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

Open and closed-loop control systems01:17

Open and closed-loop control systems

1.8K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1.8K
Local Anesthetics: Differential Sensitivity of Nerve Fibers01:24

Local Anesthetics: Differential Sensitivity of Nerve Fibers

1.5K
Local anesthetics (LAs) block the sodium channels of nerve trunks, sensory nerve endings, and neuromuscular junctions. Although LAs can block all kinds of nerves, the sensitivity of nerve fibers differs according to nerve types and structures. LAs are known to block myelinated fibers faster than unmyelinated ones. Also, they block pain or sensory neurons at low concentrations without affecting the motor neurons involved in muscle contractions. This helps relieve labor pain without affecting the...
1.5K
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

10.3K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
10.3K
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
What are Estimates?01:06

What are Estimates?

8.9K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.9K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K

You might also read

Related Articles

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

Sort by
Same author

Preserving predictive information under biologically plausible compression.

PNAS nexus·2026
Same author

The Extreme Diversity Of Retinal Amacrine Cells Has Deep Evolutionary Roots.

bioRxiv : the preprint server for biology·2026
Same author

Paraplume: A fast and accurate antibody paratope prediction method provides insights into repertoire-scale binding dynamics.

PLoS computational biology·2026
Same author

Theoretical limits for sensing through phase separation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Dynamics of memory B cells and plasmablasts in healthy individuals.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Energy-based generative models for monoclonal antibodies.

mAbs·2025

Related Experiment Video

Updated: Feb 15, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

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

Published on: March 2, 2015

10.9K

Closed-Loop Estimation of Retinal Network Sensitivity by Local Empirical Linearization.

Ulisse Ferrari1, Christophe Gardella1,2, Olivier Marre1

  • 1Institut de la Vision, Sorbonne Université, INSERM, CNRS, 17 rue Moreau, 75012 Paris, France.

Eneuro
|January 31, 2018
PubMed
Summary
This summary is machine-generated.

Researchers developed a novel method to understand sensory neuron responses by locally linearizing neural processing. This approach accurately predicts neural sensitivity and reveals efficient stimulus information encoding in the rat retina.

Keywords:
Closed-loop experimentsEfficient coding theoryFisher InformationRetinaSensory system

More Related Videos

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.2K
Author Spotlight: Understanding the Ultrastructural Basis of Retinal Synaptic Connectivity and Neurotransmitter Localization in Mice
05:15

Author Spotlight: Understanding the Ultrastructural Basis of Retinal Synaptic Connectivity and Neurotransmitter Localization in Mice

Published on: July 12, 2024

782

Related Experiment Videos

Last Updated: Feb 15, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

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

Published on: March 2, 2015

10.9K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

5.2K
Author Spotlight: Understanding the Ultrastructural Basis of Retinal Synaptic Connectivity and Neurotransmitter Localization in Mice
05:15

Author Spotlight: Understanding the Ultrastructural Basis of Retinal Synaptic Connectivity and Neurotransmitter Localization in Mice

Published on: July 12, 2024

782

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Sensory Systems Biology

Background:

  • Sensory systems process complex stimuli, but identifying specific stimulus features driving neural responses is challenging due to nonlinearities.
  • Understanding neural information processing requires methods to dissect how sensory neurons respond to varying stimuli.

Purpose of the Study:

  • To present a novel perturbative approach for analyzing information processing in sensory neurons.
  • To locally linearize the collective response of sensory neurons in stimulus space.

Main Methods:

  • Applied small, amplitude-adapted perturbations to reference stimuli in closed-loop experiments.
  • Developed a local linear model to predict the sensitivity of neural responses to perturbations.
  • Utilized this approach in the rat retina to analyze neural processing.

Main Results:

  • The local linear model accurately predicted the sensitivity of neural responses.
  • Estimated the optimal performance of a neural decoder in the rat retina.
  • Demonstrated that retinal nonlinear sensitivity supports efficient stimulus information encoding.

Conclusions:

  • The developed perturbative approach effectively characterizes neural system sensitivity to external stimuli.
  • Quantifies the capacity of neural networks for encoding sensory information.
  • Provides a framework to relate neural activity to observable behaviors.