Valérie Ventura

Summary

Affiliation: Carnegie Mellon University
Country: USA

Publications

  1. pmc Accurately estimating neuronal correlation requires a new spike-sorting paradigm
    Valérie Ventura
    Department of Statistics, Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Proc Natl Acad Sci U S A 109:7230-5. 2012
  2. pmc Automatic spike sorting using tuning information
    Valérie Ventura
    Department of Statistics and the Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Neural Comput 21:2466-501. 2009
  3. pmc Traditional waveform based spike sorting yields biased rate code estimates
    Valérie Ventura
    Department of Statistics and Center for the Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Avenue, Baker Hall 132, Pittsburgh, PA 15213, USA
    Proc Natl Acad Sci U S A 106:6921-6. 2009
  4. pmc Spike train decoding without spike sorting
    Valérie Ventura
    Department of Statistics and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, U S A
    Neural Comput 20:923-63. 2008
  5. ncbi request reprint Statistical assessment of time-varying dependency between two neurons
    Valérie Ventura
    Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213 3890, USA
    J Neurophysiol 94:2940-7. 2005
  6. ncbi request reprint Trial-to-trial variability and its effect on time-varying dependency between two neurons
    Valérie Ventura
    Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    J Neurophysiol 94:2928-39. 2005
  7. ncbi request reprint Testing for and estimating latency effects for poisson and non-poisson spike trains
    Valérie Ventura
    Department of Statistics and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Neural Comput 16:2323-49. 2004
  8. ncbi request reprint Statistical issues in the analysis of neuronal data
    Robert E Kass
    Department of Statistics and Center for the Neural Basis of Cognition, 5000 Forbes Ave, Baker Hall 132 Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    J Neurophysiol 94:8-25. 2005
  9. ncbi request reprint Statistical smoothing of neuronal data
    Robert E Kass
    Department of Statistics, Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213 3890, USA
    Network 14:5-15. 2003
  10. ncbi request reprint Spline-based non-parametric regression for periodic functions and its application to directional tuning of neurons
    Cari G Kaufman
    Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Stat Med 24:2255-65. 2005

Collaborators

Detail Information

Publications13

  1. pmc Accurately estimating neuronal correlation requires a new spike-sorting paradigm
    Valérie Ventura
    Department of Statistics, Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Proc Natl Acad Sci U S A 109:7230-5. 2012
    ....
  2. pmc Automatic spike sorting using tuning information
    Valérie Ventura
    Department of Statistics and the Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Neural Comput 21:2466-501. 2009
    ..Finally, we uncover a systematic flaw of spike sorting based on waveform information only...
  3. pmc Traditional waveform based spike sorting yields biased rate code estimates
    Valérie Ventura
    Department of Statistics and Center for the Neural Basis of Cognition, Carnegie Mellon University, 5000 Forbes Avenue, Baker Hall 132, Pittsburgh, PA 15213, USA
    Proc Natl Acad Sci U S A 106:6921-6. 2009
    ..Indeed we show that when spike sorting and tuning-curve estimation are performed in parallel, unbiased estimates of tuning curves can be recovered even from imperfectly sorted neurons...
  4. pmc Spike train decoding without spike sorting
    Valérie Ventura
    Department of Statistics and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, U S A
    Neural Comput 20:923-63. 2008
    ....
  5. ncbi request reprint Statistical assessment of time-varying dependency between two neurons
    Valérie Ventura
    Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213 3890, USA
    J Neurophysiol 94:2940-7. 2005
    ..In a companion paper, we show how this formulation can accommodate latency and time-varying excitability effects, which can confound spike timing effects...
  6. ncbi request reprint Trial-to-trial variability and its effect on time-varying dependency between two neurons
    Valérie Ventura
    Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    J Neurophysiol 94:2928-39. 2005
    ..In data from two V1 neurons we find that highly statistically significant evidence of dependency disappears after adjustment for time-varying trial-to-trial variation...
  7. ncbi request reprint Testing for and estimating latency effects for poisson and non-poisson spike trains
    Valérie Ventura
    Department of Statistics and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Neural Comput 16:2323-49. 2004
    ..It is applicable for Poisson and non-Poisson spike trains via use of the bootstrap. Our estimation method is model free, it is fast and easy to implement, and its performance compares favorably to other methods currently available...
  8. ncbi request reprint Statistical issues in the analysis of neuronal data
    Robert E Kass
    Department of Statistics and Center for the Neural Basis of Cognition, 5000 Forbes Ave, Baker Hall 132 Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
    J Neurophysiol 94:8-25. 2005
    ..Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data...
  9. ncbi request reprint Statistical smoothing of neuronal data
    Robert E Kass
    Department of Statistics, Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213 3890, USA
    Network 14:5-15. 2003
    ..We briefly review additional applications of smoothing with non-Poisson processes and in the joint PSTH for a pair of neurons...
  10. ncbi request reprint Spline-based non-parametric regression for periodic functions and its application to directional tuning of neurons
    Cari G Kaufman
    Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
    Stat Med 24:2255-65. 2005
    ..We compare the new method to a periodic version of smoothing splines and some parametric alternatives and find the new method to be especially valuable when the smoothness of the periodic function varies unevenly across its domain...
  11. ncbi request reprint Spike count correlation increases with length of time interval in the presence of trial-to-trial variation
    Robert E Kass
    Neural Comput 18:2583-91. 2006
    ..The resulting formula for the correlation is able to predict the observed correlation of two neurons recorded from primary visual cortex as a function of interval length...
  12. doi request reprint Single-snippet analysis for detection of postspike effects
    Sagi Perel
    Department of Biomedical Engineering and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, U S A
    Neural Comput 26:40-56. 2014
    ....
  13. pmc Automatic scan test for detection of functional connectivity between cortex and muscles
    Sagi Perel
    Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania
    J Neurophysiol 112:490-9. 2014
    ..The scan test is particularly useful to identify candidate PSEs, which can then be subject to further evaluation by SpTA inspection, and when PSEs are small and visual detection is ambiguous. ..