Backpropagation and the brain

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How to Represent Part-Whole Hierarchies in a Neural Network (2021, background): “But biologists often object to models that use weight-sharing claiming that there is no obvious way to replicate the weights [Lillicrap et al., 2020].”
Artificial Neural Networks for Neuroscientists: A Primer (2020, methodology): “Efforts have been devoted to approximating gradient descent with algorithms more compatible with the brain’s hardware [Lillicrap et al., 2016, Guerguiev et al., 2017, Roelfsema and Holtmaat, 2018, Lillicrap et al., 2020].”
The Forward-Forward Algorithm: Some Preliminary Investigations (2022, background): “As a model of how cortex learns, backpropagation remains implausible despite considerable effort to invent ways in which it could be implemented by real neurons (Lillicrap et al., 2020; Richards and Lillicrap, 2019; Guerguiev et al., 2017; Scellier and Bengio, 2017).”
Training Spiking Neural Networks Using Lessons From Deep Learning (2021, methodology): “Conjuring alternative methods to credit assignment that a real-time machine such as the brain can implement is not only useful for developing insight into biological learning [129] but also reduces the cost of data communication in hardware [130].”