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An Evolutionary Framework for Neuronal Architectures

An Evolutionary Framework for Neuronal Architectures. Eörs Szathmáry. Collegium Budapest. Eötvös University. The group. Zoltán Szatmáry programming, neuro Péter Ittzés programming, bio Máté Varga programming, elect. eng. Ferenc Huszár informatics Anna Fedor bio, ethol

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An Evolutionary Framework for Neuronal Architectures

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  1. An Evolutionary Framework for Neuronal Architectures Eörs Szathmáry Collegium Budapest Eötvös University

  2. The group • Zoltán Szatmáry programming, neuro • Péter Ittzés programming, bio • Máté Varga programming, elect. eng. • Ferenc Huszár informatics • Anna Fedor bio, ethol • István Zachar bio, evol • Gergő Orbán biophys, Bayesian learn • Máté Lengyel neuro • Szabolcs Számadó bio, evol

  3. 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

  4. The major transitions (JMS & ES, 1995) * * * * * These transitions are regarded to be ‘difficult’

  5. 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

  6. 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?

  7. Three interwoven processes • Note the different time-scales involved • Cultural transmission: language transmits itself as well as other things • A novel inheritance system

  8. 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.

  9. Development of NSL • NSLhas evolved from a system of nonlinguisticgestures into a full sign language with itsown grammar that continues to expand andmature • Theyoungest children in the NSL communityare the most fluent signers • DeafNicaraguan children have created theirown language independently of exposureto a preexisting language structure. • Language is so resilient that itcan be triggered by exposure to a linguisticinput that is highly limited and fragmented—an indication of the fundamental innatenessof grammar  language readiness

  10. 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

  11. 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

  12. 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

  13. Patterns are made up of naturally occurring vocal patterns • Learning to classify by operant conditioning • This is NOT production!

  14. 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!

  15. Genetic analysis of fruitfly behaviour

  16. 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

  17. Nucleotide substitutions in the FOXP2 gene • Bars are nucleotide substitutions • Grey bars indicate amino acid changes • Likely to have been recent target of selection

  18. FOXP2 seems even more interesting • FOXP2 single nucleotide polymorphism (mainly in the 5’ regulatory region) associates withschizophrenia 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)

  19. 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 andFOXP2 expression patterns in human fetal brain are strikingly similar to those in the songbird • including localization to subcorticalstructures that function in sensorimotor integration and the control of skilled, coordinated movement. • The specific co-localization ofFoxP1 and FoxP2 found in several structures in the bird andhuman brain predicts that mutations inFOXP1could also be related to speechdisorders.

  20. More on FOXP2 • fMRI: underactivity of Broca during word generation • repetition of non-wordswith complexarticulatory patterns: the core deficit is one of sequentialarticulation of phonological units • FOXP2 mutation could have beenresponsible for the perfecting of speech • How would it affect the mirror system?

  21. An evaluation of selective scenarios: Trends Ecol. Evol. in press Selective scenarios for the emergence of natural language Szabolcs Számadó and Eörs Szathmáry Collegium Budapest (Institute for Advanced Study), Szentháromság u. 2, H-1014, Budapest, Hungary Corresponding author: Számadó, S. (szamszab@ludens.elte.hu). 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.

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

  23. genes selection development learning behaviour environment The evolutionary approach Impact of evolution on the developmental genetics of the brain!

  24. 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??

  25. Between linguistic input and output…

  26. Transmission dynamics in simulated agents

  27. The most important precedent „the purpose of this paper is toexplore how genes could specify the actual neuronalnetwork functional architectures found in the mammalian brain, such as those found in the cerebral cortex.Indeed, this paper takes examples of some of theactual architectures and prototypical networks foundin the cerebral cortex, and explores how these architectures could be specified by genes which allow the networks when built to implement some of theprototypical computational problems that must besolved by neuronal networks in the brain”

  28. 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

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

  30. Software architecture 519 classes 99267 lines of C++ code ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940.

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

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

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

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

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

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

  37. A Coordination Game Speaker Listener Population Signal -1/1 Decision Decision Environment ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940.

  38. Different types of game • Coodination game (Coop) • Division of Labour (Div) • Prisoners’ dilemma (PD) • Hawk- Dove game (SD) Coodination game Division of Labour Prisoners’ dilemma Hawk - Dove game

  39. other-reporting signals self-reporting signals dishonest signals uninformative signals no signal

  40. 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

  41. other-reporting signals self-reporting signals dishonest signals uninformative signals no signal

  42. Early brains (t:10)Scenario: E1: complementary, E-1:same Visual input Mixed colours indicate input mixing. Audio input Const input or unconnected ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940.

  43. Advanced brain (t:750)Scenario: E1: complementary, E-1:same Mixed colours indicate input mixing Visual input Audio input Constants input or unconnected ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940.

  44. Is there inheritance, despite highly indirect genetic encoding? • Scatter plots for AudioIn, AudioOut, Const, Vision and Decision neurons • Experiments on clones ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940.

  45. Measuring the Heritability of Neural Connectionsin ENGA-Generated Communicating Agents 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. 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. ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940.

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

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

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

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

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