Detecting non-linearities in neuro-electrical signals: a study of synchronous local field potentials

Abstract The question of the presence and detection of non-linear dynamics and possibly low-dimensional chaos in the brain is still an open question, with recent results indicating that initial claims for low dimensionality were faulted by incomplete statistical testing. To make some progress on this question, our approach was to use stringent data analysis of precisely controlled and behaviorally significant neuroelectric data. There are strong indications that functional brain activity is correlated with synchronous local field potentials. We examine here such synchronous episodes in data recorded from the visual system of behaving cats and pigeons. Our purpose was to examine under these ideal conditions whether the time series showed any evidence of non-linearity concommitantly with the arising of synchrony. To test for non-linearity we have used surrogate sets for non-linear forecasting, the false nearest strands method, and an examination of deterministic vs stochastic modeling. Our results indicate that the time series under examination do show evidence for traces of non-linear dynamics but weakly, since they are not robust under changes of parameters. We conclude that low-dimensional chaos is unlikely to be found in the brain, and that a robust detection and characterization of higher-dimensional non-linear dynamics is beyond the reach of current analytical tools.