Quite interesting but confusing, as I come from #backpropagation DL.
If I got it right, the authors focus on showing how and why biological neural networks would benefit from being Energy Based Models for Predictive Coding, instead of Feedforward Networks employing backpropagation.
I struggled to reach where they explain how to optimize a ConvNet in PyTorch as an EB model, but they do: there is an algorithm and formulae, but I'm curious about how long and stable training is, and whether all that generalizes to typical computer vision architectures (ResNets, MobileNets, ViTs, ...).
Code is also #opensource at https://github.com/YuhangSong/Prospective-Configuration
I would like to sit a few hours at my laptop and try to better see and understand, but I think in the next days I will go to Modern #HopfieldNetworks. These too are EB and there's an energy function that is optimised by the #transformer 's dot product attention.
I think I got what attention does in Transformers, so I'm quite curious to get in what sense it's equivalent to consolidating/retrieving patterns in a Dense Associative Memory. In general, I think we're treating memory wrong with our deep neural networks. I see most of them as sensory processing, shortcut to "reasoning" without short or long term memory surrogates, but I could see how some current features may serve similar purposes...