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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a problem,...
Dimensional Analysis03:40

Dimensional Analysis

Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...
Dimensional Analysis01:27

Dimensional Analysis

Dimensional analysis is a valuable technique in fluid mechanics for simplifying complex problems by reducing them into dimensionless groups. These groups capture the essential relationships between the variables involved, allowing researchers and engineers to analyze fluid flow without dealing with each variable individually. This approach reduces the number of independent variables, allowing for easier analysis and better understanding of physical phenomena.
In fluid mechanics, dimensional...

You might also read

Related Articles

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

Sort by
Same author

Multiscale hyperbolic embedding reveals hierarchical structure in complex biological systems.

NPJ systems biology and applications·2026
Same author

Physiological basis of resolution acuity in vision.

Nature communications·2026
Same author

Spontaneous replication fork collapse regulates telomere length homeostasis in wild type yeast.

bioRxiv : the preprint server for biology·2026
Same author

Distance-Based Logistic Matrix Factorization.

Neural computation·2025
Same author

Author Correction: ImAge quantitates aging and rejuvenation.

Nature aging·2025
Same author

Computations that sustain neural feature selectivity across processing stages.

PLoS computational biology·2025
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Minimal models of multidimensional computations.

Jeffrey D Fitzgerald1, Lawrence C Sincich, Tatyana O Sharpee

  • 1Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America.

Plos Computational Biology
|April 2, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a maximum noise entropy method to model biological system computations, revealing logistic functions for neural responses. This approach accurately captures neural information processing with minimal parameters.

Related Experiment Videos

Last Updated: Jun 3, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Computational neuroscience
  • Information theory
  • Machine learning

Background:

  • Biological systems perform complex computations with limited input-output correlation data.
  • Existing models often lack a principled approach to handle multidimensional input-output relationships.

Purpose of the Study:

  • To develop a minimally biased, closed-form model for biological system computations using maximum noise entropy.
  • To characterize neural computations in the macaque retina and thalamus by analyzing their nonlinear input-output functions.

Main Methods:

  • Constructing maximum noise entropy response functions constrained by input/output moments.
  • Applying logistic functions for binary output systems (e.g., neurons).
  • Utilizing minimum mutual information models derived from average output constraints.

Main Results:

  • Maximum noise entropy models are equivalent to conditional random fields.
  • Logistic functions accurately describe neural responses in macaque retina and thalamus.
  • A second-order logistic model explains 93% of mutual information with few parameters.

Conclusions:

  • The maximum noise entropy approach provides a principled method for modeling complex biological computations.
  • Neural responses primarily encode information through first and second-order correlations, even with non-Gaussian stimuli.
  • This framework allows for unbiased modeling and determination of encoded input statistics.