Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Choosing Between z and t Distribution

Choosing Between z and t Distribution

The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

Estimating Population Mean with Unknown Standard Deviation

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Student t Distribution

Student t Distribution

The population standard deviation is rarely known in many day-to-day examples of statistics. When the sample sizes are large, it is easy to estimate the population standard deviation using a confidence interval, which provides results close enough to the original value. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
The Student t distribution was developed by William S. Goset (1876–1937) of the...

You might also read

Related Articles

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

Sort by

Same author

UP-DOWN states and ripples differentially modulate membrane potential dynamics across DG, CA3, and CA1 in awake mice.

eLife·2022

Same author

Brain State Dependence of Hippocampal Subthreshold Activity in Awake Mice.

Cell reports·2017

Same author

Nanofabricated Neural Probes for Dense 3-D Recordings of Brain Activity.

Nano letters·2016

Same author

Spatial tuning and brain state account for dorsal hippocampal CA1 activity in a non-spatial learning task.

eLife·2016

Same author

Membrane Potential Dynamics of CA1 Pyramidal Neurons during Hippocampal Ripples in Awake Mice.

Neuron·2016

Same author

Structured Variability in Purkinje Cell Activity during Locomotion.

Neuron·2015

Search research articles

Related Experiment Video

Updated: Feb 27, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

Model-based spike sorting with a mixture of drifting t-distributions.

Kevin Q Shan¹, Evgueniy V Lubenov¹, Athanassios G Siapas¹

¹Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, United States; Division of Engineering and Applied Science, California Institute of Technology, Pasadena, United States.

Journal of Neuroscience Methods

|June 28, 2017

Summary

A new spike sorting method using a mixture of drifting t-distributions improves analysis of chronic extracellular recordings. This approach accurately measures unit isolation quality and handles cluster drift effectively.

Keywords:

Chronic recording Cluster drift Clustering Heavy tails Spike overlap Unit isolation metrics

More Related Videos

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

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

Published on: March 25, 2014

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Related Experiment Videos

Last Updated: Feb 27, 2026

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Published on: February 10, 2017

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

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

Published on: March 25, 2014

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Systems Neuroscience
Computational Neuroscience

Background:

Chronic extracellular recordings are vital for systems neuroscience.
Spike sorting remains a significant challenge in data analysis.
Generative models, like Gaussian mixtures, quantify unit isolation quality.

Purpose of the Study:

To introduce a novel spike sorting strategy for chronic extracellular recordings.
To address limitations of existing methods, particularly cluster drift and outliers.
To provide a robust measure of unit isolation quality.

Main Methods:

Modeling spike data using a mixture of drifting t-distributions.
Capturing temporal cluster drift and heavy-tailed spike distributions.
Developing a software implementation for rapid, interactive clustering.

Main Results:

The drifting t-distribution model fits empirical data better than Gaussian mixtures.
The model demonstrates improved robustness to outliers.
Accurate estimation of misclassification error compared to existing metrics.

Conclusions:

The mixture of drifting t-distributions model facilitates efficient spike sorting of extensive datasets.
It offers a reliable measure of unit isolation quality across diverse recording conditions.
Enables interactive analysis of chronic neural recordings.