• Publications
  • Influence
The Time-Rescaling Theorem and Its Application to Neural Spike Train Data Analysis
TLDR
We describe how the time-rescaling theorem may be used to develop goodness-of-fit tests for both parametric and histogram-based point process models of neural spike trains. Expand
A Spike-Train Probability Model
TLDR
We propose a simple solution to this problem, which is to assume that the time at which a neuron fires is determined probabilistically by, and only by, two quantities: experimental clock time and the elapsed time since the previous spike. Expand
Asymptotic Distribution of P Values in Composite Null Models
Abstract We investigate the compatibility of a null model H 0 with the data by calculating a p value; that is, the probability, under H 0, that a given test statistic T exceeds its observed value.Expand
Controlling the Proportion of Falsely Rejected Hypotheses when Conducting Multiple Tests with Climatological Data
Abstract The analysis of climatological data often involves statistical significance testing at many locations. While the field significance approach determines if a field as a whole is significant,Expand
Statistical issues in the analysis of neuronal data.
TLDR
This article reviews well-established statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Expand
Multiple Indices of Northern Hemisphere Cyclone Activity, Winters 1949–99
The National Centers for Environmental Prediction‐National Center for Atmospheric Research (NCEP‐NCAR) reanalysis is used to estimate time trends of, and analyze the relationships among, six indicesExpand
Statistical smoothing of neuronal data.
The purpose of smoothing (filtering) neuronal data is to improve the estimation of the instantaneous firing rate. In some applications, scientific interest centres on functions of the instantaneousExpand
Spike Train Decoding Without Spike Sorting
  • V. Ventura
  • Computer Science, Medicine
  • Neural Computation
  • 1 April 2008
TLDR
We propose a novel paradigm for spike train decoding, which avoids entirely spike sorting based on waveform measurements. Expand
Spike Count Correlation Increases with Length of Time Interval in the Presence of Trial-to-Trial Variation
TLDR
We show that excess trial-to-trial variation produces spike count correlations that vary with the length of time interval during which the counts are recorded. Expand
Automatic Spike Sorting Using Tuning Information
  • V. Ventura
  • Computer Science, Medicine
  • Neural Computation
  • 1 September 2009
TLDR
We propose a novel approach to spike sorting, which incorporates both waveform information and tuning information obtained from the modulation of firing rates. Expand
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