Neurons as Autoencoders.

IF 1.6 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Life Pub Date : 2024-11-13 DOI:10.1162/artl_c_00461

Larry Bull

引用次数: 0

Abstract

This letter presents the idea that neural backpropagation is exploiting dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward network are explored. This is contrasted with the equivalent standard layered approach to autoencoding. It is shown that such individualized processing is not detrimental and can improve network learning.

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作为自动编码器的神经元

这封信提出了神经反向传播利用树突处理使单个神经元进行自动编码的观点。通过使用非常简单的连接权重搜索启发式和人工神经网络模型，探讨了前馈网络隐藏层中每个神经元交错自动编码的效果。这与自动编码的等效标准分层方法进行了对比。结果表明，这种个性化处理并不有害，而且可以改善网络学习。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Artificial Life 工程技术-计算机：理论方法

CiteScore

4.70

自引率

7.70%

发文量

审稿时长

>12 weeks

期刊介绍： Artificial Life, launched in the fall of 1993, has become the unifying forum for the exchange of scientific information on the study of artificial systems that exhibit the behavioral characteristics of natural living systems, through the synthesis or simulation using computational (software), robotic (hardware), and/or physicochemical (wetware) means. Each issue features cutting-edge research on artificial life that advances the state-of-the-art of our knowledge about various aspects of living systems such as: Artificial chemistry and the origins of life Self-assembly, growth, and development Self-replication and self-repair Systems and synthetic biology Perception, cognition, and behavior Embodiment and enactivism Collective behaviors of swarms Evolutionary and ecological dynamics Open-endedness and creativity Social organization and cultural evolution Societal and technological implications Philosophy and aesthetics Applications to biology, medicine, business, education, or entertainment.