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.

本研究主要介绍了通过 Convolutional Nonnegative SM 和类似于 canonical correlation analysis 的方法来扩大 primarily unsupervised similarity matching（SM）框架，以便在大规模数据集上实现 SM，并通过与使用 backpropagation 算法训练的模型进行比较，评估所提出方法的特征表现。