Journal
JOURNAL OF NEUROPHYSIOLOGY
Volume 113, Issue 4, Pages 1260-1274Publisher
AMER PHYSIOLOGICAL SOC
DOI: 10.1152/jn.00595.2014
Keywords
peristimulus time histogram; kernel convolution; signal detection; action potentials; spike trains; firing rate; human data
Categories
Funding
- Swiss National Science Foundation [PBSKP3-124730]
- G. Harold & Leila Y. Mathers Foundation [09212007]
- Swiss National Science Foundation (SNF) [PBSKP3-124730] Funding Source: Swiss National Science Foundation (SNF)
Ask authors/readers for more resources
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
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available