an evolutionary framework for neuronal architectures eörs szathmáry eötvös university collegium...

An Evolutionary Framework for Neuronal Architectures

Eörs Szathmáry

Eötvös University Collegium Budapest

The group• Zoltán SzatmáryZoltán Szatmáry programming, neuroprogramming, neuro• Péter IttzésPéter Ittzés programming, bioprogramming, bio• Máté VargaMáté Varga programming, elect. eng.programming, elect. eng.• Ferenc HuszárFerenc Huszár informaticsinformatics• Anna FedorAnna Fedor bio, etholbio, ethol• István ZacharIstván Zachar bio, evolbio, evol• Gergő OrbánGergő Orbán biophys, Bayesian learnbiophys, Bayesian learn• Máté LengyelMáté Lengyel neuroneuro• Szabolcs SzámadóSzabolcs Számadóbio, evolbio, evol

It all started with JMS…

• „You know Eörs, we have to consider language seriously in the book”

• The origin of language remains the primary motivation behind this work

The major transitions (JMS & ES, 1995)

***

*

* These transitions are regarded to be ‘difficult’

Why is language so interesting?

• Because everybody knows that only we talk

• …although other animals may understand a number of words

• Language makes long-term cumulative cultural evolution possible

• A novel type of inheritance system with showing “unlimited hereditary” potential

What is so special about human language?

• Basically, it is the fact that we make sentences using grammar

• Languages are translatable into one another with good efficiency

• Some capacity for language acquisition seems to be innate

• THE HOLY GRAIL IS THE EMERGENCE OF SYNTACTICAL PRODUCTION?

Three interwoven processes

• Note the different time-scales involved• Cultural transmission: language transmits itself as

well as other things• A novel inheritance system

The case of Nicaraguan sign language: something seems to be

innate

• School for deaf children was opened 30 years ago.

• People range from 4 to 45 years by now.

Development of NSL

• NSL has evolved from a system of nonlinguistic gestures into a full sign language with its own grammar that continues to expand and mature

• The youngest children in the NSL community are the most fluent signers

• Deaf Nicaraguan children have created their own language independently of exposure to a preexisting language structure.

• Language is so resilient that it can be triggered by exposure to a linguistic input that is highly limited and fragmented—an indication of the fundamental innateness of grammar language readiness

A note on semantix and syntax

• The fact that today one can dissociate semantics from syntax does not mean that they were dissociated throughout language evolution

• If language is efficacious, then selection acted on semantics

• Emerging syntax thus was semantically constrained

Challenges: a simple experiment (Hauser & Fitch)

• Habituation experiments• Finite state grammar

(AB)n is recognizable by tamarins

• Phrase structure grammar AnBn is NOT.

• Human students recognize both

BUT: Recursive syntactic pattern learning in birds!

• European starlings (Sturnus vulgaris) accurately recognize recursive syntactic patterns

• They are able to exlude agrammatical forms• Centre-embedding is not uniquely human

Patterns are made up of naturally occurring vocal patterns

• Learning to classify by operant conditioning• This is NOT production!

The genetics of complex behaviour is not easy…

• Pleiotropy: one gene affecting different traits• Epistasis: effects from different genes do not combine

independently• Intermediate phenotypes must be identified!

Genetic analysis of fruitfly behaviour

The FOXP2 gene is mutant in a family with SLI

• SLI: specific language impairment• In the KE family the mutation is a single

autosomal dominant allele• Another individual has one copy deleted• TWO intact copies must be there in humans!• The mutation affects morphosyntax: Yesterday I

went to the church and talk to nanny brother• Chromosome 7, forkhead protein

Nucleotide substitutions in the FOXP2 gene

• Bars are nucleotide substitutions• Grey bars indicate amino acid changes• Likely to have been recent target of selection

FOXP2 seems even more interesting

• FOXP2 single nucleotide polymorphism (mainly in the 5’ regulatory region) associates with schizophrenia with auditory hallucinations

• FOXP2 is under stabilizing selection (even on synonymous changes) in song-learning birds (human mutations are not seen), but not in vocal-learning mammals or in non-singing birds

• the human-unique substitution in exon 7 (T303N) was flanked by two changes in both whale and dolphin (S302P and T304A)

FOXP2 seems even more interesting II

• studies in songbirds show that during times of song plasticity FoxP2 is upregulated in a striatal region essential for song learning

• FOXP1 and FOXP2 expression patterns in human fetal brain are strikingly similar to those in the songbird

• including localization to subcortical structures that function in sensorimotor integration and the control of skilled, coordinated movement.

• The specific co-localization of FoxP1 and FoxP2 found in several structures in the bird and human brain predicts that mutations in FOXP1 could also be related to speech disorders.

More on FOXP2

• fMRI: underactivity of Broca during word generation

• repetition of non-words with complex articulatory patterns: the core deficit is one of sequential articulation of phonological units

• FOXP2 mutation could have been responsible for the perfecting of speech

• How would it affect the mirror system?

