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One of my research interests is in simulating evolution of body and brain/mind modularity in complex organisms by means of artificial neural networks and genetic algorithms (Calabretta & Neirotti, 2015; Calabretta & Parisi, 2005; Wagner, Mezey & Calabretta, 2005; Calabretta et al., 2000). In 1997 I spent one sabbatical year as post-doctoral fellow at Yale University Department of Biology and in 2000 and 2002 came back to Yale as visiting fellow at the Department of Ecology and Evolutionary Biology and of Psychology. I collaborate with Gunter Wagner (founding chair of the Department of Ecology and Evolutionary Biology) and Frank Keil (Faculty of Psychology, editor of The MIT Encyclopedia of Cognitive Sciences, MIT Press, 1999).

"The existence of modules is recognized at all levels of the biological hierarchy. In order to understand what modules are, why and how they emerge and how they change, it would be necessary to start a joint effort by researchers in different disciplines (evolutionary and developmental biology, comparative anatomy, physiology, neuro- and cognitive science). This is made difficult by disciplinary specialization. [...] we claim that, because of the strong similarities in the intellectual agenda of artificial life and evolutionary biology and of their common grounding in Darwinian evolutionary theory, a close interaction between the two fields could easily take place. Moreover, by considering that artificial neural networks draw an inspiration from neuro- and cognitive science, an artificial life approach to the problem could theoretically enlarge the field of investigation." (Calabretta et al., 1998)

A general definition of modularity and nonmodularity in neural networks can be the following: "modular systems can be defined as systems made up of structurally and/or functionally distinct parts. While non-modular systems are internally homogeneous, modular systems are segmented into modules, i.e., portions of a system having a structure and/or function different from the structure or function of other portions of the system. [...] In a nonmodular architecture one and the same connection weight may be involved in two or more tasks. In a modular architecture each weight is always involved in a single task: Modules are sets of 'proprietary' connections that are only used to accomplish a single task." (Calabretta & Parisi, 2005, Fig. 14.4; see also Calabretta et al., 2003).

Recently Jose' B. Pereira-Leal and Sarah A. Teichmann of the 'Laboratory of Molecular Biology, Structural Studies Division' at Cambridge University (the so called laboratory of Nobel laureates), provided strong support for Calabretta et al.'s (2000) hypothesis on the evolution of modularity:

"Duplication of modules has been shown to be an efficient mechanism for the generation of functional innovation in the field of artificial intelligence, but has not been studied in biological networks. Therefore, we ask whether module duplication occurs in cellular networks. We developed a generic framework for the analysis of module duplication, and use it in a large-scale analysis of Saccharomyces cerevisiae protein complexes. [...] The mechanisms that underlie the evolution of functional modules are largely unknown. Theoretical simulations in neural networks have shown that duplication and specialization of en-tire modules is an effective mode of network growth (Calabretta et al. 1998, 2000). [...] These results strongly support the view that complex duplication is a mechanism by which evolution generates functional specialization. This extends what has been found previously for duplication of individual genes (Prince and Pickett 2002), and is in agreement with the behavior of artificial systems such as neural networks (Calabretta et al. 2000)" (Pereira-Leal & Teichnmann, 2006, pp. 552 and 557).

Other simulation results, for the first time, revealed the existence of genetic interference, a new population genetic mechanism that is independent from the network architecture. Our simulations clearly show that genetic interference reduces the evolvability of visual neural networks and that sexual reproduction can at least partially solve the problem of genetic interference. It was shown that entrusting the task of finding the neural network architecture to evolution and that of finding the network connection weights to learning is a way to completely avoid the problem of genetic interference. On the basis of this evidence, it is possible to formulate a new hypothesis on the origin of structural modularity, and thus to overcome the traditional dichotomy between innatist and empiricist theories of mind (see Calabretta, 2007).

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[Here you will find my full short biography;
here and below you will find the list of my scientific publications (most of all are available on line)
here you will find the citations received by my papers]

Projects
	Evolving modularity: studying the evolution of organism modular design by using Artificial Life simulations (Raffaele Calabretta, Andrea Di Ferdinando, Frank Keil, Domenico Parisi, Gunter P. Wagner)

Links	Artificial Life
	Journals: Artificial Life, Adaptive Behavior; Santa Fe Institute: Current Research Projects
	Cognitive Science
	The Cognitive Science Society; Journals: Cognitive Science, Cognition, Cogntive Systems Research
	Neuro-science Laboratories and Seminars
	Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MA (Leslie Ungerleider) Laboratory of Neuropsychology, National Ins NIH Neuroscience Seminar Series

Workshops

Modularity - Understanding the Development and Evolution of Complex Natural Systems, Altenberg Workshops in Theoretical Biology, Konrad Lorenz Institute for Evolution and Cognition Research, Altenberg (Austria), October 26-29, 2000. (See Callebaut & Rasskin-Gutman, 2005)

Modularity in development and evolution, symposium, Hanse Institute for Advanced Study, Delmenhorst (Germany), May 11-14, 2000.

