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The backpropagation algorithm, despite its widespread use in neural networklearning, may not accurately emulate the human cortex's learning process.Alternative strategies, such as the Forward-Forward Algorithm (FFA), offer acloser match to the human cortex's learning characteristics. However, theoriginal FFA paper and related works on the Forward-Forward Algorithm onlymentioned very limited types of neural network mechanisms and may limit itsapplication and effectiveness. In response to these challenges, we propose anintegrated method that combines the strengths of both FFA and shallowbackpropagation, yielding a biologically plausible neural network trainingalgorithm which can also be applied to various network structures. We appliedthis integrated approach to the classification of the Modified NationalInstitute of Standards and Technology (MNIST) database, where it outperformedFFA and demonstrated superior resilience to noise compared to backpropagation.We show that training neural networks with the Integrated Forward-ForwardAlgorithm has the potential of generating neural networks with advantageousfeatures like robustness.

The Integrated Forward-Forward Algorithm: Integrating Forward-Forward and Shallow Backpropagation With Local Losses