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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

You might also read

Related Articles

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

Sort by

Same author

BHGNN-RT: Capturing bidirectionality and network heterogeneity in graphs.

PloS one·2025

Same author

Electrocardiographic Classification using Deep Learning with Lead Switching.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Marked point process variational autoencoder with applications to unsorted spiking activities.

PLoS computational biology·2025

Same author

Advanced paternal age diversifies individual trajectories of vocalization patterns in neonatal mice.

iScience·2022

Same author

Statistical modeling for temporal dominance of sensations data incorporating individual characteristics of panelists: an application to data of milk chocolate.

Journal of food science and technology·2022

Same author

Correction to: Statistical modeling for temporal dominance of sensations data incorporating individual characteristics of panelists: an application to data of milk chocolate.

Journal of food science and technology·2022

Same journal

Targeting intracranial electrical stimulation to network regions defined within individuals causes network-level effects.

Journal of neurophysiology·2026

Same journal

When "Noise" Isn't Simply Noise: Deterministic Postural Drive During Noisy Galvanic Vestibular Stimulation (nGVS).

Journal of neurophysiology·2026

Same journal

Abrupt Scene Onsets and Gradually Emerging Scene Information Produce Distinct EEG Decoding Dynamics.

Journal of neurophysiology·2026

Same journal

From discovery to translation: charting a course for the <i>Journal of Neurophysiology</i>.

Journal of neurophysiology·2026

Same journal

Neuromodulatory Strategies Overcome Multiple Inevitable Impairments of Cerebral Palsy.

Journal of neurophysiology·2026

Same journal

Acute Fentanyl Toxicity:From Opioid-Induced to Hypoxia-Mediated Pathophysiology.

Journal of neurophysiology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 25, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

Firing rate estimation using infinite mixture models and its application to neural decoding.

Ryohei Shibue¹, Fumiyasu Komaki^2,3

¹Department of Mathematical Informatics, The University of Tokyo, Bunkyo-ku, Tokyo, Japan; and.

Journal of Neurophysiology

|August 11, 2017

Summary

This summary is machine-generated.

We developed a new neural decoding method using infinite mixture models. This approach enhances accuracy and computational speed for reconstructing stimuli from neural activity.

Keywords:

marked point processes nonparametric Bayes statistics place cells spike sorting state-space models

More Related Videos

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

A Procedure for Implanting Organized Arrays of Microwires for Single-unit Recordings in Awake, Behaving Animals

A Procedure for Implanting Organized Arrays of Microwires for Single-unit Recordings in Awake, Behaving Animals

Published on: February 14, 2014

Related Experiment Videos

Last Updated: Feb 25, 2026

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

A Procedure for Implanting Organized Arrays of Microwires for Single-unit Recordings in Awake, Behaving Animals

A Procedure for Implanting Organized Arrays of Microwires for Single-unit Recordings in Awake, Behaving Animals

Published on: February 14, 2014

Area of Science:

Neuroscience
Computational Neuroscience
Machine Learning

Background:

Neural decoding reconstructs external stimuli from neural recordings.
Previous methods like marked point processes with kernel density estimation face challenges in accuracy and computational cost.

Purpose of the Study:

To propose a novel neural decoding method that overcomes limitations of existing techniques.
To improve decoding accuracy and computational efficiency.

Main Methods:

Utilized infinite mixture models for intensity estimation in marked point processes.
Applied nonparametric Bayesian statistics for enhanced decoding.

Main Results:

The proposed method significantly improves decoding accuracy.
Demonstrated enhanced computational speed compared to kernel density estimation.
Successfully applied to position decoding from rat hippocampal spike trains.

Conclusions:

Infinite mixture models offer a superior approach for neural decoding.
The new method provides a more accurate and computationally efficient solution for reconstructing neural stimuli.