Measuring Instantaneous Frequency of Local Field Potential Oscillations using the Kalman Smoother

Rhythmic local field potentials (LFPs) arise from coordinated neural activity. Inference of neural function based on the properties of brain rhythms remains a challenging data analysis problem. Algorithms that characterize non-stationary rhythms with high temporal and spectral resolution may be useful for interpreting LFP activity on the timescales in which they are generated. We propose a Kalman smoother based dynamic autoregressive model for tracking the instantaneous frequency (iFreq) and frequency modulation (FM) of noisy and non-stationary sinusoids such as those found in LFP data. We verify the performance of our algorithm using simulated data with broad spectral content, and demonstrate its application using real data recorded from behavioral learning experiments. In analyses of ripple oscillations (100–250 Hz) recorded from the rodent hippocampus, our algorithm identified novel repetitive, short timescale frequency dynamics. Our results suggest that iFreq and FM may be useful measures for the quantification of small timescale LFP dynamics.National Institutes of Health (U.S.) (NIH/NIMH R01 MH59733)National Institutes of Health (U.S.) (NIH/NIHLB R01 HL084502)Massachusetts Institute of Technology (Henry E. Singleton Presidential Graduate Fellowship Award

Etude de la dynamique des reactions de l'aluminium avec O_2, CO_2 et SO_2 au moyen de faisceaux moleculaires supersoniques croises pulses

SIGLECNRS T Bordereau / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc