  The Forward-Forward Learning (FFL) algorithm is a recently proposed solution
for training neural networks without needing memory-intensive backpropagation.
During training, labels accompany input data, classifying them as positive or
negative inputs. Each layer learns its response to these inputs independently.
In this study, we enhance the FFL with the following contributions: 1) We
optimize label processing by segregating label and feature forwarding between
layers, enhancing learning performance. 2) By revising label integration, we
enhance the inference process, reduce computational complexity, and improve
performance. 3) We introduce feedback loops akin to cortical loops in the
brain, where information cycles through and returns to earlier neurons,
enabling layers to combine complex features from previous layers with
lower-level features, enhancing learning efficiency.
