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The Neuronal Replicator Hypothesis Chrisantha Fernando & Eors Szathmary CUNY, December 2009 1Collegium Budapest (Institute for Advanced Study), Budapest, Hungary for Computational Neuroscience and Robotics, Sussex University, UK 3MRC National Institute for Medical Research, Mill Hill, London, UK 4Parmenides Foundation, Kardinal-Faulhaber-Strase 14a, D-80333 Munich, Germany 5Institute of Biology, Eötvös University, Pázmány Péter sétány 1/c, H-1117 Budapest, Hungary 2Centre Visiting Fellow MRC National Institute for Medical Research London Post-Doc Center for Computational Neuroscience and Robotics Sussex University Marie Curie Fellow Collegium Budapest (Institute for Advanced Study) Hungary The Hypothesis • Evolution by natural selection takes place in the brain at rapid timescales and contributes to solving cognitive/behavioural search problems. • Our background is in evolutionary biology/the origin of non-enzymatic template replication/evolutionary robotics/computational neuroscience. Outline • Limitations of some proposed search algorithms, e.g. • Reward biased stochastic search • Reinforcement Learning • How copying/replication of neuronal data structures can alleviate these limitations. • Mechanisms of neuronal replication • Applications and future work Simple Search Tasks • Behavioural and neuropsychological learning tasks can be solved by stochastic-hill climbing • Stroop Task • Wisconsin Card Sorting Task (WCST) • Instrumental Conditioning in Spiking Neural Networks • Simple inverse kinematics problem Stochastic HillClimbing • • • • • • Initially P(xi = 1) = 0.5, Initial reward = 0 0.5 0.5 0.5 0.5 0.5 Make random change to P Generate M examples of binary strings 0.5 0.5 0.5 0.5 0.5 Calculate reward If r(t) > r(t-1), keep changes of P, else revert to previous P values. One solution, change solution, keep good changes, loose bad changes. 0.8 0.5 0.5 0.4 0.5 Can get stuck on local optima Stroop Task Green Red Blue Purple Blue Purple Blue Purple Red Green Purple Green Name the colour of the words. dW = Reward x pre x post Decreased reward -> Instability in workspace Dehaene et al, 1998 WCST • Each card has several “features”. Subjects must sort cards according to a feature (color, number, shape, size). • Rougier et al 2005. PFC weights stabilised if expected reward obtained, destabilised if expected reward not obtained, i.e. TD learning Instrumental Conditioning In a spiking neural net • Simple spiking model • Random connections • STDP • Delayed reward • Eligibility traces • Synapse selected Izhikevich 2007 • Simple spiking model STDP Time tpre Time tpost Interval = Tpost - Tpre Time tpost Time tpre Interval = Tpost - Tpre A simple 2D inverse kinematics problem Reinforcement Learning • • • For large problems a tabular representation of stateaction pairs is not possible. How does compression of state representation occur? Function approximation Domain-specific knowledge provided by the designer, e.g. TD-Gammon was dependent on Tesauro’s skillful design of a non-linear multilayered neural network, used for value function approximation in the Backgammon domain consisting of approximately 1020 states” p20 [51]. So far… • SHC works on simple problems • RL is a sophisticated kind of SHC • In order for RL/SHC to work, action/value representations must fit the problem domain. • RL doesn’t explain how appropriate data-structures/representations arise. Large search space so random search or exhaustive search not possible. Representation critical local optima. Requires internal sub-goals, no explicit reward. What neural mechanisms underlie complex search? What is natural selection? 1. multiplication 2. heredity 3. variability Some hereditary traits affect survival and/or fertility Natural selection reinvented itself Evolutionary Computation • Solving problems by EC also requires decisions about genetic representations • And about fitness functions • For example, we use EC to solve the 10 coins problem Fitness function • Convolution of desired inverted triangle over grid • Instant fitness = number of coins occupying he inverted triangle template • An important question is how such fitness functions (subgoals/goals) could themselves be bootstrapped in cognition. ichael Ollinger, Parmenides Foundation, Munich Structuring Phenotypic Variation • Natural Selection can act on • genetic representations • variability properties (genetic operators, e.g mutation rates) Variation in Variability A Improvement of representations for free… B Non-trivial Neutrality g1 ed 1 p ed 2 g2 Adapted from Toussaint 2003 Population Search • Natural selection allows redistribution of search resources between multiple solutions. • We propose that multiple (possibly interacting) solutions to a search problem exist at the same time in the neuronal substrate. B AA A B C D A B C D D C B AA D C AA B B DD A B C D A B C D C D A CC Waste A D’ D’’ D D’’’ A B D’ D’’ D’’’ D Can units of selection exist in the brain? • We propose 3 possible mechanisms • Copying of connectivity patterns • Copying of bistable activity patterns • Copying of spatio-temporal spike patterns & explicit rules Copying of connectivity patterns How to copy small neuronal circuits DNA neuronal network STDP and causal inference With error correction and sparse activation 1 + 1 Evolution Stratergy Copying of bistable activity patterns 1 bit copy Hebbian Learning can Structure Exploration Distributions - Search in biased towards previous local optima The Origin of Heredity in Neuronal Networks. Genotype 2 CM2= M1 C = M2-1M1 Phenotype 2 M2 C Genotype 1 Phenotype 1 M1 Non-local, e.g. requires ATA Stochastic hill climbing can select for neuronal template replication Genotype 2 M2 C Error Genotype 1 M1 E Copying of Spatiotemporal Spike Patterns & Explicit Rules Spatiotemporal spike patterns ABA vs ABB DD vs DS Visual shift-invariance mechanisms applied to linguistics. APPLICATIONS • Evolution of Predictors (Feed-forward Models/Emulators/Bayesian Causal Networks). • First derivative of predictability • Evolution of Linguistic Construction • Evolution of controllers for robot handmanipulation • Evolution of Productions in ACTR/Copycat • Evolution of representations and search for insight problem solving. Larranaga et al, 1996. Structure Learning of Bayesian Networks by Genetic Algorithms. Kemp & Tenenbaum, 2008. The discovery of structural form. Operations to construct a BN Luc Steels et al, Sony Labs Istvan Zacher Collegium Budapest (Institute for Advanced Study) K K(v) 0 1 S(p) C(p) K S C S C 0 1 K K(v) 0 1 S(p) C(p) K S C S C 0 1 Rules K K(v) 0 1 S(p) C(p) K S C S C 0 1 Rules K K(v) 0 1 S(p) C(p) K S C S C 0 1 Rules K K(v) 0 1 S(p) C(p) K S C 0 1 Rules S C KC K K(v) 0 1 S(p) C(p) K S C 0 1 Rules S C KC S K S C Rules K S C KC S K K(v) 0 1 S(p) C(p) K S C 0 1 Rules S C KC S Helge Ritter, Bielefeld, Germany Thanks to Richard Goldstein Richard Watson Dan Bush Eugine Izhikevich Phil Husbands Luc Steels K.K. Karishma Anna Fedor, Zoltan Szatmary, Szabolcs Szamado, Istvan Zachar Anil Seth