Biological Plausibility and Representational Alignment of Feedback Alignment in Convolutional Networks
A new research paper submitted to arXiv evaluates the efficacy of Feedback Alignment (FA) algorithms as a biologically plausible alternative to standard Backpropagation (BP) for training convolutional neural networks. While FA offers theoretical advantages in mimicking biological learning processes, it has historically struggled to scale effectively to convolutional architectures. The authors, Jake Lance and Larry Kieu, conduct a tripartite comparative analysis of five learning algorithms, including modified FA variants and standard BP, using the CIFAR-10 dataset. The study focuses on biological plausibility, interpretability, and computational complexity. Results indicate that modified FA algorithms successfully converge on internal representations that are structurally similar to those produced by backpropagation. The findings suggest that the functional success of these modified algorithms stems from their ability to mimic the representational geometry of BP, achieving similar outcomes despite utilizing fundamentally different weight update mechanisms. This research contributes to the ongoing effort to develop more biologically realistic artificial intelligence models without sacrificing performance in computer vision tasks.
Wire timeline
Biological Plausibility and Representational Alignment of Feedback Alignment in Convolutional Networks
A new research paper submitted to arXiv evaluates the efficacy of Feedback Alignment (FA) algorithms as a biologically plausible alternative to standard Backpropagation (BP) for training convolutional neural networks. While FA offers theoretical advantages in mimicking biological learning processes, it has historically struggled to scale effectively to convolutional architectures. The authors, Jake Lance and Larry Kieu, conduct a tripartite comparative analysis of five learning algorithms, including modified FA variants and standard BP, using the CIFAR-10 dataset. The study focuses on biological plausibility, interpretability, and computational complexity. Results indicate that modified FA algorithms successfully converge on internal representations that are structurally similar to those produced by backpropagation. The findings suggest that the functional success of these modified algorithms stems from their ability to mimic the representational geometry of BP, achieving similar outcomes despite utilizing fundamentally different weight update mechanisms. This research contributes to the ongoing effort to develop more biologically realistic artificial intelligence models without sacrificing performance in computer vision tasks.
cs.AI updates on arXiv.org