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Noisy neural nets exhibiting memory domains.

P Anninos, M Kokkinidis, A Skouras

    Journal of Theoretical Biology
    |August 21, 1984
    PubMed
    Summary
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    This study shows that even with system noise, probabilistic neural networks exhibit multiple memory domains. These findings are analogous to those observed in noiseless neural network models.

    Area of Science:

    • Computational neuroscience
    • Artificial intelligence

    Background:

    • Previous research utilized probabilistic neural networks with chemical markers to identify multiple memory domains.
    • The role of intrinsic system noise in these memory domains was not fully explored.

    Purpose of the Study:

    • To generalize previous findings by incorporating intrinsic system noise into probabilistic neural network models.
    • To investigate the impact of spontaneous synaptic transmitter release on memory domain characteristics.

    Main Methods:

    • Development of a simple mathematical model for probabilistic neural networks.
    • Inclusion of intrinsic system noise, specifically spontaneous synaptic transmitter release, in the model.

    Main Results:

    • The mathematical model demonstrated characteristics of multiple memory domains.

    Related Experiment Videos

  • These characteristics were found to be analogous to those observed in noiseless neural network systems.
  • Conclusions:

    • Intrinsic system noise does not preclude the existence of multiple memory domains in probabilistic neural networks.
    • The developed model provides a framework for understanding memory organization in noisy neural systems.