Maximum likelihood estimation of cascade point-process neural encoding models.
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.
Paninski
Liam
L
Gatsby Computational Neuroscience Unit, University College London, UK. liam@gatsby.ucl.ac.uk
D000465:Algorithms; D000818:Animals; D016013:Likelihood Functions; D008959:Models, Neurological; D009044:Motor Cortex / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology