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Visual Object Recognition as Paradigm of Brain Function

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Visual Object Recognition as Paradigm
of Brain Function
Christoph von der Malsburg
Inst. f. Neuroinformatik, Ruhr-Universität Bochum,
Computer Science, Neuroscience, USC, Los Angeles
FIAS, Frankfurt
Mind: function
Autonomous Goal Pursuit
Instincts, drives, emotions: anima
Intentionality: relating to the environment
Perception: representation, interpretation, epistemology
Action
Consciousness
Subsystem Coordination
Intelligence: Generalization
abstract schemata applied to concrete situations
Matter: material implementation
How does Matter….
(molecules, synapses, neurons, circuits, systems, gross anatomy)
….Correspond to Mind?
What is the Data Structure
of Brain State?
of Memory?
Organization
Coordination of molecules, cells, …
into one coherent system
Evolution
beyond intelligent design and blind search
Ontogenesis
little genetic information for much structure
109 bits in the genome, 1016 bits in the wiring diagram
parametrically controlled sequence of events of SO!
Learning
accumulation of memory traces
unsolved problem!!
Organization (contd.)
State organization
SO of scene representation
synthesized from memory traces
quick form of ontogenesis
input as analogon to control genes
Challenges
coordination of subsystems (consciousness!)
generalization (intelligence!)
Discours de la mГ©thode
Science has to be portable, simple!!
teachable, paraphrasable, reproducible, structurally stable
The importance of paradigms
free fall, two-body motion, ideal gas, harmonic oscillator,
hydrogen atom, regular crystal, Ising model (phase transition)
Invariant object recognition as paradigm
Mathematics
essence: bridging description levels
1) differential equations plus stability analysis
2) statistical mechanics
Computer and Brain
Computer simulation as tool
as acqua regia to separate the wheat from the chaff
connecting mind-phenomena and brain-phenomena
immediate technical applications
Computer as model for the brain
Artificial Intelligence: whose intelligence – machine or man?
Turing universality: ready to be programmed
Brain “universality”: autonomous problem solving
The computer is not an end but a tool
Feynman’s 1959 Lecture
There's Plenty of Room at the Bottom
An Invitation to Enter a New Field of Physics
Miniaturizing the computer
If I look at your face I immediately recognize that I have seen it before. (....) Yet
there is no machine which, with that speed, can take a picture of a face and say
even that it is a man; and much less that it is the same man that you showed it
before---unless it is exactly the same picture. If the face is changed; if I am closer
to the face; if I am further from the face; if the light changes---I recognize it
anyway. Now, this little computer I carry in my head is easily able to do that. The
computers that we build are not able to do that. The number of elements in this
bone box of mine are enormously greater than the number of elements in our
``wonderful'' computers. But our mechanical computers are too big; the elements
in this box are microscopic. I want to make some that are submicroscopic.
Invariant Object (Face) Recognition
Very competitive field (benchmarks, indust. appl.)
Attention control
Figure-ground separation
Correspondence finding under variance
position, orientation, scale, pose, deformation, illumination,
surface markings, partial occlusion, background, noise
Learning
Attention, Figure-Ground Separation
Xiangyu Tang
Object recognition 1
Image Domain
Model Domain
Model Window
van Essen - Felleman
Correspondence-based Object Recognition
Graph matching
a, b, c, d,..: feature types
Devalois 1
Gabors
Devalois 2
Image-to-jets
Maryl-representation
Maryl-reconstruction
Similarity matrix
Laurenz Wiskott
2D map formation
Junmei Zhu
Maryl-match
Bunch graph method
Laurenz Wiskott
Bunchgraph
Reconstruction
from 70 Reference faces
Rekonstrution
Left: Original
Right: Reconstruction
Bunch graph: gender
Visual Learning: Aspects of the Problem
•
•
•
•
Identification of significant patterns
Extraction of examples
Finding more examples
Consolidation of the representation
One-Shot Learning
Hartmut Loos
Bottles found
One person found
Hartmut Loos
More persons
Hartmut Loos
Face Finding
Analysis
Synthesis
Parameterized
model
Principal Component Analysis (PCA)
PCA schema
PCA faces
Jan Wieghardt
Pose-estimation
PCA Nonlin
Kazunori Okada
Pose-reconstruction
Kazunori Okada
Gestures Samples
Hai Hong
Parameterized Gesture Model
Andreas Tewes
Object Recognition as Paradigm
Implementation
Perception
Action
Goals
Intelligence
Consciousness
Organization
The Importance of Dynamic Links
Classical Data Structure: Vector of Unit Activities
Units: (groups of) neurons as elementary symbols
Problem: No Structure!
Dynamic Link to represent Relatedness: a and b …
relate to the same object
relate to neighboring points (in an object)
correspond to each other
Physical Representation:
Environment: causal connection
Brain: fiber connection
Primitive Observation: temporal signal correlation
Links have to be dynamic as part of brain state
Graph matching
How implement
dynamic links?
Dynamic Links I
Temporal binding
• Temporal binding
10 msec Time
• Rapid, Reversible Synaptic Plasticity
Network Self-Organization
Network
Signals
Signal Dynamics
Synaptic Plasticity
The brain is dominated by attractor networks
Among them are:
2D Aspects of objects
Homomorphic (Correspondence) mappings
Implementation:
DLM vs. MCU
Dynamic Links Represented by…
I Switching Synapses
• Based on basic mechanisms of organization
• Simple structural preconditions
• Possible early in ontogeny
• Slow, fragile (very large search space)
II Switching Neurons (control units)
• Fast, reliable (small search space)
• Needs very specific connectivity patterns
• Storage and retrieval of DLM Networks
Dynamic Links II
Link control units
Jörg Lücke
Perception,
Action
Goals
Intelligence:
Application of abstract schemata to
concrete situations
Raven 1
Raven 2
Raven 3
Graph matching
Concrete situation
Abstract schema
Consciousness
I am conscious if I can
speak about it
remember it
picture it
act on it
in short: have all my five senses together
Consciousness is tantamount to coordination
(interfacing) of subsystems!
Vision (recognition) is prime example of an
interface
Analysis
Synthesis
Autonomy of organization
Complete organizational chain
(evolution) ontogenesis, learning, state organization
Ultrastability
Autonomous regulation of control parameters
Definition of Organization
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