While effective, the backpropagation (BP) algorithm exhibits limitations in terms of biological plausibility, computational cost, and suitability for online learning. As a result, there has been a growing interest in developing alternative biologically plausible learning approaches that rely on local learning rules. This study focuses on the primarily unsupervised similarity matching (SM) framework, which aligns with observed mechanisms in biological systems and offers online, localized, and biologically plausible algorithms. i) To scale SM to large datasets, we propose an implementation of Convolutional Nonnegative SM using PyTorch. ii) We introduce a localized supervised SM objective reminiscent of canonical correlation analysis, facilitating stacking SM layers. iii) We leverage the PyTorch implementation for pre-training architectures such as LeNet and compare the evaluation of features against BP-trained models. This work combines biologically plausible algorithms with computational efficiency opening multiple avenues for further explorations.

翻译：尽管反向传播（BP）算法效果显著，但其在生物合理性、计算成本以及在线学习适用性方面存在局限性。因此，人们日益关注开发基于局部学习规则的替代性生物合理学习方法。本研究聚焦于主要无监督学习的相似性匹配（SM）框架，该框架与生物系统中观察到的机制一致，并提供了在线、局部化且具有生物合理性的算法。具体而言：i) 为了将SM扩展到大规模数据集，我们提出了一种基于PyTorch的卷积非负SM实现；ii) 我们引入一种类似于典型相关分析的局部监督SM目标函数，便于堆叠SM层；iii) 我们利用PyTorch实现预训练LeNet等架构，并将特征评估结果与BP训练模型进行对比。本研究将生物合理算法与计算效率相结合，为后续探索开辟了多种途径。