Maximum likelihood estimation of cascade point-process neural encoding models

Network. 2004 Nov;15(4):243-62.


Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stage. We analyze the estimation of one such model for which this nonlinear step is implemented by a known parametric function; the assumption that this function is known speeds the estimation process considerably. We investigate the shape of the likelihood function for this type of model, give a simple condition on the nonlinearity ensuring that no non-global local maxima exist in the likelihood-leading, in turn, to efficient algorithms for the computation of the maximum likelihood estimator-and discuss the implications for the form of the allowed nonlinearities. Finally, we note some interesting connections between the likelihood-based estimators and the classical spike-triggered average estimator, discuss some useful extensions of the basic model structure, and provide two novel applications to physiological data.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Likelihood Functions
  • Models, Neurological*
  • Motor Cortex / physiology
  • Neural Networks, Computer*
  • Neurons / physiology