Estimation of parameters and eigenmodes of multivariate autoregressive models
ACM Transactions on Mathematical Software (TOMS)
Oscillatory behavior for a class of recurrent neural networks with time-varying input and delays
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
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Neuronal population oscillations in the hippocampus have an important effect in the information processing in the brain and the generation of epileptic seizures. In this paper, we investigate the neuronal population oscillations in the hippocampus of epileptic rats in vivo using an empirical mode decomposition (EMD) method. A neuronal population oscillation can be decomposed into several relaxation oscillations, which possess a recovery and release phase, with the different frequencies that ranges from 0 to 600 Hz. The natures of relaxation oscillations at the pre-ictal, seizure onset and ictal states are distinctly different. The analysis of relaxation oscillations show that the gamma wave is a lead relaxation oscillation at the pre-ictal stage, then it moves to beta oscillation or theta oscillation while the ictal stage starts; the fast relaxation oscillations are associated with the slow relaxation oscillations in the CA1 or CA3, in particular, the fast relaxation oscillations are associated on the recovery phase of the slow relaxation oscillations during the pre-ictal interval, however move to the release phase of the slow relaxation oscillations during the ictal interval. Comparison of the relaxation oscillations in CA1 and CA3 shows that the neurons in the CA1 are more active during the epileptic seizures than during the pre-ictal stage. These findings demonstrate that this method is very helpful to decompose neuronal population for understanding the underlying mechanism of epileptic seizures.