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

Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

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.

You might also read

Related Articles

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

Sort by
Same author

A framework for quantifying the mechanics of dexterous grasp.

bioRxiv : the preprint server for biology·2026
Same author

Limb state accounts for differences between motor imagery and action in motor cortex.

medRxiv : the preprint server for health sciences·2026
Same author

Evolutionarily conserved neural dynamics across mice, monkeys, and humans.

bioRxiv : the preprint server for biology·2026
Same author

Spatially Extensive LFP Correlations Identify Slow-Wave Sleep in Marmoset Sensorimotor Cortex.

eNeuro·2025
Same author

Combatting nonidentifiability to infer motor cortex inputs yields similar encoding of initial and corrective movements.

bioRxiv : the preprint server for biology·2025
Same author

Intracortical microstimulation in humans: a decade of safety and efficacy.

medRxiv : the preprint server for health sciences·2025
Same journal

Automated Behavior Analysis in the Novel Object Recognition Test.

Neurocomputing·2026
Same journal

CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction.

Neurocomputing·2026
Same journal

Deep Learning for analyzing chaotic dynamics in biological time series: Insights from frog heart signals.

Neurocomputing·2026
Same journal

SymRefine: A symbolic regression approach for refining and compressing neural networks.

Neurocomputing·2026
Same journal

Artificial intelligence without restriction surpassing human intelligence with probability one: Theoretical insight into secrets of the brain with AI twins of the brain.

Neurocomputing·2025
Same journal

ShaderNN: A Lightweight and Efficient Inference Engine for Real-time Applications on Mobile GPUs.

Neurocomputing·2025
See all related articles

Related Experiment Video

Updated: May 24, 2026

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging
11:24

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

Published on: December 12, 2012

Coupling Time Decoding and Trajectory Decoding using a Target-Included Model in the Motor Cortex.

Vernon Lawhern1, Nicholas G Hatsopoulos, Wei Wu

  • 1Department of Statistics, Florida State University, Tallahassee, FL 32306-4330, USA.

Neurocomputing
|March 2, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel decoding method combining trajectory and target-time estimation for motor cortex activity. The new approach significantly enhances movement prediction accuracy by integrating landmark information.

More Related Videos

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain
08:26

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

Published on: July 1, 2019

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Related Experiment Videos

Last Updated: May 24, 2026

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging
11:24

Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

Published on: December 12, 2012

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain
08:26

Non-Invasive Modulation and Robotic Mapping of Motor Cortex in the Developing Brain

Published on: July 1, 2019

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Motor cortical decoding aims to predict movement behaviors from neuronal activity.
  • Current methods often focus on hand trajectory estimation.
  • Previous work introduced landmark-time decoding for stereotyped movements.

Purpose of the Study:

  • To propose and evaluate a synergistic decoding approach combining trajectory and target-time decoding.
  • To investigate the benefits of integrating landmark information into motor cortical decoding.
  • To improve the accuracy of movement prediction from neuronal signals.

Main Methods:

  • Developed a novel decoding procedure using a linear state-space framework.
  • Implemented a forward-backward propagation algorithm incorporating target information.
  • Coupled trajectory decoding with landmark-time decoding strategies.

Main Results:

  • The new target-included method significantly improved decoding accuracy compared to non-target models.
  • The synergistic approach effectively combined time and trajectory decoding.
  • Enhanced prediction of stereotyped movement behaviors.

Conclusions:

  • Combining trajectory and target-time decoding offers synergistic benefits for motor cortical decoding.
  • Integrating landmark information enhances the accuracy of movement prediction.
  • The proposed method advances the field of brain-computer interfaces and neuroprosthetics.