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Strategy of Incremental Learning on a Compartmental Spiking Neuron Model

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Abstract

The article presents a method for implementing incremental learning on a compartmental spiking neuron model. The training of one neuron with the possibility of forming new classes was chosen as an incremental learning scenario. During the training, only a new sample was used, without knowledge of the entire previous training samples. The results of experiments on the Iris dataset are presented, demonstrating the applicability of the chosen strategy for incremental learning on a compartmental spiking neuron model.

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Funding

The work was supported by the state task of the Russian Ministry of Education and Science for 2023 “Research and development of a biosimilar system for controlling the behavior of mobile robots based on energy-efficient soft-ware and hardware neuromorphic tools” (FNRG-2022-0016 1021060307690-3-1.2.1;2.2.2).

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Authors and Affiliations

  1. Russian State Scientific Center for Robotics and Technical Cybernetics, 194064, St. Petersburg, Russia

    A. M. Korsakov & T. T. Isakov

  2. Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    T. T. Isakov & A. V. Bakhshiev

Authors
  1. A. M. Korsakov
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  2. T. T. Isakov
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  3. A. V. Bakhshiev
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Correspondence to A. M. Korsakov.

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The authors of this work declare that they have no conflicts of interest.

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Korsakov, A.M., Isakov, T.T. & Bakhshiev, A.V. Strategy of Incremental Learning on a Compartmental Spiking Neuron Model. Opt. Mem. Neural Networks 32 (Suppl 2), S237–S243 (2023). https://doi.org/10.3103/S1060992X23060073

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  • DOI: https://doi.org/10.3103/S1060992X23060073

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