Bio-inspired learning has been gaining popularity recently given that Backpropagation (BP) is not considered biologically plausible. Many algorithms have been proposed in the literature which are all more biologically plausible than BP. However, apart from overcoming the biological implausibility of BP, a strong motivation for using Bio-inspired algorithms remains lacking. In this study, we undertake a holistic comparison of BP vs. multiple Bio-inspired algorithms to answer the question of whether Bio-learning offers additional benefits over BP. We test Bio-algorithms under different design choices such as access to only partial training data, resource constraints in terms of the number of training epochs, sparsification of the neural network parameters and addition of noise to input samples. Through these experiments, we notably find two key advantages of Bio-algorithms over BP. Firstly, Bio-algorithms perform much better than BP when the entire training dataset is not supplied. Four of the five Bio-algorithms tested outperform BP by upto 5% accuracy when only 20% of the training dataset is available. Secondly, even when the full dataset is available, Bio-algorithms learn much quicker and converge to a stable accuracy in far lesser training epochs than BP. Hebbian learning, specifically, is able to learn in just 5 epochs compared to around 100 epochs required by BP. These insights present practical reasons for utilising Bio-learning beyond just their biological plausibility and also point towards interesting new directions for future work on Bio-learning.

翻译：摘要：近年来，鉴于反向传播（BP）被认为缺乏生物合理性，仿生学习日益受到关注。文献中已提出诸多比BP更具生物合理性的算法。然而，除了克服BP的生物不合理性外，目前尚缺乏使用仿生算法的强有力动机。在本研究中，我们对BP与多种仿生算法进行全面比较，以探究仿生学习是否比BP具有额外优势。我们针对不同设计选择测试仿生算法，例如仅能访问部分训练数据、训练轮次有限等资源约束、神经网络参数稀疏化以及向输入样本添加噪声。通过这些实验，我们显著发现仿生算法相对BP的两大优势。首先，当整个训练数据集未全部提供时，仿生算法的表现远优于BP。在仅有20%训练数据可用的情况下，测试的五种仿生算法中有四种的准确率优于BP达5%。其次，即使在全数据集可用时，仿生算法的学习速度更快，能在远少于BP所需的训练轮次内收敛至稳定准确率。具体而言，赫布学习仅需5轮即可完成学习，而BP约需100轮。这些见解为超越单纯生物合理性而实际应用仿生学习提供了充分理由，并为未来仿生学习研究指出了有趣的新方向。