Loss of frequencies in autocorrelations and a procedure to recover them

Mayank R. Mehta*, Hagai Bergman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


We show that the standard methods, like the autocorrelation of a discrete time series, such as a spike train, can often miss prominent oscillation frequencies. In particular, if a phenomenon involves bursting and oscillations at two or more frequencies, the higher frequencies, with a lesser number of spikes per burst, can be often missed by the conventional methods. It is argued that one should separate the intraburst phenomena from the interburst phenomena by identifying bursts as single events. The distribution of oscillation frequencies should then computed by evaluating the autocorrelation of these events. A method is also developed for situations where intraburst and interburst time scales overlap significantly. It is shown that the correct estimate of strengths of various frequencies is found by using different thresholds for burst identification. The method is applied to data from a neuron in the subthalamic nucleus of a Parkinsonian (MPTP-treated) monkey. The conventional analysis shows that the neuron oscillates at only 6 Hz, whereas the new analysis reveals the presence of an additional, predominant, oscillation frequency of 18 Hz.

Original languageAmerican English
Pages (from-to)65-71
Number of pages7
JournalJournal of Neuroscience Methods
Issue number1-2
StatePublished - Nov 1995

Bibliographical note

Funding Information:
We thank T. Wichmann and M.R. DeLong for sharing the data; E. Nelken for many helpful discussions; and M. Abeles, G. Gerstein, D. Hansel, H. Sompolinsky and E. Vaadia for a critical reading of the manuscript. M.R. Mehta was supported by a post-doctoral fellowship from the Interdisciplinary Center for Neural Computation. Part of the work was supported by the US-Israel binational science foundation and the Israeli academy of Science.


  • Autocorrelation
  • Bursting
  • Hidden frequency
  • Oscillation
  • Parkinson's tremor


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