Throughout the early history of neurology and neuroscience, most theoretical accounts of brain function have emphasized either aspects of localization or distributed properties [1]. Instead, modern views focus extensively on the structure and dynamics of large-scale neuronal networks, especially those of the cerebral cortex and associated thalamocortical

Abstract. This paper investigates the dynamics of cumulative cultural evolution in a simulation concerning the evolution of language. This simulation integrates the iterated learning model with the Talking Heads experiment in which a population of agents evolves a language to communicate geometrical coloured objects by playing guessing games and transmitting the language from one generation to the next. The results show that cumulative cultural evolution is possible if the language becomes highly regular, which only happens if the language is transmitted from generation to generation. 1

. This chapter introduces evolutionary computation (EC) and genetic algorithms (GAs) for face recognition tasks. We first address eye detection as a visual routine and show how to implement it using a hybrid approach integrating learning and evolution. The goals of the novel architecture for eye detection are twofold: (i) derivation of the saliency attention map using consensus between navigation routines encoded as finite state automata (FSA) evolved using GAs and (ii) selection of optimal features using GAs and induction of DT (decision trees) for possibly classifying as eyes the most salient locations identified earlier. Experimental results, using 30 face images from the FERET data base show the feasibility of our hybrid approach. We then introduce the Optimal Projection Axes (OPA) method for face recognition. OPA works by searching through all the rotations defined over whitened Principal Component Analysis (PCA) subspaces. Whitening, which does not preserve norms, plays a dual ro...

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Simulator : : : : : : : : : : : : : : : : : : : : : : : : : 87 Appendix E. Examples of Human Performance Process Hierarchical Decomposition 92 Appendix F. Scalable Coherent Interfaces 96 Contents (continued) Chapter Page Appendix G. Synopses of Selected High Performance Parallel Machines 98 Appendix H. Glossary of Acronyms 102 References 105 List of Figures Figure Page 1.1 A Holarchy : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 17 2.1 Possible Paths for Human Performance Process Model Creation : : : : : : : 21 6.1 Numerical Aerodynamics Simulation Results for Embarassingly Parallel Benchmarks : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 40 6.2 CM2: Numerical Aerodynamics Simulation Benchmark Results : : : : : : : 41 6.3 Human Performance Process and Architectures : : : : : : : : : : : : : : : : 42 8.1 Heterogeneous Computing Environment : : : : : : : : : : : : : : : : : : : : 50 9.1 High Performance Systems Metrics : : :...

ated between 100,000 and 10,000,000 km [5]. Despite this massive connectivity, cortical networks are exceedingly sparse, with an overall connectivity factor (number of connections present out of all possible) of around 10 #6 . Brain networks are not random, but form highly specific patterns. A predominant feature of brain networks is that neurons tend to connect predominantly with other neurons in local groups. Thus, local connectivity ratios can be significantly higher than those suggested by random topology. Networks in the brain can be analyzed at multiple levels of scale. Within small and localized region of the brain, neurons form characteristic sets of connections, socalled local circuits [6]. For example, neurons forming cortical columns show specific patterns of connectivity between morphologically and pharmacologically distinct classes of cells in different layers. At a higher level of scale, such columns communicate through "tangential" or "horizontal" connections, forming