Complex Neuro-Cognitive Systems

Schierwagen, Andreas (2011) Complex Neuro-Cognitive Systems. [Conference Paper]

Full text available as:

PDF - Accepted Version


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.

Item Type:Conference Paper
Subjects:Psychology > Cognitive Psychology
Computer Science > Complexity Theory
Computer Science > Dynamical Systems
Neuroscience > Neurophysiology
ID Code:8737
Deposited By: Schierwagen, Professor Andreas
Deposited On:25 Nov 2012 12:35
Last Modified:18 Feb 2013 15:10

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

[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)


[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)


Repository Staff Only: item control page