1. Neuronal evolution and the origins of language: Towards a simulation platform Eörs Szathmáry Eötvös University Collegium Budapest

2. The group Zoltán Szatmáryprogramming, neuro Péter Ittzésprogramming, bio Péter Ittzésprogramming, bio Máté Vargaprogramming, elect. eng. Máté Vargaprogramming, elect. eng. Ferenc Huszárinformatics Ferenc Huszárinformatics Anna Fedorbio, ethol Anna Fedorbio, ethol István Zacharbio, evol István Zacharbio, evol Gergő Orbánbiophys, Bayesian learn Gergő Orbánbiophys, Bayesian learn Máté Lengyelneuro Máté Lengyelneuro Szabolcs Számadóbio, evol Szabolcs Számadóbio, evol SUPPORTED BY ECAGENTS

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. Some general lessons drawn Emergence of novel inheritance system Holistic  digital BEWARE! Limited heredity  unlimited heredity Solution of the cooperation problem is needed Unlimited heredity allows CUMULATIVE selection

6. Unique transitions are difficult –Genetic code –Eukaryotic cell –Eukaryotic sex –Language Objective and subjective difficulty Limitation by selection Limitation by genetic variation

7. Recruitment (predaptation) is fine, except it is unlikely to give optimal solutions Initial engulfment of bacteria, BUT… Hundreds of mutations must have gone to fixation!!!

8. The ‘momentum’ of evolution IF a trait is useful (functional) AND IF there is genetic variation for it AND IF it is not perfect to start with, THEN we can expect (some) improvement through evolution by natural selection!

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

10. Trends Ecol. Evol. (2006) A critical examination of ideas

11. Theories/Questions 123456 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

12. An educated guess The origin of language had to do possibly with a combination of –Language as a mental tool –Gesturing –Tool making –Hunting

13. The coevolutionary ladder cooperation language

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

15. 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!

16. One method of finding out (within ECAgents) Simulated dynamics of interacting agents Agents have a “nervous system” It is under partial genetic control Selection is based on learning performance for symbolic and syntactical tasks If successful, look and reverse engineer the emerging architectures HOW GENES RIG THE NETWORKS??

17. 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”

18. 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, many more synapses

19. Summary of our efforts In: Nehaniv, C., Cangelosi, A & Lyon, C. (2006) Origin of Communication, in press. Springer-Verlag

20. 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

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

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

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

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

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

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

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

28. Different 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 -1Environment 1 Coop (-1)Coop (1) 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)15 D(-1)03 D(1)D(-1) D(1)5 D(-1)03 D(1)D(-1) D(1)05 D(-1)50 D(1)D(-1) D(1)51 D(-1)00 Coodination game Division of Labour Prisoners’ dilemma Hawk - Dove game

29. other-reporting signals self-reporting signals dishonest signalsuninformative signals no signal

30. 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 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

31. other-reporting signals self-reporting signals dishonest signalsuninformative signals no signal

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

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

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

35. ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940. 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 AudioIn0.8689 AudioOut0.8708 Const0.8696 Decision0.8123 Vision0.8428 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.

36. ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940. Is there inheritance, or only council of the elders? The increase with age of time The code of individuals in time Green lines: individual living still the end of the simulation Red: birth events

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

38. ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940. A minimalist version of the naming game 2 objects Agents have two individual „concepts” (bit strings of length 2) One agent signals the other if shown an object Success of communication is measured in terms of fitness Learning is indispensable

39. ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940. Flow chart of the naming game Mother nature Concept Speaker visual Signal ouput Listener ouput Decision Concept?

40. ECAgents: project founded by the Future and Emerging Technologies program (IST-FET) of the European Community under EU R&D contract IST1940. What is ENGA good for? To test (some) ideas about language evolutionary scenarios Are certain suggested preadaptation ideas better than others? Can you select for recursion? How? Put the networks into robots! A USER-FRIENDLY PLATFORM IS TO BE RELEASED