# FFCL: Forward-Forward Net with Cortical Loops, Training and Inference on Edge Without Backpropagation

arXiv: https://arxiv.org/abs/2405.12443 · HTML: https://mlsanity.com/paper/2405.12443/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 21 May 2024.

## Builds on (most cited first, 6 of 6)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [The Forward-Forward Algorithm: Some Preliminary Investigations](https://mlsanity.com/paper/2212.13345/) (2022)
- [The HSIC Bottleneck: Deep Learning without Back-Propagation](https://mlsanity.com/paper/1908.01580/) (2019)
- [Gradients without Backpropagation](https://mlsanity.com/paper/2202.08587/) (2022)
- [Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass](https://mlsanity.com/paper/2201.11665/) (2022)
- [Meta Learning Backpropagation And Improving It](https://mlsanity.com/paper/2012.14905/) (2020)

## Built on by (most cited first, 6 of 6)

- [Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task](https://mlsanity.com/paper/2412.03915/) (2024)
- [Hyperspherical Forward-Forward with Prototypical Representations](https://mlsanity.com/paper/2605.00082/) (2026)
- [FFGAF-SNN: The Forward-Forward Based Gradient Approximation Free Training Framework for Spiking Neural Networks](https://mlsanity.com/paper/2507.23643/) (2025)
- [NetworkFF: Unified Layer Optimization in Forward-Only Neural Networks](https://mlsanity.com/paper/2512.17531/) (2025)
- [Selectivity and Shape in the Design of Forward-Forward Goodness Functions](https://mlsanity.com/paper/2604.13081/) (2026)
- [Adaptive Multi-Scale Goodness Aggregation for Forward-Forward Learning](https://mlsanity.com/paper/2605.18804/) (2026)

## Abstract (arXiv)

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.