The backpropagation algorithm, or backprop, is a widely utilized optimization
technique in deep learning. While there's growing evidence suggesting that
models trained with backprop can accurately explain neuronal data, no
backprop-like method has yet been discovered in the biological brain for
learning. Moreover, employing a naive implementation of backprop in the brain
has several drawbacks. In 2022, Geoffrey Hinton proposed a biologically
plausible learning method known as the Forward-Forward (FF) algorithm. Shortly
after this paper, a modified version called FFCL was introduced. However, FFCL
had limitations, notably being a three-stage learning system where the final
stage still relied on regular backpropagation. In our approach, we address
these drawbacks by eliminating the last two stages of FFCL and completely
removing regular backpropagation. Instead, we rely solely on local updates,
offering a more biologically plausible alternative.

在这篇论文中，我们通过消除 Forward-Forward Continuous Learning (FFCL) 的最后两个阶段和完全去除常规的反向传播，而仅仅依靠本地更新来解决常规反向传播在生物大脑中的局限性，从而提出了一种更具生物可行性的学习方法。