# Minimizing the Effect of Sleep Deprivation in the Forward-Forward Algorithm

arXiv: https://arxiv.org/abs/2609.06042 · HTML: https://mlsanity.com/paper/2609.06042/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 5 Sep 2026.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Contrastive Hebbian Learning with Random Feedback Weights](https://mlsanity.com/paper/1806.07406/) (2018)
- [Extending the Forward Forward Algorithm](https://mlsanity.com/paper/2307.04205/) (2023)
- [Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks](https://mlsanity.com/paper/2501.17266/) (2025)
- [Sleep Deprivation in the Forward-Forward Algorithm](https://mlsanity.com/paper/2310.18647/) (2023)

## Abstract (arXiv)

This paper addresses the challenge posed by sleep deprivation in the Forward-Forward algorithm, where separating the two passes in this algorithm and imbalancing the data processing in the passes is considered an imitation of the cognitive processes observed in humans suffering from sleep deprivation. Previous research has demonstrated that sleep deprivation in the Forward-Forward algorithm has a catastrophic effect on learning efficacy. To mitigate this issue, we explore several approaches; these include alternative activation, optimized loss function, and threshold tuning. To simulate periodic rest, we reduce the number of positive passes in alternating epochs, creating short break phases. We additionally investigate the potential of caffeine-induced stimulation to enhance performance during sleep-deprived conditions. Experimental evaluations conducted on the MNIST and Fashion-MNIST datasets demonstrate that these modifications improve accuracy under the context of sleep deprivation. For example, a 2%-62% accuracy gain is observed in a severe sleep deprivation setting (16 positive or awake periods and 1 negative or sleep period). The approaches also enhance the resilience of the algorithm and its alignment with the adaptive mechanisms of human cognition.