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

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

531
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
531
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

544
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
544
Classification of Systems-II01:31

Classification of Systems-II

371
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
371
Neural Regulation01:37

Neural Regulation

41.8K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
41.8K
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

4.3K
The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
4.3K
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

609
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
609

You might also read

Related Articles

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

Sort by
Same author

The Enactive Torch: Open-source sensory substitution device for neurophysiological research.

HardwareX·2026
Same author

Perceptual sensitivity, but not metacognitive monitoring, is shaped by increases and decreases in control.

Experimental brain research·2026
Same author

Critical phase transition in bee movement dynamics can be modeled using a two-dimensional cellular automaton.

Physical review. E·2026
Same author

Gaze crossing: a new paradigm to assess social contingency and word learning during real-time infant-adult interactions.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2026
Same author

Quantifying What Is Efficacious Yet Not Observable: Cognitive Neuroscience's Measurement Problem Has a Solution.

Cognitive science·2026
Same author

The role of regularity detection and prediction in the exploration of sense of agency.

Consciousness and cognition·2025
Same journal

Passive wheels on legged robots: a survey.

Frontiers in robotics and AI·2026
Same journal

Politeness cannot make up for robots' errors.

Frontiers in robotics and AI·2026
Same journal

Workers expect basic social skills but limited autonomy from future robots - a qualitative interview study and taxonomy for robot social skills.

Frontiers in robotics and AI·2026
Same journal

Human-robot interaction in sustainable hospitality: how robot type shapes customer emotions, green perceptions, and service loyalty.

Frontiers in robotics and AI·2026
Same journal

Dynamic variance-aware federated tuning for efficient autonomous vehicle perception under non-IID settings.

Frontiers in robotics and AI·2026
Same journal

Editorial: Synergizing large language models and computational intelligence for advanced robotic systems.

Frontiers in robotics and AI·2026
See all related articles

Related Experiment Video

Updated: Nov 19, 2025

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

10.6K

Self-Optimization in Continuous-Time Recurrent Neural Networks.

Mario Zarco1, Tom Froese1,2

  • 1Departamento de Ciencias de la Computación, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Mexico City, Mexico.

Frontiers in Robotics and AI
|January 27, 2021
PubMed
Summary
This summary is machine-generated.

A new unsupervised learning technique, self-optimization, enhances complex networks by reinforcing optimal solutions. This study demonstrates its first application in continuous-time recurrent neural networks for potential use in evolutionary robotics.

Keywords:
Hebbian learningHopfield neural networkfixed-point attractorsmodelingoptimization

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.7K

Related Experiment Videos

Last Updated: Nov 19, 2025

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

10.6K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.7K

Area of Science:

  • Complex Adaptive Systems
  • Machine Learning
  • Computational Neuroscience

Background:

  • Unsupervised learning techniques are crucial for complex systems.
  • Self-optimization, a novel technique, allows networks to improve their structure.
  • Previous applications include social, gene regulatory, and neural networks.

Purpose of the Study:

  • To explore the implementation of self-optimization in continuous-time recurrent neural networks (RNNs).
  • To assess the potential of self-optimization for neural controllers in evolutionary robotics.

Main Methods:

  • Utilized a continuous-time recurrent neural network with asymmetrical connections.
  • Implemented the self-optimization process within the network architecture.
  • Analyzed network convergence and attractor dominance.

Main Results:

  • Successfully demonstrated the implementation of self-optimization in continuous-time RNNs.
  • Showcased the network's ability to generalize and reinforce optimal attractors.
  • Identified remaining challenges for real-world robotic applications.

Conclusions:

  • Self-optimization is a viable technique for continuous-time RNNs.
  • This research opens avenues for applying self-optimization in evolutionary robotics.
  • Further development is needed to address practical robotic implementation challenges.