%0 Journal Article %1 D1992 %A D, Hansel %A H., Sompolinsky %D 1992 %J Physical review letters %K Neuronale modelle %P 718-721 %T Synchronization and computation in a chaotic neural network %V 68 %X Chaos generated by the internal dynamics of a large neural network can be correlated over large spatial scales. Modulating the spatial coherence of the chaotic fluctuations by the spatial pattern of the external input provides a robust mechanism for feature segmentation and binding; which cannot be accomplished by networks of oscillators with local noise. This is demonstrated by an investigation of synchronized chaos in a network model of bursting neurons responding to an inhomogeneous stimulus.

@article{D1992, abstract = {Chaos generated by the internal dynamics of a large neural network can be correlated over large spatial scales. Modulating the spatial coherence of the chaotic fluctuations by the spatial pattern of the external input provides a robust mechanism for feature segmentation and binding; which cannot be accomplished by networks of oscillators with local noise. This is demonstrated by an investigation of synchronized chaos in a network model of bursting neurons responding to an inhomogeneous stimulus.}, added-at = {2012-01-27T14:10:42.000+0100}, author = {D, Hansel and H., Sompolinsky}, biburl = {https://www.bibsonomy.org/bibtex/29069152d014a17b8b0c9baafe09e6bb3/muhe}, file = {Synchronization and computation in a chaotic neural network.pdf:1992\\Synchronization and computation in a chaotic neural network.pdf:PDF}, interhash = {635ec4802ffbe966eaccc6e03f72a4f2}, intrahash = {9069152d014a17b8b0c9baafe09e6bb3}, journal = {Physical review letters}, keywords = {Neuronale modelle}, owner = {Mu}, pages = {718-721}, pdf = {English\没什么用的\Synchronization and computation in a chaotic neural network.pdf}, timestamp = {2012-01-27T14:10:48.000+0100}, title = {Synchronization and computation in a chaotic neural network}, volume = 68, year = 1992 }