@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}
}
