Abstract
We present Cedar, a software framework for the implementation and simulation of embodied cognitive models based on Dynamic Field Theory (DFT). DFT is a neurally inspired theoretical framework that integrates perception, action, and cognition. Cedar captures the power of DFT in software by facilitating the process of software development for embodied cognitive systems, both artificial and as models of human cognition. In Cedar, models can be designed through a graphical interface and interactively tuned. We demonstrate this by implementing an exemplary robotic architecture.
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Amari, S.-I.: Dynamics of pattern formation in lateral-inhibition type neural fields. Biological Cybernetics 27, 77–87 (1977)
Bernardet, U., Verschure, P.F.M.J.: iqr: A tool for the construction of multi-level simulations of brain and behaviour. Neuroinformatics 8(2), 113–134 (2010)
Bicho, E., Mallet, P., Schöner, G.: Target representation on an autonomous vehicle with low-level sensors. International Journal of Robotics Research 19(5), 424–447 (2000)
Clark, A.: An embodied cognitive science? Trends in Cognitive Sciences 3(9), 345–351 (1999)
Eliasmith, C., Anderson, C.H.: Neural engineering: Computation, representation, and dynamics in neurobiological systems. MIT Press (2004)
Erlhagen, W., Schöner, G.: Dynamic Field Theory of movement preparation. Psychological Review 109(3), 545–572 (2002)
Kloeden, P.E., Platen, E.: Numerical solution of stochastic differential equations, 2nd edn. Springer (1999)
Metta, G., Fitzpatrick, P., Natale, L.: YARP: Yet another robot platform. International Journal on Advanced Robotics Systems 3(1), 43–48 (2006)
Richter, M., Sandamirskaya, Y., Schöner, G.: A robotic architecture for action selection and behavioral organization inspired by human cognition. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2457–2464. IEEE Press (2012)
Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing: Explorations in the microstructure of cognition. Foundations, vol. 1. MIT Press, Cambridge (1986)
Schöner, G.: Dynamical systems approaches to cognition. In: Cambridge Handbook of Computational Cognitive Modeling, pp. 101–126. Cambridge University Press (2008)
Schutte, A.R., Spencer, J.P., Schöner, G.: Testing the Dynamic Field Theory: Working memory for locations becomes more spatially precise over development. Child Development 74, 1393–1417 (2003)
Stewart, T.C., Tripp, B., Eliasmith, C.: Python scripting in the Nengo simulator. Frontiers in Neuroinformatics 3 (2009)
Zibner, S.U., Faubel, C., Iossifidis, I., Schöner, G.: Dynamic Neural Fields as building blocks for a cortex-inspired architecture of robotic scene representation. IEEE Transactions on Autonomous Mental Development 3(1) (2011)
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Lomp, O., Zibner, S.K.U., Richter, M., Rañó, I., Schöner, G. (2013). A Software Framework for Cognition, Embodiment, Dynamics, and Autonomy in Robotics: Cedar . In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_60
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DOI: https://doi.org/10.1007/978-3-642-40728-4_60
Publisher Name: Springer, Berlin, Heidelberg
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