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Related Concept Videos

Neural Circuits01:25

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...
Propagation of Action Potentials01:23

Propagation of Action Potentials

The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
Neurons: The Axon01:21

Neurons: The Axon

Axons are long, cytoplasmic processes of nerve cells capable of propagating electrical impulses known as action potentials. The cytoplasm or axoplasm of an axon contains neurofibrils, neurotubules, small vesicles, lysosomes, mitochondria, and various enzymes, all encased within the axolemma, the plasma membrane of the axon.
The axon attaches to the cell body at a cone-shaped elevation called the axon hillock. The initial part of the axon, closest to the hillock, is known as the initial segment.
The Role of Ion Channels in Neuronal Computation01:19

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.
Coupled Reactions01:17

Coupled Reactions

Cellular processes such as building and breaking down complex molecules occur through stepwise chemical reactions. Some of these chemical reactions are spontaneous and release energy, whereas others require energy to proceed. Cells often couple the energy-releasing reaction with the energy-requiring one to carry out important cell functions. 
Energy in adenosine triphosphate or ATP molecules is easily accessible to do work. ATP powers the majority of energy-requiring cellular reactions. Cells...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

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Related Experiment Video

Updated: Jul 7, 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

A new back-propagation algorithm with coupled neuron.

M Fukumi1, S Omatu

  • 1Dept. of Inf. Sci. and Intelligent Syst., Tokushima Univ.

IEEE Transactions on Neural Networks
|January 1, 1991
PubMed
Summary

A new saturating linear coupled neuron (sl-CONE) model and learning algorithm accelerate neural network training. This novel approach improves convergence rates compared to traditional backpropagation, even with non-differentiable functions.

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Artificial Intelligence

Background:

  • Layered neural networks are widely used but can suffer from slow convergence during training.
  • Training neural networks often relies on gradient descent, which can be challenging with non-differentiable neuron output functions.

Purpose of the Study:

  • To introduce a novel neuron model, the saturating linear coupled neuron (sl-CONE), and its associated learning algorithm.
  • To demonstrate the effectiveness of sl-CONE in accelerating convergence for layered neural networks.
  • To enable the training of networks with non-differentiable output functions using gradient descent.

Main Methods:

  • Development of a new neuron model: saturating linear coupled neuron (sl-CONE).
  • Design of a novel learning algorithm tailored for the sl-CONE model.
  • Application of the gradient descent method for training neural networks with sl-CONE.
  • Comparative simulations against the conventional backpropagation algorithm.

Main Results:

  • The sl-CONE model demonstrates a significantly higher convergence rate in learning simulations.
  • The proposed learning algorithm effectively trains networks utilizing the sl-CONE, including those with non-differentiable output functions.
  • Simulation results confirm the superior performance of sl-CONE compared to standard backpropagation.

Conclusions:

  • The sl-CONE neuron model and its learning algorithm offer a promising advancement for efficient neural network training.
  • This novel approach enhances convergence speed and broadens the applicability of gradient descent to networks with complex neuron characteristics.
  • The sl-CONE represents a valuable contribution to the field of artificial neural networks, particularly for accelerating learning processes.