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Propagation of Action Potentials01:23

Propagation of Action Potentials

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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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Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
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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.
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The first order operators using the del operator include the gradient, divergence and curl. Certain combinations of first order operators on a scalar or vector function yield second order expressions. Second-order expressions play a very important role in mathematics and physics. Some second order expressions include the divergence and curl of a gradient function, the divergence and curl of a curl function, and the gradient of a divergence function.
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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.
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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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q-Neurons: Neuron Activations Based on Stochastic Jackson's Derivative Operators.

Frank Nielsen, Ke Sun

    IEEE Transactions on Neural Networks and Learning Systems
    |September 4, 2020
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    Researchers introduced novel stochastic artificial neurons, called q-neurons, utilizing a discrete q-derivative. These q-neurons enhance deep learning models, showing improved performance on training and test data compared to standard activation functions.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computational Neuroscience

    Background:

    • Artificial neurons are fundamental units in neural networks.
    • Standard activation functions limit model performance and learning dynamics.
    • Stochasticity in neurons can offer advantages in complex learning tasks.

    Purpose of the Study:

    • To introduce a novel type of artificial neuron, the q-neuron.
    • To explore the mathematical foundation of q-neurons using Jackson's discrete q-derivative.
    • To demonstrate the integration and performance benefits of q-neurons in deep learning.

    Main Methods:

    • Development of the q-neuron model based on Jackson's discrete q-derivative.
    • Generalization of standard neural network architectures to incorporate q-neurons.
    • Implementation of q-neurons within existing deep learning frameworks.
    • Experimental evaluation comparing q-neurons against state-of-the-art activation functions.

    Main Results:

    • Q-neurons were successfully integrated into generalized neural network architectures.
    • The implementation of q-neurons demonstrated scalability and ease of integration.
    • Experimental results showed consistent performance improvements for q-neurons over standard activation functions.
    • Both training and test loss functions exhibited superior performance with q-neurons.

    Conclusions:

    • Q-neurons represent a promising new generic type of artificial neuron.
    • The q-neuron model offers enhanced performance and flexibility in deep learning.
    • The mathematical framework based on q-derivatives provides a novel approach to artificial neuron design.