Modularity of animal form: beyond homology and analogy, workshop, Friday Harbor Laboratories, September 5-8, 1997

Bibliography
	Altenberg, L. 1995. Genome growth and the evolution of the genotype-phenotype map. In W. Banzhaf and F. H. Eeckman (Eds.), Evolution and biocomputation. Computational models of evolution. Berlin: Springer Verlag. [abstract, pdf]
	Ancel, L. W. and Fontana, W. (in press). Evolutionary Lock-in and the Origin of Modularity in RNA Structure. In W. Callabaut & D. Rasskin-Gutman (Eds.), Modularity. Understanding the development and evolution of complex natural systems, The MIT Press, Cambridge, MA. [Preprint]
	Barrett, H. C., and Kurzban, R. (2006). Modularity in cognition: Framing the debate. Psychological Review, 113, 628-647. [Full text]
	Bates, E. (1994). Modularity, domain specificity and the development of language. Discussions in Neuroscience, 10:136-149. [pdf]
	Brooks, R. A. (1986). A robust layered control system for a mobile robot. IEEE Journal of Robotics and Automation 2:14-23.
	Calabretta, R. & Neirotti, J. (2015). Adaptive agents in changing environments: the role of modularity. Neural Processing Letters 42(2), 257-274. doi:10.1007/s11063-014-9355-8 [abstract; pdf]
	Calabretta, R. (2013). A novel's structure as a complex system. International Journal of Creativity & Problem Solving 23(2), 113-120.
	Calabretta, R. (2010). Doparie, dopo le primarie. Diario di un elettore errante alla ricerca della felicita'. Nutrimenti editore, Rome. [A modular scientific novel (about participatory democracy and emotions) that conveys scientific theories and results (in Italian)]
	Calabretta, R. (2010). Evolution, (sequential) learning and generalization in modular and nonmodular visual neural networks. In Graeme Ruxton & Colin Tosh (Eds.), Modelling Perception with Artificial Neural Networks. Cambridge University Press, Cambridge, UK.
	Calabretta, R. (2010). A hypertextual novel that dramatizes the process of its creation and proposes techniques to increase creativity. Biological Theory (MIT Press) 5(2), 102-105. [pdf]
	Calabretta, R., Di Ferdinando, A., Parisi, D., Keil, F. C. (2008). How to learn multiple tasks. Biological Theory (MIT Press) 3(1), 30-41.[pdf] [draft: doc, pdf]
	Calabretta, R. (2007, second edition). Il film delle emozioni. Alberto Gaffi Editore, Rome. [A modular scientific novel (about emotions) that conveys scientific theories and results (in Italian)] [pdf]
	Calabretta, R. (2007). Genetic interference reduces the evolvability of modular and nonmodular visual neural networks. Philosophical Transactions of The Royal Society B: Biological Sciences 362(1469), 403-10. (invited paper) [abstract full text pdf] [draft: doc, pdf]
	Calabretta, R. & Parisi, D. (2005). Evolutionary Connectionism and Mind/Brain Modularity. In W. Callabaut & D. Rasskin-Gutman (Eds.), Modularity. Understanding the development and evolution of complex natural systems, pp. 309-330. The MIT Press, Cambridge, MA. [pdf] [draft: doc, pdf; updated June 4, 2001]
	Calabretta, R., Di Ferdinando, A., & Parisi, D. (2004). Ecological neural networks for object recognition and generalization. Neural Processing Letters 19, 37-48 [pdf] [draft: doc, pdf]
	Calabretta, R., Di Ferdinando, A., Wagner, G. P. & Parisi, D. (2003). What does it take to evolve behaviorally complex organisms? BioSystems 69, 245-262 [pdf] [draft: doc, pdf]
	Calabretta, R., Nolfi, S., Parisi, D. & Wagner, G. P. (2000). Duplication of modules facilitates the evolution of functional specialization. Artificial Life 6:69-84. [pdf] [draft: abstract, pdf]
	Calabretta, R., Nolfi, S., Parisi, D., and Wagner, G. P. (1998a). Emergence of functional modularity in robots. In Pfeifer, R., Blumberg, B., Meyer, J.-A., and Wilson, S.W. (Eds.), From Animals to Animats 5, pp. 497-504. The MIT Press, Cambridge, MA. [pdf] [draft: abstract, pdf]
	Calabretta, R., Nolfi, S., Parisi, D., and Wagner, G. P. (1998b). A case study of the evolution of modularity: towards a bridge between evolutionary biology, artificial life, neuro- and cognitive science. In Adami, C., Belew, R. Kitano, H., and Taylor, C. (Eds.), Proceedings of the Sixth International Conference on Artificial Life, pp. 275-284. The MIT Press, Cambridge, MA.[pdf] [draft: abstract, pdf]
