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

Manipulation and Analysis01:21

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Role of Cerebellum and Prefrontal Cortex in Memory01:14

Role of Cerebellum and Prefrontal Cortex in Memory

The cerebellum, while traditionally associated with motor control, also plays a crucial role in memory, particularly in procedural memory, which involves learning motor tasks that become automatic through repetition. For example, studies have shown that when the cerebellum is damaged, individuals or animals lose the ability to learn conditioned motor responses, such as the conditioned eye-blink response in classical conditioning experiments with rabbits. This study demonstrates the cerebellum's...
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...

You might also read

Related Articles

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

Sort by
Same author

Infants' spontaneous movements explore arm dynamics.

Communications biology·2026
Same author

A computational model of the cerebellar granular layer calibrated to experimental data for studying inhibition and sensory encoding.

Scientific reports·2025
Same author

Cholinergic modulation enables scalable action selection learning in a computational model of the striatum.

Scientific reports·2025
Same author

A neuromechanics solution for adjustable robot compliance and accuracy.

Science robotics·2025
Same author

Publisher Correction: A generic self-learning emotional framework for machines.

Scientific reports·2025
Same author

A generic self-learning emotional framework for machines.

Scientific reports·2024
Same journal

Strategic Ability Updating in Concurrent Games by Coalitional Commitment.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2015
Same journal

Meta-Analysis of the First Facial Expression Recognition Challenge.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Adjustable model-based fusion method for multispectral and panchromatic images.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

A New Adaptive Fast Cellular Automaton Neighborhood Detection and Rule Identification Algorithm.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
Same journal

Human-arm-and-hand-dynamic model with variability analyses for a stylus-based haptic interface.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2012
See all related articles

Related Experiment Video

Updated: Jun 2, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Cerebellarlike corrective model inference engine for manipulation tasks.

Niceto Rafael Luque1, Jesús Alberto Garrido, Richard Rafael Carrillo

  • 1Department of Computer Architecture and Technology, University of Granada, Granada, Spain. nluque@atc.ugr.es

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|May 4, 2011
PubMed
Summary
This summary is machine-generated.

This study shows how a cerebellum-inspired neural network can learn control models for robotic manipulation. It uses biologically plausible learning rules to adapt internal models for accurate movement, even with system uncertainties.

More Related Videos

A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease
05:39

A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease

Published on: June 13, 2025

Related Experiment Videos

Last Updated: Jun 2, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease
05:39

A Fine Motor Task to Study Joint Kinematics in a Preclinical Model of Neurodegenerative Disease

Published on: June 13, 2025

Area of Science:

  • Robotics
  • Computational Neuroscience
  • Control Theory

Background:

  • Accurate control of robotic systems, especially those with complex dynamics or low-power actuators, remains a challenge.
  • Biological systems, like the cerebellum, exhibit remarkable control capabilities, offering insights for artificial systems.
  • Understanding biological motor control mechanisms can inform the development of more robust and efficient artificial control systems.

Purpose of the Study:

  • To evaluate a simplified, biomimetic approach for inferring corrective control models in a robotic manipulation task.
  • To investigate how a cerebellum-like architecture can build internal models through biologically plausible synaptic plasticity.
  • To explore the role of specific plasticity mechanisms, like long-term depression (LTD) and long-term potentiation (LTP), in achieving accurate motor control.

Main Methods:

  • Utilized a cerebellum-like spiking neural network architecture for a robotic manipulation control task.
  • Implemented a feedforward control loop incorporating biologically plausible synaptic adaptation mechanisms.
  • Modeled synaptic plasticity using a temporal-correlation kernel with LTD and long-term potentiation (LTP) at parallel fiber-Purkinje cell synapses.
  • Correlated sensorimotor activity (parallel fibers) with error-based teaching signals (climbing fibers).

Main Results:

  • The proposed architecture effectively inferred corrective models for controlling objects with dynamics that significantly affect the system's model.
  • A balanced interplay between LTD and LTP was crucial for accurate learning and model inference.
  • The temporal-correlation kernel demonstrated robustness, functioning effectively even with transmission delays in sensorimotor pathways.
  • Corrective models were stored as distributed weight patterns within the neural network.

Conclusions:

  • A simplified cerebellum-like architecture can successfully infer corrective control models for complex manipulation tasks.
  • Biologically plausible synaptic plasticity mechanisms, particularly a balanced LTD/LTP, are key to adaptive motor control.
  • This biomimetic approach offers potential for developing low-power, high-performance robotic control systems, particularly for those with high inertial components.