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Is Learning in Biological Neural Networks Based on Stochastic Gradient Descent? An Analysis Using Stochastic Processes.

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Title: Is Learning in Biological Neural Networks Based on Stochastic Gradient Descent? An Analysis Using Stochastic Processes.
Authors: Christensen, Sören (AUTHOR), Kallsen, Jan (AUTHOR)
Source: Neural Computation. Jul2024, Vol. 36 Issue 7, p1424-1432. 9p.
Subjects: Biological neural networks, Stochastic processes, Stochastic learning models, Artificial neural networks
Abstract: In recent years, there has been an intense debate about how learning in biological neural networks (BNNs) differs from learning in artificial neural networks. It is often argued that the updating of connections in the brain relies only on local information, and therefore a stochastic gradient-descent type optimization method cannot be used. In this note, we study a stochastic model for supervised learning in BNNs. We show that a (continuous) gradient step occurs approximately when each learning opportunity is processed by many local updates. This result suggests that stochastic gradient descent may indeed play a role in optimizing BNNs. [ABSTRACT FROM AUTHOR]
Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Is Learning in Biological Neural Networks Based on Stochastic Gradient Descent? An Analysis Using Stochastic Processes.
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  Data: <searchLink fieldCode="AR" term="%22Christensen%2C+Sören%22">Christensen, Sören</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kallsen%2C+Jan%22">Kallsen, Jan</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Neural+Computation%22">Neural Computation</searchLink>. Jul2024, Vol. 36 Issue 7, p1424-1432. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Biological+neural+networks%22">Biological neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+learning+models%22">Stochastic learning models</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink>
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  Data: In recent years, there has been an intense debate about how learning in biological neural networks (BNNs) differs from learning in artificial neural networks. It is often argued that the updating of connections in the brain relies only on local information, and therefore a stochastic gradient-descent type optimization method cannot be used. In this note, we study a stochastic model for supervised learning in BNNs. We show that a (continuous) gradient step occurs approximately when each learning opportunity is processed by many local updates. This result suggests that stochastic gradient descent may indeed play a role in optimizing BNNs. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Neural Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1162/neco_a_01668
    Languages:
      – Code: eng
        Text: English
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        PageCount: 9
        StartPage: 1424
    Subjects:
      – SubjectFull: Biological neural networks
        Type: general
      – SubjectFull: Stochastic processes
        Type: general
      – SubjectFull: Stochastic learning models
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
    Titles:
      – TitleFull: Is Learning in Biological Neural Networks Based on Stochastic Gradient Descent? An Analysis Using Stochastic Processes.
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            NameFull: Christensen, Sören
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            NameFull: Kallsen, Jan
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            – D: 01
              M: 07
              Text: Jul2024
              Type: published
              Y: 2024
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            – TitleFull: Neural Computation
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