	Callabaut, W. & Rasskin-Gutman, D. (2005, 2007, 2009). Modularity. Understanding the development and evolution of complex natural systems. Cambridge, MA: MIT Press.
	Carruthers, P. (2003). Is the mind a system of modules shaped by natural selection? In: C. Hitchcock (Ed.), Contemporary Debates in the Philosophy of Science. Blackwell, Oxford.
	Chalmers, D. J. (?). A Computational Foundation for the Study of Cognition. Unpublished paper; parts of it have appeared as "On Implementing a Computation" in Minds and Machines (1994). [pdf]
	Cho, S-B. and Shimohara, K. (1997). Emergence of Structure and Function in Evolutionary Modular Neural Networks. In Husbands, P. and Harvey, I., editors, Proceedings of Fourth European Conference on Artificial Life. The MIT Press, Cambridge, MA.
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	Clune, J., Mouret, J.-B., Lipson, H. (2013). The evolutionary origins of modularity. Proceedings of the Royal Society B 280: 20122863. [pdf]
	Cosmides, L. and Tooby, J. (1994). The evolution of domain specificity: the evolution of functional organization. In Hirschfeld, L. A., and Gelman, S. A. (Eds.), Mapping the Mind: Domain Specificity in Cognition and Culture. The MIT Press, Cambridge, MA.
	Di Ferdinando, A., Calabretta, R., & Parisi, D. (2001). Evolving modular architectures for neural networks. In R. French & J. Sougne' (Eds.), Proceedings of the Sixth Neural Computation and Psychology Workshop Evolution, Learning, and Development, pp. 253-262, London: Springer Verlag. [pdf] [draft: abstract, pdf, doc]
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	Farah, M. J. (2014), Brain Images, Babies, and Bathwater: Critiquing Critiques of Functional Neuroimaging. Hastings Center Report, 44: S19–S30. doi: 10.1002/hast.295 [html, pdf]
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	Fodor, J. (2000). The Mind Doesn't Work That Way:The Scope and Limits of Computational Psychology. The MIT Press, Cambridge, MA. First chapter
	Frazier, Lyn (1999). Modularity and Language. In R. A. Wilson and F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences, pp. 558-560. The MIT Press, Cambridge, MA.
	French, R. M. (1999). A review of catastrophic forgetting in connectionist networks. Trends in Cognitive Sciences, 3(4), 128-135.
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	Karmiloff-Smith, A. (1999). Modularity of mind. In R. A. Wilson and F. C. Keil (Eds.), The MIT encyclopedia of the cognitive sciences, pp. 558-560. The MIT Press, Cambridge, MA.
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	Marcus, G. F. (1998). Rethinking eliminative connectionism. Cognitive Psychology.
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	Marcus, G. F., Vijayan, S., Bandi Rao, S. and Vishton, P.M. (1999). Rule learning in seven-month-old infants. Science.
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	Nolfi, S. (1997). Using emergent modularity to develop control systems for mobile robots. Adaptive Behavior, 5:343-363.
	Nolfi, S., Baldassarre G., Calabretta R., Hallam J., Marocco D., Meyer J-A., Parisi, D. (2006). From animals to animats 9: Proceedings of the Ninth International Conference on Simulation of Adaptive Behaviour, LNAI. Volume 4095. Springer Verlag, Berlin.
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	Wagner, G. P. (2014). Homology, genes, and evolutionary innovation. Princeton University Press. [introduction]
	Wagner, G. P., Mezey, J. & Calabretta, R. (2005). Natural selection and the origin of modules. In W. Callabaut & D. Rasskin-Gutman (Eds.), Modularity. Understanding the development and evolution of complex natural systems, pp. 33-49. Cambridge, MA: MIT Press. [pdf] [draft: doc, pdf]
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If you think of anything else that could be added to this list, please e-mail me

Raffaele Calabretta
Institute of Cognitive Sciences and Technologies
Italian National Research Council (C.N.R.)
Email: raffaele.calabretta@istc.cnr.it

The Modularity Home Page ('la Home Page della Modularita'): http://laral.istc.cnr.it/rcalabretta/modularity.html

Page Creation: September 19, 2000

Last Update: 08/08/2016
Page Creation: December 2001