From Amoeba to Cognition Frankfurt Institute of Advanced Studies April 16, 2003 Christoph von der Malsburg Institut für Neuroinformatik und Fakultät für Physik und Astronomie Ruhr-University Bochum, Germany and Computer Science Department and Program in Neuroscience University of Southern California Los Angeles

Amoeba

Euglena

History of life

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: 10 9 bits Information content of the brain’s wiring: bits (10 10 neurons, hence ld = 33 bits per connection, times synapses = bits of information) Solution: genetically controlled self-organization

Rettec anatomical schema A 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”) Time 10 sec

Meister (Prenatal ferret retina, M. Meister et al.)

Network Self-Organization NetworkSignals 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 projection A 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 Φ i (x,y) under 2 constraints: Preservation of information (ability to reconstruct) Sparsity Natural images

Olshausen-Field Gabors

Points of Conclusion: Retinotopy, orientation specificity as paradigms of 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) imagemodel

van Essen

Rubfig 1 Image DomainModel Domain Model Window Object recognition

Rubfig 2 Image DomainModel Domain Model Window Objection recognition 2

Temporal binding Rapid, Reversible Synaptic Plasticity Time 10 msec

Network Self-Organization NetworkSignals Signal Dynamic Synaptic Plasticity

Image-to-jets

Maryl-representation

2D mapping formation

Face recognition rates ModelProbeSizeRecogniti on rate * Other systems frontalDiff expression large transform 12485% frontalDiff expression small transform %98% (=245/250)(Wiskott et al 97) frontal30° rotation in depth %66.4% (=73/110)(Wiskott & Malsburg 96) * After 3 iterations

Marylin

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 Labor Algorithmic DOL

Human: Loose Communication Machine : Goal Definition Creative Infrastructure: Goals, Methods, Interpretation, World Knowldege, Debugging Data, „Algorithms“ Organic 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