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 Experiment Videos

Statistical encoding model for a primary motor cortical brain-machine interface.

Shy Shoham1, Liam M Paninski, Matthew R Fellows

  • 1Faculty of Biomedical Engineering, the Technion, Israel Institute of Technology, Haifa 32000, Israel. sshoham@bm.technion.ac.il

IEEE Transactions on Bio-Medical Engineering
|July 27, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

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

Observation-Related Activity in Human Motor Cortex Increases with Effector Anthropomorphicity.

bioRxiv : the preprint server for biology·2026
Same author

Thermal bioeffects considerations for transcranial focused ultrasound associated neurodegeneration.

Brain stimulation·2026
Same author

Rapid temporal processing in the olfactory bulb underlies concentration-invariant odor identification and signal decorrelation.

Nature neuroscience·2026
Same author

Bessel beam side lobe suppression via non-degenerate two-photon excitation.

Biomedical optics express·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 journal

Highly Accelerated 1-mm Isotropic 3D Chemical Exchange Saturation Transfer MRI Using Wave-Co-CAIPI at 5 Tesla.

IEEE transactions on bio-medical engineering·2026
Same journal

Systematic Evaluation of Hip Exoskeleton Assistance Parameters for Enhancing Gait Stability During Ground Slip Perturbations.

IEEE transactions on bio-medical engineering·2026
Same journal

SleepConFormer: A Single-Channel EEG Framework for Sleep Staging and Consciousness Assessment in Patients with Disorders of Consciousness.

IEEE transactions on bio-medical engineering·2026
Same journal

Modeling Partial and Total Support of Left Ventricular Assist Device for Discrete Hemodynamic Control Framework.

IEEE transactions on bio-medical engineering·2026
Same journal

A Low-Cost Wearable TI-TACS Stimulator With Bipolar Quadratic-Boost Converter for Current Stimulation Validation in the Rat Brain.

IEEE transactions on bio-medical engineering·2026
Same journal

EMG-Based Gait Estimation Using Koopman-Inspired Method.

IEEE transactions on bio-medical engineering·2026
See all related articles

Researchers developed new statistical models to understand how motor cortex neurons encode movement. They found most neurons linearly transform kinematic values, with some nonlinear distortion, and introduced a novel normalized-Gaussian model for neural response variability.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Motor Control

Background:

  • Primary motor cortical neurons are known to represent movement-related kinematic and dynamic quantities.
  • Previous models often rely on averaging neural responses, potentially obscuring important details.

Purpose of the Study:

  • To systematically develop statistical encoding models for movement-related motor neurons.
  • To analyze neural responses during a two-dimensional (2-D) continuous pursuit-tracking task.
  • To introduce a novel statistical model for neural response variability.

Main Methods:

  • Utilized multielectrode array recordings during a 2-D continuous pursuit-tracking task.
  • Employed 2-D normalized occupancy plots and cascaded linear-nonlinear (LN) system models.

Related Experiment Videos

  • Developed a method for describing variability in discrete random systems and introduced a normalized-Gaussian model.

    • Main Results:

    • The expected firing rate of most motor neurons showed a linear transformation of kinematic values.
    • Approximately one-third of neurons exhibited significant nonlinear distortion.
    • Neural response variability was non-Poisson and well-captured by the normalized-Gaussian model.

    Conclusions:

    • Developed advanced statistical models for motor neuron encoding and decoding.
    • The normalized-Gaussian model effectively describes neural response variability.
    • Integrated these models into a nearly-optimal recursive decoding method using Sequential Monte Carlo filtering.