用更真实的神经元模型提升ANN表达能力与训练效率
Updating the standard neuron model in artificial neural networks
- 采用最新皮层细胞模型替代传统点神经元
- 无需增加参数即提升表达力、鲁棒性与学习速度
- 适合追求高效训练和低数据依赖的深度学习研究者
自1950年代起，人工神经网络（ANNs）一直采用当时神经科学中流行的点神经元模型，旨在更好地模拟脑功能。然而，近年神经科学研究表明该模型过于简化，无法准确描述许多基本神经过程；但当前ANN仍沿用此模型。本文引入一种最新的皮层细胞模型，通过理论分析与实验验证发现：仅用更真实的神经元单元，不增加参数数量，即可显著提升网络的表达能力、鲁棒性与学习速度，并降低记忆倾向及所需训练数据量。
原文摘要 · Abstract (English)
From their inception in the 1950s, artificial neural networks (ANNs) started using the so-called point neuron model then prevalent in neuroscience, hoping that this analogy would allow for a better emulation of brain function. Over the years the neuroscience literature has shown that the point neuron model is too simplistic to properly represent many fundamental neural processes; however, the standard neuron model in ANNs still remains the same. Here we substitute it by a very recent model of cortical cells and demonstrate through theoretical analyses and experimental results how, simply by using a more realistic neural unit element without augmenting the number of parameters, the resulting ANNs offer a number of important advantages that include increases in expressivity, robustness and learning speed, and a reduction in memorization and the amount of training data needed.
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