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Related Concept Videos

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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Group Polarization01:01

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Group polarization is the strengthening of an original group attitude following the discussion of views within a group (Teger & Pruitt, 1967). That is, if a group initially favors a viewpoint, after discussion the group consensus is likely a stronger endorsement of the viewpoint. Conversely, if the group was initially opposed to a viewpoint, group discussion would likely lead to stronger opposition.
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Postsynaptic Potential (PSP)01:32

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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.
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
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Potential Due to a Polarized Object01:29

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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
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Biasing of P-N Junction01:16

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Related Experiment Video

Updated: Feb 25, 2026

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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Spiking Neural P Systems With Polarizations.

Tingfang Wu, Andrei Paun, Zhiqiang Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |August 8, 2017
    PubMed
    Summary

    Spiking neural P (SN P) systems were modified to avoid regular expressions by introducing neuron polarization. These new systems remain computationally complete, calculating all Turing computable sets.

    Area of Science:

    • Theoretical Computer Science
    • Computational Neuroscience
    • Artificial Intelligence

    Background:

    • Spiking neural P (SN P) systems are parallel computation models inspired by biological neurons.
    • Traditional SN P systems use regular expressions for neuron firing conditions, leading to NP-complete decision problems.
    • The complexity arises from determining if the spike count matches the regular expression's language.

    Purpose of the Study:

    • To propose modifications to SN P systems to circumvent the limitations of regular expressions.
    • To introduce a new computational model that avoids NP-complete problems associated with spike counting.
    • To analyze the computational power and resource requirements of the modified SN P systems.

    Main Methods:

    • Replaced regular expression-based spiking rules with a polarization mechanism for neurons and rules.

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  • Introduced three electrical charges (-, 0, +) to govern neuron behavior instead of spike counts.
  • Investigated the computational completeness of these modified SN P systems.

    • Main Results:

    • The modified SN P systems, utilizing polarizations, are computationally complete.
    • These systems can compute all Turing computable sets of natural numbers.
    • The number of neurons required for a universal SN P system with polarizations was estimated.

    Conclusions:

    • The proposed modifications effectively eliminate the NP-complete problem associated with regular expressions in SN P systems.
    • Polarization-based SN P systems offer a computationally powerful alternative for modeling computation.
    • Further research directions are suggested based on these findings.