the signals to a higher level that might be able to piece the clues together.
    This is a continuous flow of information, forward signals and feedback are continuously updated

Discussion

Convergence:

  • Multiple types:
    • Convergence over space: Allows for processing over a spread out area, however loses spatial resolution
    • Convergence over time: Maintains spatial resolution, but loses temporal resolution (needs to be averaged first)
    • Could potentially combine through different processing pathways

Parallel processing:

  • Examples
    • The obvious example is what/where ventral/dorsal pathways.
    • Columns in V1
    • ITD/ILD
    • Broadband/color specific sensing
  • Benefits
    • Minimizes energy
    • Allows to combine benefits/minimize weaknesses of different approaches
    • Specializations

Disparity:

  • Zero-disparity:
    • Map the left eye zero to right eye zero. Coincidence detector
  • Particular Positive or Negative Disparity:
    • Coincidence detector with an offset. (-2, 0), (-1, 1), (0, 2), etc.
  • Near tuned or far tuned neuron
    • Higher level neuron that takes input from sets of disparity tuned neurons, and fires when any of their selective group fires (fires when the +1 disparity, or the +2 or +3 fires)
    • Build something simple, and combine together to build something more complex
    • Not the only way to do it, but one way

🧪 -> Refresh the Info

Did you generally find the overall content understandable or compelling or relevant or not, and why, or which aspects of the reading were most novel or challenging for you and which aspects were most familiar or straightforward?)

Did a specific aspect of the reading raise questions for you or relate to other ideas and findings you’ve encountered, or are there other related issues you wish had been covered?)

🔗 -> Links

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