The Major Transitions in Evolution...Evolution of correlation in two environments • Some evolve at...

How can evolution learn?

Eörs Szathmáry

Biological Insitute,

Eötvös University, Budapest

Patmenides Center for the Conceptual

Foundations of Science,Pullach/Munich

Units of evolution: a tacit

‘algorithm’

Some hereditary traits affect

survival and/or fertility

1. multiplication

2. heredity

3. variability

• Electronics and

Computer Science

• University of

Southampton

Evolution as a cognitive process?

• Gregory Bateson (1904-1980)

Bayes and Darwin

Bayes and selection (e.g. Harper,

1010)

The Hebb synapse

A Hopfield network

Unsupervised learning with

Hebbian rule

Types of learning

Cycle of the lambda phage

Genetic regulatory network

Analogue Hopfield neural

network

Potential domains of learning

New activity level of a gene

Selective environment

Fitness of a phenotype is a scalar product

Selection pressures on interaction

coefficients are Hebbian

Strong selection, weak mutation

Selection for single phenotypic

patterns

Changing interaction, two target

patterns

Evolution of correlation in two

environments• Some evolve at a constant positive rate; these arise from

pairs of traits that are positively correlated in both patterns

(e.g., genes 2 and 6 are ++ in S1 and −− in S2), likewise

negative interactions evolve at a constant rate between

pairs of traits that have opposite signs in both patterns.

• When the correlation of a pair of traits in one pattern is

contradicted by the correlation of that pair in the other

pattern (e.g., s1s2>0 in S1 and s1s2<0 in S2) the

corresponding regulatory interactions (e.g., B12 and B21)

are unable to record the correlation of either target pattern

and remain near zero onaverage

Memory with two target

phenotypes

Two target phenotypes

Can the system generalize

beyond the training set?

Empirical modularity

Fixed and modularly varying

goals

Evolution of genetic triggers

MVG “modularity language”

Modularity Varying Goals: goals change

over time but share the same subgoals

Lotka-Volterra competitive model

Experiments affect carrying

capacities

Selection for mutations affecting

wij

Caveat! Limiting assumptions

• Wij = Wji symmetry

normalization

Mechanistic equivalence between

eco-evo and learning

The flip side of the coin:

evolution IN cognition?

Hebb and Darwin

(Adams, 1998)

synaptic replication synaptic mutation

The most exciting hint from

neurobiology: structural plasticity

What could be the algorithmic advantages?

Candidate mechanisms of

“neuronal replication”

• Local connectivity copying

• Copying of activity patterns in bistable

neurons

• Path evolution

• Other?

A recurrent attractor network

Population of networks: selection

Evolution

Thanks for the invitation!

Thanks for your attention!

Paths as Units of Evolution

Mutation and Crossover of Paths

The interplay of Hebb and Darwin

DNA replication Neuronal copying

local influence non-local influence