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Related Concept Videos

Neural Circuits01:25

Neural Circuits

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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.
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...
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Integration of Synaptic Events01:28

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Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
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Postsynaptic Potential (PSP)01:32

Postsynaptic Potential (PSP)

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Postsynaptic potential (PSP) refers to a change in the electrical potential of a neuron when neurotransmitters released by presynaptic neurons bind to postsynaptic receptors. This potential can either be excitatory, leading to depolarization and ultimately action potential generation, or inhibitory, leading to hyperpolarization and suppression of the postsynaptic neuron.
There are two types of receptors: ionotropic and metabotropic.
The ionotropic receptor is the membrane protein that has an...
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Graded Potential01:19

Graded Potential

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Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments

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Numerical Spiking Neural P Systems.

Tingfang Wu, Linqiang Pan, Qiang Yu

    IEEE Transactions on Neural Networks and Learning Systems
    |July 11, 2020
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    Numerical Spiking Neural P (NSNP) systems encode information using variable values and continuous functions. These NSNP systems are proven Turing universal, offering potential for developing new learning algorithms.

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    Area of Science:

    • Computational Neuroscience
    • Membrane Computing
    • Artificial Intelligence

    Background:

    • Spiking Neural P (SNP) systems use spike timing and number for computation but are incompatible with gradient-based learning.
    • Existing SNP systems struggle with numerical data processing due to their symbolic nature.

    Purpose of the Study:

    • Introduce Numerical Spiking Neural P (NSNP) systems, a novel class of neuron-inspired models.
    • Investigate the computational power of NSNP systems.
    • Explore the potential for developing learning algorithms for NSNP systems.

    Main Methods:

    • Defined NSNP systems where information is encoded by variable values.
    • Utilized continuous production functions to describe neuron behavior and value distribution.
    • Proved Turing universality for NSNP systems as number generating and accepting devices.

    Main Results:

    • NSNP systems demonstrate Turing universality with simple linear production functions.
    • Universality is achieved even with neurons containing a single production function.
    • Networks of simple neurons exhibit significant computational power.

    Conclusions:

    • NSNP systems offer a powerful computational framework with continuous dynamics.
    • The continuous nature of NSNP systems opens avenues for gradient-descent-based learning algorithm development.
    • NSNP systems represent a promising advancement in neuron-inspired computing models.