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From Amoeba to Cognition Frankfurt Institute of Advanced Studies
April 16, 2003
Christoph von der Malsburg Institut für Neuroinformatik
und Fakultät für Physik und AstronomieRuhr-University Bochum, Germany
andComputer Science Departmentand Program in Neuroscience
University of Southern CaliforniaLos Angeles
Amoeba
Euglena
Repertoire of single-celled animals 1
Metabolism
• Production, transformation and breakdown of molecules
• Synthesis of molecules under genetic control
• Regulation, e.g., of ionic concentrations
• Transport of molecules, inside, in and out of cell
• Electrical “behavior”
• Circadian rhythm
Reproduction
Repertoire of single-celled animals 2
Behavior
•Sensing (light, sound, chemical milieu)
•Self-shaping (pseudopodia, mitosis)
•Motility, esp. chemotaxis
•Feeding: ingestion and digestion
•Aggression, flight
•Signalling
•Collaboration (e.g., slime mold, biofilms)
Amoeba aggregation 2
Spiral waves
Ants
Neuron 1
Neuron 2
Synapse
The Ontogenetic “Riddle”
• Information content of the genome: 109 bits
• Information content of the brain’s wiring: 1016 bits
(1010 neurons, hence ld 1010 = 33 bits per connection,
times 1015 synapses = 1016 bits of information)
• Solution: genetically controlled self-organization
Rettec anatomical schemaA Model for the Ontogenesis of Retinotopy
(Willshaw and Malsburg, 1976)
Rettec functional schma
• Chemotaxis
• Synaptic plasticity controlled by electrical signals
Hebbian Plasticity
Correlation-controlled Synaptic Plasticity
(“Hebbian Plasticity”)
Time10 sec
Meister
(Prenatal ferret retina, M. Meister et al.)
Network Self-Organization
Network Signals
Signal Dynamic
Synaptic Plasticity
Rettec functional schma
Rettec principle 2
Rettec development
Visual system schema
Levay stripes
Binoc 1 A Model for the Ontogenesis of Ocularity Domains (Biol. Cybernetics, 1977)
Binoc 2
H&W orient
Devalois 2
73 projectionA model for the development of orientation-
specific neurons (Kybernetik, 1973)
Retina
Cortex
Connection Strength
73 stimuli Retinal Stimuli
Meister
(Prenatal ferret retina, M. Meister et al.)
73 cell 70 Re-organization of a cortical receptive field
73 cortex post
73 orientmap
Devalois 1
Gabors
Olshausen-and Field: Schema
Development of connections strengths Φi(x,y) under 2 constraints:
• Preservation of information (ability to reconstruct)
• Sparsity
Natural images
Olshausen-Field Gabors
Points of Conclusion:
• Retinotopy, orientation specificity as paradigmsof network self-organization and CNS ontogenesis
• Ontogenesis of CNS and cellular repertoire
• Amount of genetic information
Invariant object Recognition(As paradigm of a cognitive function)
image model
van Essen
Rubfig 1Image Domain Model Domain
Model Window
Object recognition
Rubfig 2Image Domain Model Domain
Model Window
Objection recognition 2
Temporal binding
Temporal binding
Rapid, Reversible Synaptic Plasticity
Time10 msec
Network Self-Organization
Network Signals
Signal Dynamic
Synaptic Plasticity
Image-to-jets
Maryl-representation
2D mapping formation
Face recognition rates
Model Probe Size Recognition rate *
Other systems
frontal Diff expressionlarge transform
124 85%
frontal Diff expressionsmall transform
124 96.8% 98% (=245/250)(Wiskott et al 97)
frontal 30° rotation in depth
110 93.6% 66.4% (=73/110)(Wiskott & Malsburg 96)
* After 3 iterations
Points of Conclusion:
• Evolution as a game of varying the eurkaryote’s repertoire
• Ontogenesis as a refinement of old cellular behavioral patterns
• reproduction, differentiation
• cellular migration, chemotaxis
• chemical signalling, reaction-diffusion patterns
• putting out of “pseudopodia”
• Brain function as a fast version of the same game again
• Network Self-Organization the central process
Outlook
• The flexibility of the human brain shows that fundamental principles are at work
• Similar conclusions may be drawn from the rapid development of human society
• Elucidating the general principles of organization is the challenge of our times
• This issue has at present no academic home
Molecular Biology
The Software Crisis
NIST Study 02: yearly US loss due to SW failure: $60 Billion
Human:
Detailed Communication
Machine:
Creative Infrastructure: Goals, Methods, Interpretation, World Knowledge, Diagnostics
Algorithms: deterministic, fast, clue-less
Algorithmic Division of LaborAlgorithmic
DOL
Human:
Loose Communication
Machine:
Goal Definition
Creative Infrastructure:Goals, Methods, Interpretation, World Knowldege, Debugging
Data, „Algorithms“
Organic ComputersOrganic
Computers
Self-Organization in Need of Development
The ideas of self-organization have created a revolution, but they are now in need of forceful further development!
Underdeveloped aspects:
• Control of the control parameters (Ashby’s super-stability)
• Explicit representation of goals
• Cascades of organization (description of unfolding systems)
• Escaping geometry (e.g., network self-organization)
Physics to the Rescue!!• Physics has a proven track-record of understanding complex
phenomena on the basis of simple paradigms and principles
• Physics is in possession of highly relevant methodology(statistical mechanics, systems of non-linear differential equations)
• Physics has a very successful system of education
• Physics is on the look-out for a new application field