Statistical inference for assessing functional connectivity of neuronal ensembles with sparse spiking data

IEEE Trans Neural Syst Rehabil Eng. 2011 Apr;19(2):121-35. doi: 10.1109/TNSRE.2010.2086079. Epub 2010 Oct 11.


The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as effective methods for the identification of spiking dependency between neurons. However, unfavorable experimental conditions occasionally results in insufficient data collection due to factors such as low neuronal firing rates or brief recording periods, and in these cases, the standard maximum likelihood estimate becomes unreliable. The present studies compares the performance of different statistical inference procedures when applied to the estimation of functional connectivity in neuronal assemblies with sparse spiking data. Four inference methods were compared: maximum likelihood estimation, penalized maximum likelihood estimation, using either l(2) or l(1) regularization, and hierarchical Bayesian estimation based on a variational Bayes algorithm. Algorithmic performances were compared using well-established goodness-of-fit measures in benchmark simulation studies, and the hierarchical Bayesian approach performed favorably when compared with the other algorithms, and this approach was then successfully applied to real spiking data recorded from the cat motor cortex. The identification of spiking dependencies in physiologically acquired data was encouraging, since their sparse nature would have previously precluded them from successful analysis using traditional methods.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Animals
  • Bayes Theorem
  • Cats
  • Computer Simulation
  • Data Interpretation, Statistical
  • Electrophysiological Phenomena
  • Likelihood Functions
  • Linear Models
  • Logistic Models
  • Models, Neurological*
  • Motor Cortex / physiology
  • Neural Networks, Computer*
  • Neural Pathways / cytology
  • Neural Pathways / physiology*
  • Neurons / physiology*
  • Reproducibility of Results