隔室尖峰神经元模型的增量学习策略

IF 1 Q4 OPTICS

Optical Memory and Neural Networks Pub Date : 2023-11-28 DOI:10.3103/S1060992X23060073

A. M. Korsakov, T. T. Isakov, A. V. Bakhshiev

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引用次数: 0

摘要

本文提出了一种在间隔尖峰神经元模型上实现增量学习的方法。选择具有形成新类可能性的单个神经元的训练作为增量学习场景。在训练过程中，只使用一个新样本，而不知道之前的全部训练样本。本文给出了Iris数据集的实验结果，证明了所选策略在区隔尖峰神经元模型上的增量学习的适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Strategy of Incremental Learning on a Compartmental Spiking Neuron Model

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Strategy of Incremental Learning on a Compartmental Spiking Neuron Model

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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来源期刊

Optical Memory and Neural Networks OPTICS-

CiteScore

1.50

自引率

11.10%

发文量

期刊介绍： The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.