9.523/6.861: Aspects of a Computational Theory of

Intelligence

Shimon Ullman + Tomaso Poggio

Gemma Roig + Chia-Jung Chang

9.523/6.861: Aspects of a Computational Theory of

Intelligence

Class 4, Part A

• Human Brain –1010-1011 neurons (~1 million flies) –1014- 1015 synapses

Vision:whatiswhere

• Ventral stream in rhesus monkey –~109 neurons in the ventral stream

(350 106 in each emisphere) –~15 106 neurons in AIT (Anterior

InferoTemporal) cortex

• ~200M in V1, ~200M in V2, 50M in V4

Van Essen & Anderson, 1990

Desimone & Ungerleider 1989

ventral stream

Theventralstream

Source: Lennie, Maunsell, Movshon

[software available online]Riesenhuber & Poggio 1999, 2000; Serre Kouh Cadieu Knoblich Kreiman & Poggio 2005; Serre Oliva Poggio 2007

HMAX is in the family of “Hubel-Wiesel” models such as

Hubel & Wiesel, 1959: Fukushima, 1980, Oram & Perrett, 1993; Wallis & Rolls, 1997; Riesenhuber & Poggio, 1999; Thorpe, 2002; Ullman et al., 2002; Mel, 1997; Wersing and Koerner, 2003; LeCun et al 1998; Serre et al., 2007; Freeman and Simoncelli, 2011….

Convolutional networks such as HMAX

Hierarchical feedforward models of the ventral stream do “work”

Computational Model based on Deep Learning

Figure 1: HCNNs as models of sensory cortex.Using goal-driven deep learning models to understand sensory cortex. Daniel Yamins & James DiCarlo.

Nature Neuroscience(2016)

A key aspect of hierarchical models is

invariance to viewpoint

Theorem (transla)on case) Consider a space of images ofdimensions pixelswhichmayappearinanyposi5onwithin a window of size pixels. The usual imagerepresenta5on yields a sample complexity ( of a linearclassifier) oforder ;the oracle representa5on(invariant)yields(becauseofmuchsmallercoveringnumbers)a--muchbeBer--samplecomplexityoforder

9

moracle = O(d2 ) =

mimage

r2

d × drd × rd

m = O(r2d 2 )

poggio, rosasco

Theorem: invariance can significantly reduce sample complexity

10

Empiricaldemonstra5on:invariantrepresenta5onleadstolowersamplecomplexityforasupervisedclassifier

Thus a new hypothesis

A main computational goal of the feedforward ventral stream hierarchy — and of vision — is to compute a representation for each incoming image which is invariant to transformations previously experienced in the visual environment (in general, transformations of other objects).

Old neural data: IT neurons can be selective and invariant

Background: recording sites in Anterior IT

Logothetis, Pauls & Poggio 1995

…neurons tuned to faces are intermingled

nearby….

Neurons tuned to object views, as predicted by model!

Logothetis Pauls & Poggio 1995

12 7224 8448 10860 12036 96

12 24 36 48 60 72 84 96 108 120 132 168o o o o o o o o o o o o

-108 -96 -84 -72 -60 -48 -36 -24 -12 0-168 -120

Distractors

Target Views60

spi

kes/

sec

800 msec

-108 -96 -84 -72 -60 -48 -36 -24 -12 0-168 -120 oo o o o o o o o oo o

Logothetis Pauls & Poggio 1995

A very selective “view-tuned” cell in IT

View-tuned cells: scale invariance (one training view only) !!!

Logothetis Pauls & Poggio 1995

How neurons may compute an invariant signature

18

Algorithm that learns in an unsupervised way to compute invariant representations

ν

P(ν )

νµkn(I) = 1/|G|

|G|X

i=1

�(I · gitk + n�)

...

Our basic machine: a HW module (dot products and histograms/moments for image seen through RF)

• The cumulative histogram (empirical cdf) can be be computed as

• This maps directly into a set of simple cells with threshold

• …and a complex cell indexed by n and k summating the simple cells

µnk (I ) = 1

|G |σ ( I ,git

k + nΔ)i=1

|G |

∑

nΔ

The nonlinearity can be arbitrary for invariance, if optimal selectivity is not required

20

Invariant signature from a single image of a new object

21

Invariant signature from a single image of a new object

...

< x,t >

• Highly simplified neuroscience suggests that a natural functional for a neuron to compute is a high-dimensional dot product between an “image patch” and another image patch (called template) stored in terms of synaptic weights (synapses per neuron )

• Projections via dot products are natural for neurons: here simple cells

∼ 102 −105

Neuroscience definition of dot product!

Remark: biological motivation for image representation

...

Our basic machine: a HW module (dot products and histograms/moments for image seen through RF)

• The cumulative histogram (empirical cdf) can be be computed as

• This maps directly into a set of simple cells with threshold

• …and a complex cell indexed by n and k summating the simple cells

µnk (I ) = 1

|G |σ ( I ,git

k + nΔ)i=1

|G |

∑

nΔ

Pooling

Dendrites of a complex cells as simple cells…

Active properties in the dendrites of the complex cell

Invariance explains a puzzle

• what is visual cortex computing?

• function and circuits of simple-complex cells

• how does the face network work?

• what is the computational reason for the eccentricity-dependent size of RFs in V1, V2, V4?

poggio, anselmi, rosasco, tacchetti, leibo, liao

RF size depends on eccentricity in a special way

Note:wefocusonthesamplinglayoutoftheretinalganglioncells(RGCs)-theoutputsoftheretina.

