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Homeostatic learning rule for artificial neural networks

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F18%3A00321781" target="_blank" >RIV/68407700:21730/18:00321781 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.14311/NNW.2018.28.011" target="_blank" >http://dx.doi.org/10.14311/NNW.2018.28.011</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.14311/NNW.2018.28.011" target="_blank" >10.14311/NNW.2018.28.011</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Homeostatic learning rule for artificial neural networks

  • Original language description

    This article presents an improvement of learning algorithm for an artificial neural network that makes the learning process more similar to a biological neuron, but still simple enough to be easily programmed. This idea is based on autonomous artificial neurons that are working together and at same time competing for resources; every neuron is trying to be better than the others, but also needs the feed back from other neurons. The proposed artificial neuron has similar forward signal processing as the standard perceptron; the main difference is the learning phase. The learning process is based on observing the weights of other neurons, but only in biologically plausible way, no back propagation of error or 'teacher' is allowed. The neuron is sending the signal in a forward direction into the higher layer, while the information about its function is being propagated in the opposite direction. This information does not have the form of energy, it is the observation of how the neuron's output is accepted by the others. The neurons are trying to2nd such setting of their internal parameters that are optimal for the whole network. For this algorithm, it is necessary that the neurons are organized in layers. The tests proved the viability of this concept { the learning process is slower; but has other advantages, such as resistance against catastrophic interference or higher generalization.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>ost</sub> - Miscellaneous article in a specialist periodical

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    N - Vyzkumna aktivita podporovana z neverejnych zdroju

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Neural Network World

  • ISSN

    1210-0552

  • e-ISSN

  • Volume of the periodical

    28

  • Issue of the periodical within the volume

    6

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    11

  • Pages from-to

    179-189

  • UT code for WoS article

  • EID of the result in the Scopus database