An evaluation of selective scenarios: Trends Ecol. Evol. in press

Selective scenarios for the emergence of natural languageSzabolcs Számadó and Eörs SzathmáryCollegium Budapest (Institute for Advanced Study), Szentháromság u. 2, H-1014, Budapest, Hungary

Corresponding author: Számadó, S. ([email protected]).

The recent blossoming of evolutionary linguistics has resulted in a variety of theories that attempt to provide a selective scenario for the evolution of early language. However, their overabundance makes many researchers sceptical of such theorising. Here, we suggest that a more rigorous approach is needed towards their construction although, despite justified scepticism, there is no agreement as to the criteria that should be used to determine the validity of the various competing theories. We attempt to fill this gap by providing criteria upon which the various historical narratives can be judged. Although individually none of these criteria are highly constraining, taken together they could provide a useful evolutionary framework for thinking about the evolution of human language.

Theories/Questions 1 2 3 4 5 6

Language as a mental tool (Jerison, 1991; Burling, 1993) + + - + - -Grooming hypothesis (Dunbar, 1998) - + - - - -Gossip (Power, 1998) + - - + - -Tool making (Greenfield, 1991) + + + + + -Mating contract (Deacon, 1997) - - - - - -Sexual selection (Miller, 2000) + - - - - -Status for information (Dessalles, 2000) + - - + - -Song hypothesis (Vaneechoutte & Skoyles, 1998) - - - - - +Group bonding/ ritual (Knight, 1998) - + - - - -Gestural theory (Hewes, 1973) + - + + - -Hunting theories (Washburn & Lanchester, 1968) + + + + - -

(1) selective advantage (2) honesty (3) grounded in reality (4) power of generalisations (5) cognitive abilities (6) uniqueness

The evolutionary approachgenes

development

behaviour

selection

learning

environmentImpact of evolution on the developmental genetics of the brain!

One method of finding out (within ECAgents)

• Simulated dynamics of interacting agents• Agents have a “nervous system”• It is under partial genetic control• Selection will be based on learning performance

for symbolic and syntactical tasks• If successful, look and reverse engineer the

emerging architectures• HOW GENES RIG THE NETWORKS??

Between linguistic input and output…

Transmission dynamics in simulated agents

The most important precedent

„the purpose of this paper is to explore how genes could specify the actual neuronal network functional architectures found in the mammalian brain, such as those found in the cerebral cortex. Indeed, this paper takes examples of some of the actual architectures and prototypical networks found in the cerebral cortex, and explores how these architectures could be specified by genes which allow the networks when built to implement some of the prototypical computational problems that must be solved by neuronal networks in the brain”

Highly indirect genetic encoding

• There are special results with direct genetic encoding (one gene per neuron or per synapse)

• THIS IS NOT WHAT WE WANT• There are around 35 thousand genes• Only a fraction of them can deal with the

brain• Billions of neurons, more synapses

Summary of our efforts

In: Nehaniv, C., Cangelosi, A & Lyon, C. (2005) Origin of Communication, in press. Springer-Verlag

ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940.

Software architecture

519 classes

99267 lines of C++ code


Population dynamics and agent lifecycle


Ontogenesis of a neuronal network


A note on the importance of topographicity

• For each tropographical net, one can construct an equivalent topological net

• The nature of variation is very different for the two options

• Genes obviously affect topographical networks


Parameter values

Population dynamics and games• Population size: 100. • Time steps: 500 (200 for the cloning test). • Number of games played per time step per agent:

100. • Death process: least fit (5). • Mating process: roulette wheel. • Number of offspring: Poisson with Lambda=5.


Parameter values 2

Neurobiological parameters• Number of layers: randomly chosen from the range [1,3]

(mutation rate: 0.008). • Number of neuron classes: randomly chosen from the range

[1,3] (mutation rate: 0.2). • Number of neurons: randomly chosen from the range [10,30]

(mutation rate: 0.2). • Number of projections: randomly chosen from the range [1,3]

(mutation rate: 0.02). • Rate coding with linear transfer function [-1 , 1]. • Hebbian learning rules. • Reward matrix is same as the pay-off matrix of the given game

(below). • Brain update: 10 (same for listener and speaker).


There are:• two kinds of environments, E={-1,1},• three types of cost-free signals S=[-1, 1, else],• three types of possible decisions D=[-1, 1],

where values other than –1 or 1 mean no signal and no response respectively.