Desimone & Ungerleider 1989

ventral stream

Theventralstream

Source: Lennie, Maunsell, Movshon

Retinalsamplingisnonuniform

• Thumbnailatarm’slength=1degree• By+/-1degree,resolutionhasdroppedby½• Mostcommonexplanation:

– Fullresolutioneverywherewouldrequireanopticnervethethicknessofyourneck,andvisualcortexthesizeofasmallcar(*)

– Solution:asmallpatchofhighresolutionthatyoucanmovearound

• However:theparticularsamplingstrategytheretinahaschosensuggeststhereismoretothestory

(*)calculationsareapproximate

An application of i-theory: translation and scale invariance implies

a specific model of eccentricity-dependent RFs in cortex

Hubel and Wiesel, 1971

Scatter of receptive field sizes in V1

Schiller, P., Finlay, B., Volman S. Quantitative Studies of Single Cells Properties in monkey striate cortex, 1976

Explaining the puzzle

34

Computational reason for eccentricity dependence of RFs size

ν

P(ν )

νµkn(I) = 1/|G|

|G|X

i=1

�(I · gitk + n�)

to compute invariant representation

Recipe:

• memorize a set of images/objects called templates and for each template memorize observed transformations as images

• to generate an invariant signature - compute dot products of transformations with image - pool, e.g. compute histogram of the resulting values

35

36

Geometry of scaling

37

Sampling in the window

38

Magic window in V1

5 degree! total 40x40 units

25’ !!! total 40x40 units

Qualitative predictions

• Very small foveola ~25’

• In the center of fovea “full” scale invariance, little position invariance

• Position invariance proportional to spatial frequency

• Anstis

• Bouma’s law for peripheral crowding d= b x (role of V2 b=0.5)

• Prediction: crowding in the fovea at less than d=2’40” in fovea

Qualitative predictions

Anstis, 1974

“Prediction” of Anstis observation

Computational model

V

D

SV = 2 * arctan (S/(2D))

D= 50.39 cm

…

…

…

…… … … …

… …

… …

5 degrees - 4.4 cm - 224 px

0.63 degrees - 0.55 cm - 28 pxtemplate smaller resolutiontemplate 2nd smaller resolution

template larger resolutiontemplate 2nd larger resolution

Eccentricity dependent model for quant predictions

0 eccentricity (deg)

scale

smallest res

largest res

orig

inal

imag

ew

hat t

he m

odel

“see

s”

(sam

plin

g ph

otor

ecep

tors

)…

……

…

… … … …

… ………

…

……

…

… … … …

… ………

…

……

…

… … … …

… ………

…

……

…

… … … …

… ………

Gemma Roig

Smallest scale

largest scale

original image

what the model “sees” (sampling photoreceptors)Gemma Roig

V

D

S

V = 2 * arctan (S/(2D))

D= 50.39 cm

template larger scale - convolutiontemplate 2nd larger scale - convolution

template smaller scale - convolutiontemplate 2nd smaller scale - convolution

1st layer- model with 4 scales

input crops at 4 scales input image

…

…

…

…… … … …

… …

… …

input crops at 4 scales what the model sees

5 degrees - 4.4 cm - 224 px

0.63 degrees - 0.55 cm - 28 px

Psychophysical experiments

Experimental question: is the window of visibility…

…the same as the window of invariance to scale and shift for novel, unfamiliar objects, never seen before

(as predicted by i-theory)?

window of visibility

scale

eccentricity0 deg

window of invariance

scale

eccentricity0 deg

?Gemma Roig

Yena Han

Notice: published data (refs…) are inconclusive and inconsistent

Question: is the window of visibility…

Gemma RoigYena Han

50

Examples

51

Example

52

ExperimentsPhase 1

check parameters of the visual window

psychophysics experiments with very familiar letters:

recognition of letters at different eccentricities and sizes we have seen letters in all positions: no need for training

sanity check

Gemma RoigYena Han

A

Phase 1 experiments

A A

A

A

A

recognize familiar letters of different sizes at different eccentricities

visual window:

scale

eccentricity0 deg

Gemma RoigYena Han

ExperimentsPhase 2

check position invariance with unfamiliar characters (Chinese letters) psychophysics experiments with Chinese letters:

training phase: learn few new letters at one eccentricity, testing phase: is the letter recognizable at other eccentricities? (same/different? task)

is the visual window the same as the window of invariance?

Gemma RoigYena Han

Phase 2 experiments

learn a novel character (never experienced before) at a eccentricity and scale, test recognition of the character at other eccentricities and /or scales

train (show once): test:

visual window:

scale

eccentricity0 deg

ecc. (deg.)ecc. (deg.)0 0

scale scale

Psychophysics Experiment • Stimuli: Korean Letters. Should be unfamiliar to subjects.

• Same/Different Discrimination Task

• Scale Invariance: Present target letter and test either the target or a distractor letter at the center. The letters vary in size.

• Position Invariance: Present target letter at one eccentricity and test either the target or a distractor letter at another eccentricity

• Presentation time 33 ms

• Letter size 1 deg

모 보==

모 == 모

Gemma RoigYena Han

Position Invariance

Scale Invariance

Gemma RoigYena Han

Next Question(experiments and simulations)

Which kind of pooling?In V1 and V2 and V4?

Next Question(experiments and simulations)

Crowding predictions depending on pooling

Psychophysics Experiment

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Psychophysics

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