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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...
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.
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Neural Regulation01:37

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.
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...
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...
Motor Unit Stimulation01:20

Motor Unit Stimulation

When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
The latent period of contraction marks the onset of excitation-contraction coupling, when the action potential propagates across the sarcolemma, preparing the muscle fibers for contraction. As the fibers enter the contraction phase, the...

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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An accelerator for neural networks with pulse-coded model neurons.

G Frank1, G Hartmann, A Jahnke

  • 1Universität-GH Paderborn, FB14 Elektrotechnik, 33098 Paderborn, Germany.

IEEE Transactions on Neural Networks
|February 7, 2008
PubMed
Summary

Simulating large-scale neural networks for vision is challenging. This study introduces an efficient accelerator for pulse-coded neurons, enabling near real-time simulation of complex visual systems.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Neuro-inspired computing

Background:

  • Spike-synchronization is an efficient neural encoding scheme for visual systems.
  • Simulating large-scale pulse-coded neural networks is computationally intensive and faces hardware limitations.
  • Existing "one-to-one" silicon implementations of pulse-coded neurons have communication and flexibility issues.

Purpose of the Study:

  • To develop a flexible and efficient hardware accelerator for simulating pulse-coded leaky integrator neurons.
  • To enable the simulation of large-scale neural networks, particularly for visual system modeling.
  • To incorporate modulatory inputs, Hebbian-like learning, and adaptivity into the simulation.

Main Methods:

  • Developed a specialized accelerator for French and Stein neurons with modulatory inputs.
  • Implemented a Hebbian-like learning rule and adaptive capabilities within the accelerator.
  • Designed a system capable of simulating up to 128K neurons and 16M synapses, with arbitrary scalability.

Main Results:

  • The accelerator provides a straightforward, flexible, and efficient method for simulating pulse-coded neurons.
  • The system supports large network sizes (128K neurons, 16M synapses) and arbitrary expansion.
  • Achieved near real-time simulation performance for locally interconnected networks, crucial for visual mechanisms.

Conclusions:

  • The developed accelerator significantly enhances the feasibility of simulating large-scale, biologically realistic neural networks.
  • This approach overcomes the computational barriers of traditional simulation methods.
  • The system is well-suited for research in visual processing and other complex neural dynamics.