Overcoming Rank Collapse in Feedback Alignment
Backpropagation (BP) is widely viewed as biologically implausible, in part because it requires feedback weights to be the transpose of forward weights for error propagation. Interestingly, when training a network with fixed random feedback weights to circumvent this issue, learning aligns the forward weights with the feedback weights, leading the backpropagated error signal to become an approximation of the standard gradient used by BP. This process, called Feedback Alignment (FA), occurs in MLPs and very shallow CNNs but does not scale well to deeper architectures. In this work, we first investigated differences between BP and FA models, trained on CIFAR10, specifically focusing on the e...
| Authors | Gauthier Boeshertz, Razvan Pascanu, Claudia Clopath |
|---|---|
| Primary category | cs.LG |
| Published | 9 June 2026 |
| Last updated at source | 14 June 2026 |
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