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

Parallel Processing01:20

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
Direct Motor Pathways01:11

Direct Motor Pathways

The direct motor pathways, also known as the pyramidal tracts, are a group of neural pathways that originate in the brain and descend through the spinal cord. They control the voluntary movement of the body. There are two major direct motor pathways: the corticospinal and the corticobulbar tracts.
The corticospinal tract is responsible for the voluntary movement of the limbs and trunk. It originates in the cerebral cortex of the brain and descends through the cerebrum's internal capsule and the...
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of the problem,...
Indirect Motor Pathways01:22

Indirect Motor Pathways

The indirect motor or extrapyramidal pathways originate in the brainstem, the lower portion of the brain that connects it to the spinal cord. They consist of several distinct tracts, each with specialized functions. The four main tracts of the indirect motor pathways are the vestibulospinal tract, the reticulospinal tract, the tectospinal tract, and the rubrospinal tract.
The vestibulospinal tract originates in the vestibular nuclei of the brainstem. The vestibular system detects changes in...
Block Diagram Reduction01:22

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...

You might also read

Related Articles

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

Sort by
Same author

Human-AI Cooperation in Healthcare and Rehabilitation.

Delaware journal of public health·2026
Same author

Training sparse convolutional deep predictive coding networks with attention.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

A generative spike prediction model using behavioral reinforcement for re-establishing neural functional connectivity.

Nature computational science·2026
Same author

Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers.

bioRxiv : the preprint server for biology·2025
Same author

Latent Growth Models of Longitudinal Changes in Functional Connectivity during Early Stage Psychosis.

Neuroinformatics·2025
Same author

A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes.

Sports medicine (Auckland, N.Z.)·2025

Related Experiment Video

Updated: May 25, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

An information-theoretic approach to motor action decoding with a reconfigurable parallel architecture.

Stefan Craciun1, Austin J Brockmeier, Alan D George

  • 1Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA. craciuns@ufl.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
Summary

This study introduces a non-parametric approach using minimum error entropy (MEE) for decoding neural signals, outperforming traditional methods for non-Gaussian error distributions. A hardware implementation significantly accelerates this advanced motor decoding technique.

More Related Videos

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Related Experiment Videos

Last Updated: May 25, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Area of Science:

  • Neuroscience
  • Signal Processing
  • Computer Engineering

Background:

  • Traditional movement decoding from neural spike counts often uses mean-squared error minimization.
  • This approach is suboptimal for non-Gaussian error distributions, limiting decoding accuracy.

Purpose of the Study:

  • To propose and evaluate a non-parametric decoding method using minimum error entropy (MEE).
  • To compare MEE-based linear filters with traditional Wiener filters for neural decoding.
  • To investigate hardware acceleration for MEE filters in motor decoding applications.

Main Methods:

  • Implemented an information-theoretic approach, minimum error entropy (MEE), to minimize error entropy in decoding.
  • Compared the decoding performance of an analytic Wiener filter against a linear filter trained with MEE.
  • Mapped the MEE filter to a parallel architecture on reconfigurable hardware (FPGA) for computational efficiency.

Main Results:

  • The MEE-based filter demonstrated improved decoding performance, especially for non-Gaussian error distributions.
  • The hardware implementation achieved considerable speedup for the MEE filter.
  • The parallel architecture showed sublinear increases in execution time with respect to window size and filter order, enabling scalability.

Conclusions:

  • Minimum Error Entropy (MEE) offers a promising non-parametric alternative for neural decoding, surpassing traditional methods in specific conditions.
  • Hardware acceleration using parallel architectures on FPGAs is effective in mitigating the computational complexity of MEE filters.
  • The proposed approach is highly scalable for decoding movements from large numbers of neurons.