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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.5K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.5K
Neural Circuits01:25

Neural Circuits

2.1K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.1K
The Retina01:32

The Retina

73.0K
The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.
73.0K
Anatomy of the Eyeball01:20

Anatomy of the Eyeball

8.4K
The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle...
8.4K
Color Vision01:24

Color Vision

1.0K
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
1.0K
Vision01:24

Vision

57.8K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
57.8K

You might also read

Related Articles

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

Sort by
Same author

Large-scale synaptic dynamics drive the reconstruction of binocular circuits in mouse visual cortex.

Nature communications·2025
Same author

Brain-wide microstrokes affect the stability of memory circuits in the hippocampus.

Nature communications·2025
Same author

Pre-training artificial neural networks with spontaneous retinal activity improves motion prediction in natural scenes.

PLoS computational biology·2025
Same author

Dendritic growth and synaptic organization from activity-independent cues and local activity-dependent plasticity.

eLife·2025
Same author

Visualization of Apical-Basal Stress Polarity Regulating Directed Cell Migration with a FRET-Based Biosensor.

ACS sensors·2025
Same author

Tailoring selenization dynamics: How heating rate manipulates nucleation and growth boosts efficiency in kesterite solar cells.

The Journal of chemical physics·2025

Related Experiment Video

Updated: Nov 8, 2025

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

6.2K

Efficient population coding depends on stimulus convergence and source of noise.

Kai Röth1,2, Shuai Shao1,3, Julijana Gjorgjieva1,2

  • 1Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Frankfurt, Germany.

Plos Computational Biology
|April 26, 2021
PubMed
Summary

Neural populations optimize information transfer by adapting neuron thresholds based on noise levels. This study reveals distinct coding strategies for converging versus independent sensory signals, akin to phase transitions.

More Related Videos

Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.4K
Examining Local Network Processing using Multi-contact Laminar Electrode Recording
13:40

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

13.0K

Related Experiment Videos

Last Updated: Nov 8, 2025

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns
09:42

Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

Published on: May 12, 2019

6.2K
Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

9.4K
Examining Local Network Processing using Multi-contact Laminar Electrode Recording
13:40

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

13.0K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Information Theory

Background:

  • Sensory organs encode stimuli using neural codes of neuronal spikes.
  • Efficient coding theory posits sensory populations maximize information under biophysical constraints.
  • Downstream convergence of sensory signals and noise effects on information coding remain poorly understood.

Purpose of the Study:

  • To investigate how information coding depends on signal convergence and noise in neural populations.
  • To calculate optimal information transfer in lumped-coding and independent-coding channels.
  • To determine critical noise levels affecting neural population coding strategies.

Main Methods:

  • Modeled information transfer in populations of nonlinear neurons under two coding scenarios: lumped and independent.
  • Analyzed information loss due to two distinct noise sources.
  • Identified critical noise thresholds and related them to phase transitions in physical systems.
  • Compared theoretical predictions with experimental data from auditory nerve fibers.

Main Results:

  • Determined optimal information transfer for lumped- and independent-coding channels under varying noise conditions.
  • Identified critical noise levels where the number of neuronal thresholds changes.
  • Observed first-order phase transitions in lumped-coding and second-order in independent-coding channels.
  • Found experimental signatures of efficient coding in auditory nerve fiber data.

Conclusions:

  • Neural populations employ diverse strategies to efficiently integrate sensory stimuli amidst noise.
  • Signal convergence and noise levels critically influence optimal neural coding.
  • Theoretical predictions align with experimental observations in sensory pathways.
  • Findings provide insights into the principles of neural information processing.