title: Quantification and classification of neuronal responses in kernel-smoothed peristimulus time histograms.
abstract: Peristimulus time histograms are a widespread form of visualizing neuronal responses. Kernel convolution methods transform these histograms into a smooth, continuous probability density function. This provides an improved estimate of a neuron's actual response envelope. We here develop a classifier, called the h-coefficient, to determine whether time-locked fluctuations in the firing rate of a neuron should be classified as a response or as random noise. Unlike previous approaches, the h-coefficient takes advantage of the more precise response envelope estimation provided by the kernel convolution method. The h-coefficient quantizes the smoothed response envelope and calculates the probability of a response of a given shape to occur by chance. We tested the efficacy of the h-coefficient in a large data set of Monte Carlo simulated smoothed peristimulus time histograms with varying response amplitudes, response durations, trial numbers, and baseline firing rates. Across all these conditions, the h-coefficient significantly outperformed more classical classifiers, with a mean false alarm rate of 0.004 and a mean hit rate of 0.494. We also tested the h-coefficient's performance in a set of neuronal responses recorded in humans. The algorithm behind the h-coefficient provides various opportunities for further adaptation and the flexibility to target specific parameters in a given data set. Our findings confirm that the h-coefficient can provide a conservative and powerful tool for the analysis of peristimulus time histograms with great potential for future development.
authors.affiliation: California Institute of Technology, Pasadena, California; mrhill@caltech.edu.
authors.forename: Michael R H
authors.initials: MR
authors.lastname: Hill
authors.affiliation: Department of Neurosurgery, University of California, Los Angeles, California; and.
authors.forename: Itzhak
authors.initials: I
authors.lastname: Fried
authors.affiliation: California Institute of Technology, Pasadena, California; Allen Institute for Brain Science, Seattle, Washington.
authors.forename: Christof
authors.initials: C
authors.lastname: Koch
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D006801:Humans; D009474:Neurons / Q000502:physiology*; D059629:Signal-To-Noise Ratio
keywords: action potentials; firing rate; human data; kernel convolution; peristimulus time histogram; signal detection; spike trains