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The Predictive Forward-Forward Algorithm
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We propose the predictive forward-forward (PFF) algorithm for conducting credit assignment in neural systems. Specifically, we design a novel, dynamic recurrent neural system that learns a directed generative circuit jointly and simultaneously with a representation circuit. Notably, the system integrates learnable lateral competition, noise injection, and elements of predictive coding, an emerging and viable neurobiological process theory of cortical function, with the forward-forward (FF) adaptation scheme. Furthermore, PFF efficiently learns to propagate learning signals and updates synapses with forward passes only, eliminating key structural and computational constraints imposed by backpropagation-based schemes. Besides computational advantages, the PFF process could prove useful for understanding the learning mechanisms behind biological neurons that use local signals despite missing feedback connections. We run experiments on image data and demonstrate that the PFF procedure works as well as backpropagation, offering a promising brain-inspired algorithm for classifying, reconstructing, and synthesizing data patterns.
Forward citations
Cited by 4 Pith papers
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What Does Goodness Measure? A Likelihood-Ratio Account of Forward-Forward Learning
Squared Forward-Forward goodness is the likelihood-ratio statistic for zero-mean populations differing in scale; anisotropic and heavy-tailed cases yield Mahalanobis and saturating (divisive-normalization) forms.
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From Local Learning to Global Prediction Through Layered Surprise Cascades
An inverted Forward-Forward rule makes layered networks cancel expected activity and amplify surprise, producing brain-like bottom-up cascades.
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FFGAF-SNN: The Forward-Forward Based Gradient Approximation Free Training Framework for Spiking Neural Networks
A Forward-Forward training framework that freezes spiking layers as black-box encoders and allocates channels by inter-class difficulty achieves 99.58% on MNIST, 92.13% on Fashion-MNIST, and 75.64% on CIFAR-10, the be...
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Forward Target Propagation: A Forward-Only Approach to Global Error Credit Assignment via Local Losses
FTP trains neural networks using only forward passes, propagating random-projection target signals through the network, and achieves near-backpropagation accuracy on small shallow benchmarks.
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