@article {5259,
	title = {Forward learning with top-down feedback: empirical and analytical characterization},
	journal = {arXiv},
	year = {2023},
	month = {02/2023},
	abstract = {<p>{\textquotedblleft}Forward-only{\textquotedblright} algorithms, which train neural networks while avoiding a backward pass, have recently gained attention as a way of solving the biologically unrealistic aspects of backpropagation. Here, we first discuss the similarities between two {\textquotedblleft}forward-only{\textquotedblright} algorithms, the Forward- Forward and PEPITA frameworks, and demonstrate that PEPITA is equivalent to a Forward- Forward framework with top-down feedback connections. Then, we focus on PEPITA to address compelling challenges related to the {\textquotedblleft}forward- only{\textquotedblright} rules, which include providing an analytical understanding of their dynamics and reducing the gap between their performance and that of backpropagation. We propose a theoretical analysis of the dynamics of PEPITA. In particular, we show that PEPITA is well-approximated by an {\textquotedblleft}adaptive-feedback-alignment{\textquotedblright} algorithm and we analytically track its performance during learning in a prototype high-dimensional setting. Finally, we develop a strategy to apply the weight mirroring algorithm on {\textquotedblleft}forward-only{\textquotedblright} algorithms with top-down feedback and we show how it impacts PEPITA{\textquoteright}s accuracy and convergence rate.</p>
},
	url = {https://arxiv.org/abs/2302.05440},
	author = {Ravi Francesco Srinivasan and Francesca Mignacco and Martino Sorbaro and Maria Refinetti and Gabriel Kreiman and Giorgia Dellaferrera}
}
@proceedings {5140,
	title = {Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass},
	volume = {162},
	year = {2022},
	month = {07/2022},
	pages = {4937-4955},
	abstract = {<p>Supervised learning in artificial neural networks typically relies on backpropagation, where the weights are updated based on the error-function gradients and sequentially propagated from the output layer to the input layer. Although this approach has proven effective in a wide domain of applications, it lacks biological plausibility in many regards, including the weight symmetry problem, the dependence of learning on non-local signals, the freezing of neural activity during error propagation, and the update locking problem. Alternative training schemes have been introduced, including sign symmetry, feedback alignment, and direct feedback alignment, but they invariably rely on a backward pass that hinders the possibility of solving all the issues simultaneously. Here, we propose to replace the backward pass with a second forward pass in which the input signal is modulated based on the error of the network. We show that this novel learning rule comprehensively addresses all the above-mentioned issues and can be applied to both fully connected and convolutional models. We test this learning rule on MNIST, CIFAR-10, and CIFAR-100. These results help incorporate biological principles into machine learning.</p>
},
	url = {https://proceedings.mlr.press/v162/dellaferrera22a.html},
	author = {Giorgia Dellaferrera and Gabriel Kreiman}
}
