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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...
Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
The Synapse02:47

The Synapse

Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
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...
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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.

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Related Experiment Video

Updated: Jul 6, 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

Random neural networks with synchronized interactions.

Erol Gelenbe1, Stelios Timotheou

  • 1Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College, London, UK. e.gelenbe@imperial.ac.uk

Neural Computation
|April 5, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for neuronal networks that incorporates synchronous firing, enhancing information propagation speed in large-scale distributed systems. A novel learning algorithm is developed for these networks, applied to solve complex resource allocation problems efficiently.

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Last Updated: Jul 6, 2026

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

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Interfacing 3D Engineered Neuronal Cultures to Micro-Electrode Arrays: An Innovative In Vitro Experimental Model
09:47

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Published on: October 18, 2015

Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Complex systems

Background:

  • Large-scale distributed systems, including natural and artificial neuronal networks, exhibit both local interconnections and rapid long-distance information propagation.
  • Mechanisms for fast information transfer may involve long signaling paths and synchronized network behavior.

Purpose of the Study:

  • To model fast information propagation in neuronal networks, specifically incorporating synchronous interactions.
  • To develop a learning algorithm for these extended neuronal network models.
  • To apply the model and algorithm to solve challenging computational problems.

Main Methods:

  • Extension of the Random Neural Network (RNN) model to include synchronous interactions and synchronous firing of cell ensembles.
  • Development of an O(N^3) gradient descent learning algorithm for recurrent networks with both conventional and synchronous interactions.
  • Application of the developed model and learning algorithm to an NP-hard resource allocation problem.

Main Results:

  • A novel extension of the Random Neural Network (RNN) model capable of simulating synchronous neuronal firing.
  • An efficient O(N^3) gradient descent learning algorithm for recurrent networks with synchronous interactions.
  • Successful application of the model and algorithm to find fast approximate solutions for a resource allocation problem.

Conclusions:

  • The extended RNN model effectively captures fast information propagation through synchronous neuronal firing.
  • The developed learning algorithm provides an efficient method for training complex recurrent networks.
  • The approach offers a promising solution for rapid decision-making in NP-hard problems like resource allocation.