Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ⁹-tetrahydrocannabinol administration

J Neurosci Methods. 2015 Apr 15;244:136-53. doi: 10.1016/j.jneumeth.2014.07.013. Epub 2014 Jul 30.


Background: Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing.

New method: Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain-computer interfaces and nonlinear neuronal models.

Results: Neurons involved in memory processing ("Functional Cell Types" or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid type-1 receptor (CB1R) partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons.

Comparison with existing methods: WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events.

Conclusion: z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain-computer interfaces.

Keywords: Cannabinoid; Cognition; Delayed non-match to sample; Electrophysiology; Wavelet leaders; Working memory.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Action Potentials / drug effects
  • Algorithms
  • Analysis of Variance
  • Animals
  • Computer Simulation
  • Dose-Response Relationship, Drug
  • Dronabinol / administration & dosage*
  • Electronic Data Processing*
  • Hippocampus / cytology*
  • Memory, Short-Term / drug effects*
  • Models, Neurological
  • Neurons / cytology
  • Neurons / drug effects*
  • Nonlinear Dynamics
  • Psychotropic Drugs / administration & dosage*
  • Rats
  • Rats, Long-Evans


  • Psychotropic Drugs
  • Dronabinol