creators_name: Schierwagen, Andreas creators_id: schierwa@uni-leipzig.de editors_name: Ferrández, J.M. editors_name: Álvarez, J.R. editors_name: de la Paz, F. editors_name: Toledo, F.J. type: confpaper datestamp: 2012-11-25 12:35:22 lastmod: 2013-02-18 15:10:43 metadata_visibility: show title: Complex Neuro-Cognitive Systems ispublished: pub subjects: cog-psy subjects: comp-sci-complex-theory subjects: comp-sci-mach-dynam-sys subjects: neuro-physio full_text_status: public abstract: Cognitive functions such as a perception, thinking and acting are based on the working of the brain, one of the most complex systems we know. The traditional scientific methodology, however, has proved to be not sufficient to understand the relation between brain and cognition. The aim of this paper is to review an alternative methodology – nonlinear dynamical analysis – and to demonstrate its benefit for cognitive neuroscience in cases when the usual reductionist method fails. date: 2011 date_type: published volume: 6686 number: Part I publisher: Springer pagerange: 1-9 refereed: TRUE referencetext: [1] Schierwagen, A.: Brain complexity: analysis, models and limits of understanding. In: J. Mira et al. (Eds.): IWINAC 2009, Part I, LNCS 5601, Springer-Verlag Berlin Heidelberg, pp. 195-204 (2009) [2] Schierwagen, A.: On reverse engineering in the cognitive and brain sciences. Natural Comput. 11 (2012), 141-150 [3] Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE).DARPA / IBM (2008) [4] Markram, H.: The Blue Brain Project. Nature Rev. Neurosci. 7 (2006) 153–160 [5] de Garis, H. et al.: The China-Brain Project: Building China’s Artificial Brain Using an Evolved Neural NetModule Approach. In: PeiWang, Ben Goertzel,and Stan Franklin (Eds.): Proceedings First AGI Conference, IOS Press, Amsterdam,The Netherlands, pp. 107–121 (2008) [6] de Garis, H., Shuoa, C., Goertzel, B., Ruiting, L.: A world survey of artificial brain projects, Part I: Large-scale brain simulations. Neurocomput. 74 (2010) 3–29 [7] Goertzel, B., Ruiting, L., Arel, I., de Garis, H., Chen, S.: World survey of artificial brains, Part II: Biologically inspired cognitive architectures. Neurocomput. 74 (2010), 30–49 [8] Edmonds, B.: Syntactic Measures of Complexity. PhD thesis, University of Manchester (1999) [9] Chu, D., Strand, R., Fjelland, R.: Theories of complexity. Complexity 8 (2003) 19–30 [10] Gershenson, C.: Complexity. arXiv:1003.5947v1 [nlin.AO] [11] Editorial. Complicated is not complex. Nature Biotechnology 17 (1999) 511 [12] Rosen, R.: Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life. Columbia University Press, New York (1991) [13] Rosen, R.: Essays on Life Itself. Columbia University Press, New York (2000) [14] Kitto, K.: High End Complexity. Intern. J. Gen. Syst. 37 (2008) 689–714 [15] Babloyantz, A., Destexhe, A.: Low-dimensional chaos in an instance of epilepsy. Proc. Natl. Acad. Sci. USA, 83 (1986) 3513–3517 [16] Jaeger, H.: Dynamische Systeme in der Kognitionswissenschaft. Kognitionswissenschaft 5 (1996) 151–174 [17] Stam, C.J.: Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field. Clin. Neurophysiol. 116 (2005) 2266-2301 [18] Takens, F.: Detecting strange attractors in turbulence. Lecture Notes Math., Vol. 898, pp. 366–381 (1981) [19] Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (1997) citation: Schierwagen, Andreas (2011) Complex Neuro-Cognitive Systems. [Conference Paper] document_url: http://cogprints.org/8737/1/iwinac11prep.pdf