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

Storage01:23

Storage

477
A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze...
477
Somatosensory, Motor, and Association Cortex01:23

Somatosensory, Motor, and Association Cortex

3.9K
The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at...
3.9K
Association Areas of the Cortex01:21

Association Areas of the Cortex

10.4K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
10.4K
Spinal Cord: Information Processing01:10

Spinal Cord: Information Processing

4.2K
The spinal cord is an integral hub for motor and sensory information that enables the brain to communicate with the peripheral nervous system (PNS). This communication consists of relaying sensory data and transmission of motor commands.
Sensory Information Processing
Sensory information processing begins at the sensory receptors located in the skin and other tissues, which detect somatic sensory stimuli such as touch, temperature, or pain. These receptors function as catalysts, initiating...
4.2K
Neural Circuits01:25

Neural Circuits

3.2K
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...
3.2K
Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

8.9K
The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
Motor Areas
The motor areas located in the frontal lobe are central to controlling voluntary movements. This region is further subdivided into the primary motor cortex and the premotor cortex....
8.9K

You might also read

Related Articles

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

Sort by
Same author

Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence.

bioRxiv : the preprint server for biology·2026
Same author

A Spatially Structured Spiking Network Model of Beta Traveling Waves and Their Attenuation in Motor Cortex.

bioRxiv : the preprint server for biology·2026
Same author

Climbing fibres recruit disinhibition to enhance Purkinje cell calcium signals.

Nature·2026
Same author

Corticothalamic communication for action coordination in a skilled motor behavior.

Nature neuroscience·2026
Same author

Inter- and intrahemispheric sources of vestibular signals to V1.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Climbing fibers selectively recruit disinhibitory interneurons to enhance dendritic calcium signaling in cerebellar Purkinje cells.

bioRxiv : the preprint server for biology·2025
Same journal

Author Correction: Spinal cord Tau pathology induces tactile deficits and cognitive impairment in Alzheimer's disease via dysregulation of CCK neurons.

Nature neuroscience·2026
Same journal

Hippocampal theta sweeps indicate goal direction during navigation.

Nature neuroscience·2026
Same journal

Just how goal-directed are hippocampal theta sweeps, anyway?

Nature neuroscience·2026
Same journal

Goal-directed hippocampal theta sweeps during memory-guided navigation.

Nature neuroscience·2026
Same journal

Connectomic evidence that ordered activity drives neuromuscular network formation.

Nature neuroscience·2026
Same journal

Noninvasive decoding of typed sentences from human brain activity.

Nature neuroscience·2026
See all related articles

Related Experiment Video

Updated: Mar 22, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K

Is cortical connectivity optimized for storing information?

Nicolas Brunel1,2

  • 1Department of Statistics, The University of Chicago, Chicago, Illinois, USA.

Nature Neuroscience
|April 12, 2016
PubMed
Summary
This summary is machine-generated.

Cortical networks store memories through synaptic plasticity. This study reveals that cortical connectivity is optimized for robust storage of numerous memory patterns, featuring sparse, strong bidirectional connections.

More Related Videos

Visualization of Cortical Modules in Flattened Mammalian Cortices
08:49

Visualization of Cortical Modules in Flattened Mammalian Cortices

Published on: January 22, 2018

13.9K
Biocytin Recovery and 3D Reconstructions of Filled Hippocampal CA2 Interneurons
11:21

Biocytin Recovery and 3D Reconstructions of Filled Hippocampal CA2 Interneurons

Published on: November 20, 2018

9.1K

Related Experiment Videos

Last Updated: Mar 22, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K
Visualization of Cortical Modules in Flattened Mammalian Cortices
08:49

Visualization of Cortical Modules in Flattened Mammalian Cortices

Published on: January 22, 2018

13.9K
Biocytin Recovery and 3D Reconstructions of Filled Hippocampal CA2 Interneurons
11:21

Biocytin Recovery and 3D Reconstructions of Filled Hippocampal CA2 Interneurons

Published on: November 20, 2018

9.1K

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Cortical networks are shaped by experience-dependent synaptic plasticity.
  • Synaptic plasticity enables neural networks to store activity patterns as dynamic attractors.

Purpose of the Study:

  • To investigate the properties of excitatory synaptic connectivity in networks optimized for robust storage of activity patterns.
  • To determine if theoretical findings align with empirical data on cortical connectivity.

Main Methods:

  • Theoretical modeling of neural networks.
  • Analysis of synaptic connectivity matrices.
  • Comparison of model properties with experimental data on cortical connectivity.

Main Results:

  • Optimized networks exhibit sparse connectivity with a high proportion of zero-weight synapses.
  • Bidirectionally coupled neuronal pairs are over-represented compared to random networks.
  • Bidirectionally connected pairs have stronger synaptic weights on average than unidirectionally connected pairs.

Conclusions:

  • Cortical synaptic connectivity appears optimized for robustly storing a large number of attractor states.
  • The observed connectivity features (sparsity, over-representation of bidirectional pairs, stronger bidirectional synapses) quantitatively match experimental data.
  • This optimization supports the role of synaptic plasticity in memory storage within cortical networks.