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

Neural Circuits01:25

Neural Circuits

3.4K
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...
3.4K
Neural Regulation01:37

Neural Regulation

45.2K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
45.2K

You might also read

Related Articles

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

Sort by
Same author

SmartEM: machine learning-guided electron microscopy.

Nature methods·2025
Same author

Analysis of smart imaging runtime.

Applied microscopy·2025
Same author

A connectomics-driven analysis reveals novel characterization of border regions in mouse visual cortex.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

SmartEM: machine-learning guided electron microscopy.

bioRxiv : the preprint server for biology·2024
Same author

X-Ray2EM: Uncertainty-Aware Cross-Modality Image Reconstruction from X-Ray to Electron Microscopy in Connectomics.

ArXiv·2023
Same author

Space Lower Bounds for the Signal Detection Problem.

Theory of computing systems·2021
Same journal

Chemotactic self-organization captures the dynamics of mammalian hair follicle patterning.

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

Tomographic imaging of superconducting order using particle-hole interference.

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

Inhibitory potential of autologous neutralizing antibodies sets quantitative limits on the rebound-competent HIV-1 reservoir.

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

Inferring epidemiological parameters under an infectious phylogeography model with visitor dynamics.

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

Analytical modeling for suction cup designs for skin-interfaced wearable devices.

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

Improving cell-free metabolism through direct integration of artificial respiratory chains.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

Related Experiment Video

Updated: Apr 21, 2026

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.7K

Sparse sign-consistent Johnson-Lindenstrauss matrices: compression with neuroscience-based constraints.

Zeyuan Allen-Zhu1, Rati Gelashvili1, Silvio Micali2

  • 1Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA 02139.

Proceedings of the National Academy of Sciences of the United States of America
|November 12, 2014
PubMed
Summary
This summary is machine-generated.

Sparse, sign-consistent Johnson-Lindenstrauss (JL) matrices, crucial for brain information compression, are mathematically supported. This research confirms their feasibility in neural tissue, highlighting the role of inhibition.

Keywords:
Johnson–Lindenstrauss compressionsign-consistent matricessynaptic-connectivity matrices

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.7K
Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

2.0K

Related Experiment Videos

Last Updated: Apr 21, 2026

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

7.7K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.7K
Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
08:33

Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

Published on: January 5, 2024

2.0K

Area of Science:

  • Computational neuroscience
  • Mathematical neuroscience
  • Neural coding

Background:

  • Johnson-Lindenstrauss (JL) matrices are proposed mechanisms for information compression in the brain via sparse random synaptic connections.
  • Existing mathematical frameworks lack complete support for JL matrix implementation under biological neural constraints.

Purpose of the Study:

  • To provide mathematical support for sparse, sign-consistent JL matrices in neural computation.
  • To address the biological constraints of neural tissue for information compression.

Main Methods:

  • Construction of sparse, sign-consistent JL matrices.
  • Mathematical proof of the optimality of the proposed construction.

Main Results:

  • Successful construction of sparse JL matrices adhering to sign consistency.
  • Demonstration that the construction is essentially optimal.

Conclusions:

  • Mathematical justification for JL compression in the brain is now established.
  • Inhibitory neurons are essential for efficient, correlation-preserving compression in neural pathways.