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Krubitzer & Kaas

S1. V1. A1. S2. V2. MT. Krubitzer & Kaas. 30 μ m. pia. white matter. Somato- Mean of Motor sensory Frontal Temporal Parietal Visual means Mouse 109.2 ± 6.7 111.9 ±6.9 110.8 ±7.1 110.5 ±6.5 104.7 ±7.2 112.2 ±6.0 109.9 ±6.8

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Krubitzer & Kaas

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  1. S1 V1 A1 S2 V2 MT Krubitzer & Kaas

  2. 30 μm pia white matter Somato- Mean of Motor sensory Frontal Temporal Parietal Visual means Mouse 109.2 ± 6.7 111.9 ±6.9 110.8 ±7.1 110.5 ±6.5 104.7 ±7.2 112.2 ±6.0 109.9 ±6.8 Rat 108.2 ±5.8 107.0 ±6.7 104.3 ±7.2 107.7 ±9.2 105.2 ±6.8 107.8 ±7.9 106.7 ±7.4 Cat 103.9 ±7.6 106.6 ±7.2 108.0 ±6.2 113.8 ±7.3 110.6 ±7.4 109.8 ±9.9 108.8 ±7.7 Monkey 110.2 ±9.4 109.4 ±9.4 112.0 ±11.1 109.8 ±10.3 114.6 ±9.9 267.9 ±13.7 ---- Man 102.3 ±9.5 103.7 ±5.8 103.3 ±8.6 107.7 ±7.5 104.1 ±12.5 258.9 ±15.8 ---- mean ± s.d. Rockel AJ, Hiorns RW & Powell TP (1980) “The basic uniformity in structure of the neocortex,” Brain103:221-44.

  3. 2 3 1 0 0 1 2 3 mm cortex visual field 45º 22º Hubel 1982 7º . . 10º 0º 45º receptive fields Hubel & Wiesel 1974

  4. 2 mm Hubel & Wiesel

  5. “Hypercolumn” ~2 mm ~2 mm after Hubel & Wiesel 1962

  6. Re-routing experiments (ferret) visual auditory lab of Mriganka Sur

  7. 5 mm 1 mm Roe et al. 1990

  8. Sur et al. 1988

  9. 10,000 porpoise modern human blue whale elephant 1,000 E = 0.07  P2/3 100 crow alligator 10 Primates Mammals Birds Bony Fish Reptiles 1 humming- bird eel 0.1 0.001 0.01 0.1 1 10 100 1,000 10,000 100,000 Brain weight (grams) goldfish Body weight (Kilograms) Crile & Quiring

  10. 2º 1 cm Tootell et al. 1982 Van Essen et al. 1984 Half of area V1 represents the central 10º (2% of the visual field)

  11. ? S1 V1 A1 S2 V2 MT Krubitzer & Kaas

  12. Lateral view of monkey brain Medial view of monkey brain Cortex unfolded Felleman and Van Essen 1991

  13. "Thus the hypothesis is that the cerebral cortex confers skill in deriving useful knowledge about the material and social world from the uncertain evidence of our senses, it stores this knowledge, and gives access to it when required." Barlow 1994

  14. Finding New Associations in Sensory Data 1. Remove evidence of associations you already know about . . . . . . to facilitate detecting new ones. (1/f2 and center-surround) 2. Make available the probabilities of the features currently present . . . . . . to determine chance expectations. (-logp, adaptation) 3. Choose features that occur independently of each other in the normal environment . . . . . . to determine chance expectations or combinations of them. (lateral inhibition) 4. Choose “suspicious coincidences” as features . . . . . . to reduce redundancy and ensure appropriate generalization. (orientation selectivity) Barlow 1994

  15. Stored knowledge about environment Context: Previous sense data Task priorities Unsatisfied appetites Model of current scene New associative knowledge What we actually see Compare and remove matches Sensory messages New information about environment This cycle can be repeated Barlow 1994, fig. 1.3

  16. Measurement Update (“Correct”) • Compute the Kalman gain • Update estimate with measurement zk • Update the error covariance • Time Update (“Predict”) • Project the state ahead • (2) Project the error covariance ahead Initial estimates for and Schematic of a Kalman Filter Welch & Bishop, fig. 1.2

  17. Neighboring pixels tend to have similar values Simoncelli & Olshausen 2001

  18. natural image 1/f 2 Neighboring pixels tend to have similar values Simoncelli & Olshausen 2001

