Chavlis Spyridon, Poirazi Panayiota
Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology-Hellas, Heraklion, Crete, Greece.
Nat Commun. 2025 Jan 22;16(1):943. doi: 10.1038/s41467-025-56297-9.
Artificial neural networks (ANNs) are at the core of most Deep Learning (DL) algorithms that successfully tackle complex problems like image recognition, autonomous driving, and natural language processing. However, unlike biological brains who tackle similar problems in a very efficient manner, DL algorithms require a large number of trainable parameters, making them energy-intensive and prone to overfitting. Here, we show that a new ANN architecture that incorporates the structured connectivity and restricted sampling properties of biological dendrites counteracts these limitations. We find that dendritic ANNs are more robust to overfitting and match or outperform traditional ANNs on several image classification tasks while using significantly fewer trainable parameters. These advantages are likely the result of a different learning strategy, whereby most of the nodes in dendritic ANNs respond to multiple classes, unlike classical ANNs that strive for class-specificity. Our findings suggest that the incorporation of dendritic properties can make learning in ANNs more precise, resilient, and parameter-efficient and shed new light on how biological features can impact the learning strategies of ANNs.
人工神经网络（ANNs）是大多数深度学习（DL）算法的核心，这些算法成功地解决了诸如图像识别、自动驾驶和自然语言处理等复杂问题。然而，与能够非常高效地解决类似问题的生物大脑不同，深度学习算法需要大量可训练参数，这使得它们能耗高且容易过拟合。在此，我们表明，一种结合了生物树突的结构化连接和受限采样特性的新型人工神经网络架构可以克服这些限制。我们发现，树突状人工神经网络对过拟合更具鲁棒性，在一些图像分类任务中与传统人工神经网络相当或表现更优，同时使用的可训练参数显著减少。这些优势可能源于一种不同的学习策略，即与力求类别特异性的经典人工神经网络不同，树突状人工神经网络中的大多数节点对多个类别做出响应。我们的研究结果表明，纳入树突特性可以使人工神经网络的学习更精确、更具弹性且参数效率更高，并为生物特征如何影响人工神经网络的学习策略提供了新的思路。