Task: A two-person game


-1/1

Population

Environment

A Coordination Game

Speaker Listener

Decision

Signal

Decision

Different types of gameDifferent types of game

Coodination game (Coop)Coodination game (Coop) Division of Labour (Div)Division of Labour (Div) Prisoners’ dilemma (PD)Prisoners’ dilemma (PD) Hawk- Dove game (SD)Hawk- Dove game (SD)

Environment -1 Environment 1

Coop (-1) Coop (1)

Div Div

PD (-1) PD (1)

SD (-1) SD (1)

PD (-1) Coop (-1)

PD (-1) CoopRev (1)

SD (-1) Coop (-1)

SD (-1) CoopRev (1)

D(1) D(-1)

D(1) 1 5

D(-1) 0 3

D(1) D(-1)

D(1) -1 5

D(-1) 0 3

D(1) D(-1)

D(1) 0 5

D(-1) 5 0

D(1) D(-1)

D(1) 5 1

D(-1) 0 0

Coodination gameCoodination game Division of LabourDivision of Labour Prisoners’ dilemmaPrisoners’ dilemma Hawk - Dove gameHawk - Dove game

Div/Div

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S:[1] E:[-1] DL:[-1];DS:[1]

S:[1] E:[-1] DL:[1];DS:[-1]

S:[1] E:[1] DL:[-1];DS:[1]

S:[1] E:[1] DL:[1];DS:[-1]

Coop/Coop

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S:[-1] E:[1] DL:[1];DS:[-1]

S:[0] E:[-1] DL:[-1];DS:[1]

S:[0] E:[-1] DL:[1];DS:[1]

S:[0] E:[1] DL:[-1];DS:[-1]

S:[0] E:[1] DL:[1];DS:[-1]

S:[1] E:[-1] DL:[-1];DS:[1]

S:[1] E:[-1] DL:[1];DS:[1]

S:[1] E:[1] DL:[-1];DS:[-1]

S:[1] E:[1] DL:[1];DS:[-1]

other-reporting signals self-reporting signals

dishonest signals uninformative signals

no signal

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S:[1] E:[-1] DL:[-1];DS:[-1]

S:[1] E:[-1] DL:[-1];DS:[1]

S:[1] E:[-1] DL:[1];DS:[1]

Why is there communication in SD/SD?

• There is conflict of interest in the game, BUT:• There is mixed ESS: it pays to be the reverse of

the opponent!• Speaker sees the environment, chooses the selfish

strategy and and informs the listener about it in the „hope” that the other behaves complementarily. The other has no real choice but to „believe” in it.

• Mixed ESS AND changing environments AND informational asymmetry RESULT IN communication

other-reporting signals self-reporting signals

dishonest signals uninformative signals

no signal

PD/Coop

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S:[-1] E:[1] DL:[-1];DS:[-1]

S:[0] E:[-1] DL:[-1];DS:[-1]

S:[0] E:[-1] DL:[-1];DS:[1]

S:[0] E:[1] DL:[-1];DS:[-1]

S:[1] E:[-1] DL:[-1];DS:[1]

S:[1] E:[1] DL:[-1];DS:[-1]

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S:[1] E:[-1] DL:[1];DS:[-1]

S:[1] E:[-1] DL:[1];DS:[1]

S:[1] E:[1] DL:[1];DS:[1]


Early brains (t:10) Scenario: E1: complementary, E-1:same

Visual input

Audio input

Const input or unconnected

Mixed colours indicate input mixing.


Advanced brain (t:750)

Scenario: E1: complementary, E-1:same

Visual input

Audio input

Constants input or unconnected

Mixed colours indicate input mixing


Is there inheritance, despite highly indirect genetic encoding?

• Scatter plots for AudioIn, AudioOut, Const, Vision and Decision neurons

• Experiments on clones


The central issue with indirect encoding is whether one can find heritability of the simulated, evolved neuronal networks. If our biomimetic, indirect encoding is successful; this should be the case.

Measuring the Heritability of Neural Connections

in ENGA-Generated Communicating Agents

Input/output neuron

h2

AudioIn 0.8689

AudioOut 0.8708

Const 0.8696

Decision 0.8123

Vision 0.8428

Estimated heritability values (h2) of the number of connections of the given input/output neurons (right).

This is a proof that ENGA works as we hoped: despite indirect encoding, there is hereditary variation between indivudal phenotypes on which simulated natural selection can act.


Is there inheritance, or only council of the elders?

• The increase with age of time• The code of individuals of time• Green lines: individual living still the end of the simulation• Red: birth events


Details of learning/heritability experiment

• Individuals are taken from an equilibrated Coop game• All are newborn, no close relatives• Smart and stupid individuals are included• Individuals were educated in a testbed• You see the average of the reward received in 1010

turns• Convention carved into pieces: two environments x

two types of input (audio and visual), measure the signal or the decision


Is there inheritance of behaviour?


What next?

• For example, do the Fitch-Hauser experiment• Select for networks that do finite state grammar and

that do central embedding • If successful, look at the networks • What is an ‘easy’ evolutionary path?

an evolutionary framework for neuronal architectures eörs szathmáry eötvös university collegium...

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