  19. Sophie in the Arctic “Whitened”: 2G or what ctr-sur does natural image 1/f 2 whitened image barlow_filt3.m

  20. Reward? Yes No Yes Yellow Volkswagen? No Finding New Associations in Sensory Data (The yellow Volkswagen problem) Harris 1980

  21. YV Finding New Associations in Sensory Data (The yellow Volkswagen problem) sparse dense “yellow Volkswagen” cell “red Ferrari” cell “combinatorial explosion” Harris 1980

  22. Finding New Associations in Sensory Data (The yellow Volkswagen problem) sparse dense “yellow” cell Y “Volkswagen” cell V Harris 1980

  23. Reward? Reward? Yes Yes No No Yes Yes Volkswagen? Yellow? No No Finding New Associations in Sensory Data (The yellow Volkswagen problem) Harris 1980

  24. Finding New Associations in Sensory Data (The yellow Volkswagen problem) sparse dense n k e g s “y” cell y “v” cell v o l a w Harris 1980

  25. Y V “sparseness” The curve shows how statistical efficiency for detecting associations with a feature X varies with the value of a parameter defined as follows: x=xpxZ /  where x ,  are the activity ratio for feature X and the average activity ratio, px is the probability of X, and Z is the number of neurons in the subset under consideration. For instance, one could identify an association with any one of the 45 possible pairs of active neurons in a subset of 10 with an efficiency of 50% provided that the neurons were active independently, the pair caused two neurons to be active, the probability of the pair occurring was 0.1, and the average fraction active was 0.2. (From Gardner-Medwin and Barlow 1994) Gardner-Medwin & Barlow 2001

  26. What are the desirable properties of directly represented features? “. . . primitive conjunctions of active elements that actually occur often, but would be expected to occur only infrequently by chance,” that is, “suspicious coincidences” Gardner-Medwin & Barlow 2001

  27. Sophie in the Arctic “Whitened”: 2G or what ctr-sur does 6 Suspicious Coincidences Random 4 log10(#) 2 0 2 3 4 5 6 7 8 6 Line 4 p < 0.0100 log10(#) 2 0 2 3 4 5 6 7 8 sum of 9 pixels barlow_filt3.m

  28. The perfect map?

  29. A more useful map 11 12 13 K L T T M Streets Aberdeen Rd …….….C7 Academy St …….…...D9 Acorn Pk ……….…....F9 Acton St ……….…….C7 Adamian Pk …....……C9 Adams St ……….…...D9 Addison St ……..……D9 Aerial St ……….…....C8 Albermarle St ….……D8 Alfred Rd …………....E9 Allen St ……………...D9 Alpine St ………...…..C7 . . . . . . . . . . . . . Longwood Ave …….L12

  30. MBTA map

  31. Linking Features: Orientation Guzmann 1968

  32. Striate cortex contains a map of orientation. “Hypercolumn” after Hubel & Wiesel 1962

  33. “Space” “Feature” Tootell et al. 1982

  34. Bosking et al. 1997

  35. Tootell et al. 1982

  36. Guzmann 1968 Linking Features: Orientation

  37. gain adjustment (1024 * 768)pixels * 24 bits/pixel = 18,874,368 bits edge detection invariance a) position b) sign of contrast 38 points * 2 words/point * 16 bits/word = 1,216 bits curvature compression ratio = 15,522 hierarchy

  38. Takahashi, Nowakowski & Caviness 1996

  39. Q1 Q1 + + (P12)Q2 + (P12)Q2 (((P12)P2)2)Q3 mitosis PVE Size 1 P1 P12 (P12)P2 ((P12)P2)2 (((P12)P2)2)P3 Q1 Cumulative Output (((P12)P2)2)Q3 (P12)Q2 Q1 CC #1 CC #2 CC #3 Takahashi, Nowakowski & Caviness 1996

  40. 1 150 PVE volume 0.8 # PVE output Q Cumulative output 100 0.6 0.4 50 0.2 0 0 1 2 3 4 5 6 7 8 9 10 11 0 0 2 4 6 8 10 12 Elapsed Cell Cycles Elapsed Cell Cycles E11 E12 E13 E14 E15 E16 E17 neuronogenetic.m Takahashi, Nowakowski & Caviness 1996

  41. 1 mm 2 mm Chenn & Walsh 2002

  42. Grove & Fukuchi-Shimogori 2003

  43. Fukuchi-Shimogori & Grove 2001

  44. gain of function (S1 smaller and shifted rostral and lateral) loss of function (S1 larger and shifted caudal and medial) Hamasaki et al. 2004

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