Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

3.6K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.6K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Random walks with stochastic resetting in complex networks: A discrete-time approach.

Chaos (Woodbury, N.Y.)·2025
Same author

Editorial.

Bio Systems·2019
Same author

A study of dependency features of spike trains through copulas.

Bio Systems·2019
Same author

The Gamma renewal process as an output of the diffusion leaky integrate-and-fire neuronal model.

Biological cybernetics·2016
Same author

Cooperative behavior in a jump diffusion model for a simple network of spiking neurons.

Mathematical biosciences and engineering : MBE·2013
Same author

Joint distribution of first exit times of a two dimensional Wiener process with jumps with application to a pair of coupled neurons.

Mathematical biosciences·2013

Related Experiment Video

Updated: Apr 18, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.4K

On firing rate estimation for dependent interspike intervals.

Elisa Benedetto1, Federico Polito, Laura Sacerdote

  • 1Department of Mathematics G. Peano, University of Torino, Via Carlo Alberto 10, 10123, Turin, Italy elisa.benedetto@unito.it.

Neural Computation
|January 21, 2015
PubMed
Summary

This study introduces a new method to estimate the conditional firing rate for neuronal spike trains when interspike intervals are dependent. This approach accurately captures firing patterns missed by traditional methods.

More Related Videos

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.5K
Microelectrode Array Recording of Sinoatrial Node Firing Rate to Identify Intrinsic Cardiac Pacemaking Defects in Mice
09:20

Microelectrode Array Recording of Sinoatrial Node Firing Rate to Identify Intrinsic Cardiac Pacemaking Defects in Mice

Published on: July 5, 2021

3.8K

Related Experiment Videos

Last Updated: Apr 18, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.4K
Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning
11:20

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

Published on: June 2, 2014

12.5K
Microelectrode Array Recording of Sinoatrial Node Firing Rate to Identify Intrinsic Cardiac Pacemaking Defects in Mice
09:20

Microelectrode Array Recording of Sinoatrial Node Firing Rate to Identify Intrinsic Cardiac Pacemaking Defects in Mice

Published on: July 5, 2021

3.8K

Area of Science:

  • Computational Neuroscience
  • Statistical Signal Processing

Background:

  • Traditional instantaneous firing rate analysis fails when interspike intervals (ISIs) are dependent.
  • The conditional firing rate is crucial for understanding spike train dynamics with dependent ISIs.
  • Estimating the conditional firing rate is challenging when the ISI distribution is unknown.

Purpose of the Study:

  • To develop a nonparametric estimator for the conditional instantaneous firing rate.
  • To address the limitations of existing methods for dependent interspike intervals.
  • To provide a reliable method for analyzing complex spike train data.

Main Methods:

  • Proposed a nonparametric estimation method for conditional instantaneous firing rate.
  • Developed an algorithm to assess the reliability of the estimator.
  • Proved the consistency properties of the proposed estimator.
  • Applied the method to simulated data from a stochastic two-compartment model and to experimental in vitro data.

Main Results:

  • The proposed nonparametric estimator effectively captures firing rate dynamics in the presence of dependent interspike intervals.
  • The reliability assessment algorithm confirms the robustness of the estimator.
  • Consistency properties were mathematically proven, ensuring theoretical validity.
  • Successful application to both simulated and experimental neural data.

Conclusions:

  • The developed nonparametric estimator is a valuable tool for analyzing neuronal spike trains with dependent interspike intervals.
  • This method enhances the understanding of neural coding by accurately characterizing firing rates.
  • The approach is applicable to diverse neuroscience research, including computational models and experimental recordings.