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Day 2 — Sun, Mar 22

What mechanisms power each visual stream?

  • Book to read: "Principles of Neural Science" — covers intermediate & high-level visual processing in detail

Dorsal Stream — "Where / How"

Retina
V1
V2 / V3
MT / V5motion energy
MSToptical flow
PPCpredictive coding
Motor cortex Frontal eye field efference copy

Ventral Stream — "What"

Retina
LGN
V1fine detail, colour, high freq
V2contours, figure-ground, illusionary edges
V4shapes, colour constancy, curves
IT (posterior)complex shapes
Anterior ITrecognition
Amygdala Hippocampus emotional relevance & contextual memory

Dorsal Stream — "Where / How"

Works with M-cells (fast, transient, change-detecting). Operates in real-time with no memory.

  • Path: Retina → V1 → V2/V3 → MT/V5 → MST → PPC → Motor cortex & Frontal eye field
  • Mechanism 1 — Motion Energy MT / V5
    MT neurons don't see objects — they only perceive motion vectors, a bank of directional filters tracking movement
  • Mechanism 2 — Optical Flow MST
    Computes self-motion — how fast is the visual field changing around you?
  • Mechanism 3 — Predictive Coding PPC
    Doesn't track the object itself, only the prediction error — where the object should be vs where it actually is
  • Mechanism 4 — Efference Copy Motor / FEF
    The cancellation trick — brain subtracts self-generated motion so the world stays stable despite 3 eye movements per second

Ventral Stream — "What"

Works with P-cells (slower but persistent, sustained high-quality signals unlike M-cells which only respond to changes).

  • Path: Retina → LGN → V1 → V2 → V4 → IT (posterior) → Anterior IT → Amygdala & Hippocampus
  • V2 isn't passively observing reality — it's actively constructing it. "Perception is always a controlled hallucination"
  • V4 computes colour relative to surroundings, not absolute — this is colour constancy
  • After Anterior IT, signal splits: Amygdala asks "does what I'm seeing matter to me?" (emotional relevance), Hippocampus stores the context — when, where, with whom
  • Interesting: only the ventral stream actually "sees" the object
  • Mechanism 1 — Hierarchical Assembly V1 → IT
    Like a CNN — V1 detects edges, V2 contours, V4 curves, IT full objects. Each level combines neurons from the previous one
  • Mechanism 2 — Invariance V4 / IT
    Objects remain recognised despite changes in size, position, rotation (translational & rotational invariance)
  • Mechanism 3 — Population Coding IT
    Similar to probabilistic neural networks — recognition outputs a probability distribution, not a binary answer
  • Mechanism 4 — Top-down Prediction IT → V4
    More fibers run downward than upward — like backpropagation. IT tells V4 "I think this is a face, expect contours here." Makes recognition much faster via probabilistic priors