  Backpropagation is a cornerstone algorithm in training neural networks for
supervised learning, which uses a gradient descent method to update network
weights by minimizing the discrepancy between actual and desired outputs.
Despite its pivotal role in propelling deep learning advancements, the
biological plausibility of backpropagation is questioned due to its
requirements for weight symmetry, global error computation, and dual-phase
training. To address this long-standing challenge, many studies have endeavored
to devise biologically plausible training algorithms. However, a fully
biologically plausible algorithm for training multilayer neural networks
remains elusive, and interpretations of biological plausibility vary among
researchers. In this study, we establish criteria for biological plausibility
that a desirable learning algorithm should meet. Using these criteria, we
evaluate a range of existing algorithms considered to be biologically
plausible, including Hebbian learning, spike-timing-dependent plasticity,
feedback alignment, target propagation, predictive coding, forward-forward
algorithm, perturbation learning, local losses, and energy-based learning.
Additionally, we empirically evaluate these algorithms across diverse network
architectures and datasets. We compare the feature representations learned by
these algorithms with brain activity recorded by non-invasive devices under
identical stimuli, aiming to identify which algorithm can most accurately
replicate brain activity patterns. We are hopeful that this study could inspire
the development of new biologically plausible algorithms for training
multilayer networks, thereby fostering progress in both the fields of
neuroscience and machine learning.
