Towards the Neural Level
In previous chapters we have developed mathematical models that can reproduce cognitive abilities and can be implemented on serial or certain kinds of parallel computers. In this chapter we will go one step further: we shall investigate the extent to which such models can be linked to the properties of neurones that have been studied experimentally in a number of animals. It is not our task here to present all the physiological details; we merely discuss a few salient features which are decisive for the functioning of neurones within the neural network of a brain.
KeywordsPhase Angle Associative Memory Phase Oscillator Rotate Wave Approximation Pattern Vector
Unable to display preview. Download preview PDF.
- R. Eckhorn, H.J. Reitböck: In Synergetics of Cognition, ed. by H. Haken, M. Stadler, Springer Ser. Syn. Vol. 45 ( Springer, Berlin, Heidelberg 1990 )Google Scholar
- W.J. Freeman: ibid Google Scholar
- C.M. Gray, P. König, A.K. Engel, W. Singer: ibid Google Scholar
- H.J. Reitböck, R. Eckhorn, M. Arndt, P. Dicke: ibid Google Scholar
- Further references may be found in the above contributionsGoogle Scholar
- The main results of these sections are based on unpublished work by the author.Google Scholar
- Y. Kuramoto: Chemical oscillations, waves, and turbulence (Springer, Berlin, 1984) Y. Kuramoto, I. Nishikawa: J. Stat. Phys. 49, 569 (1987)Google Scholar
- C.S. Peskin: Mathematical aspects of heart physiology, Courant Institute of Mathematical Sciences, New York University, New York, 268–278 (1975)Google Scholar
- S.M. Strogatz: From Kuramoto to Crawford: exploring the onset of synchronization in populations of coupled oscillators, Physica D 143 (2000)Google Scholar
- For a general survey on phase synchronization see the papers in the volumes 10, Ns. 10 and 11 (2000) in the Int. J. of Bifurcation and Chaos, on phase synchronization and its applications. Guest editor: Jürgen KurthsGoogle Scholar