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Computation Neural Model

1. Neural Circuits

What they want to do?

Learn some algorithm from our brain and translate them into computer code

Some feature of brain-based computations

  • work for many decades
  • parallel computation
  • interaction

How to study brain at different scales

  • Large to small: EEG\MEG\PET\Patch Damp
  • A central question in neuroscience: what’s the right level of abstraction

Bottom-Up method, first study single neurons. Some model

  • filter operation
  • integrate-and-fire circuit (easy to implement)
  • Hodgkin-Huxley unit
  • Multi-compartmental models
  • Spines, channels

Circuits

image-20200406153827627

Something like this

Visual system shows an approximately hierarchical architecture.

  • V1 part is important
  • Timing of the response, latency 50~60ms
  • What pathway and Where pathway

First order approximation: immediate recognition

  • behavior, we recognize objects within ~150ms
  • physiology, visually selective responses to complex shapes within ~150ms
  • computation, bottom-up computational models (maybe inspire deep learning)

2. Feedback Signal

2.1. Basic Mechanisms in V1

Feedback signal enhance surround suppression

signals from higher part

visual cortex orientation tuning, there is a Gabor function can describe the system

There are so many models can explain the situation

  • feed-forward model
  • feed-back? (by cooling v2/v3 cortex) NO

Tuned feedback signals can instantiate visual search and feature-based attention

to see particular face/color

go through some linear and non-linear computation

  1. filter 4 orientation
  2. local max
  3. filter
  4. filter
  5. comparison

3. Questions

Reasons for optimism

  • Wiring diagram
  • Strength in numbers
  • Source code