Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Neural Circuits

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

Neural Regulation

Neural Regulation

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.

Random Variables

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...

Entropy Change in Reversible Processes

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.

The Role of Ion Channels in Neuronal Computation

The Role of Ion Channels in Neuronal Computation

A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.

Reinforcement

Reinforcement

Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:

You might also read

Related Articles

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

Sort by

Same author

Information-Theoretic Model and Analysis of Molecular Signaling in Targeted Drug Delivery.

IEEE transactions on nanobioscience·2020

Same author

Delay-Induced Synchronization of Identical Linear Multiagent Systems.

IEEE transactions on cybernetics·2012

Same author

Linguistic models and linguistic modeling.

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society·2008

Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013

Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011

Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011

Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011

Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011

Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

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

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

Published on: March 2, 2015

ANASA-a stochastic reinforcement algorithm for real-valued neural computation.

A V Vasilakos¹, N H Loukas

¹Inst. of Comput. Sci., Found. for Res. and Technol.-Hellas, Heraklion.

IEEE Transactions on Neural Networks

|January 1, 1996

Summary

This summary is machine-generated.

A new adaptive neural algorithm of stochastic activation (ANASA) offers efficient reinforcement learning for neural networks. This efficient algorithm demonstrates superior accuracy and faster convergence rates in various learning tasks.

More Related Videos

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Related Experiment Videos

Last Updated: Jul 7, 2026

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

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

Published on: March 2, 2015

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Neural networks require efficient training algorithms for continuous output tasks.
Existing reinforcement learning methods can be slow to converge and lack optimal performance.
Self-organizing neural networks and reinforcement estimation offer potential for improved learning.

Purpose of the Study:

Introduce ANASA, an adaptive neural algorithm of stochastic activation, for efficient neural network training.
Improve accuracy and convergence rates in continuous output learning tasks.
Provide theoretical and experimental validation of the ANASA algorithm's performance.

Main Methods:

Developed ANASA, integrating concepts from self-organizing neural networks and reinforcement estimation.
Implemented adaptive learning rate and self-adjusting stochastic activation for accelerated learning.
Proved optimal performance using strong convergence theorems and conducted experimental evaluations.

Main Results:

ANASA demonstrated superior accuracy and convergence rates compared to existing associative reinforcement learning methods.
The algorithm showed rapid convergence in single neural unit tasks and multilayered network function modeling.
Theoretical analysis confirmed the algorithm's optimal performance under specific assumptions.

Conclusions:

ANASA is an efficient and effective reinforcement learning algorithm for training neural networks with continuous outputs.
The algorithm's adaptive and stochastic features contribute to its accelerated learning and superior performance.
ANASA represents a significant advancement in neural network training methodologies.