Here is an reversed view, where ANNs provide inspiration for neuroscience to investigate the brain. Forward learning models provide a new perspective on how neurons without "feedback" or "learning" connections have the ability to still learn, a common scenerio. We make note of this and show the conceptual framework for forward learning: https://arxiv.org/abs/2204.01723. This conceptual framework is applicable to neuroscience models, providing an investigative path forward.

As the depth and width of the network grows, the computational advantage grows. Forward only learning algorithms, such as FF and PFF, have this advantage.

There is also a compatability advantage: forward only learning algorithm work on limited resource devices (edge devices) and neuromorphic chips.

For an analysis on efficiency, refer to section IV: https://arxiv.org/abs/2204.01723

We demonstrate reasonable performance on cifar-10 and cifar-100, in that same paper (Section IV). So, the performance gap may decrease over time.

For a review of forward only learning, with an explaination on why it has efficiency and compatibility: https://amassivek.github.io/sigprop

There is a framework for learning with forward passes, a friendly and thorough tutorial: https://amassivek.github.io/sigprop .

The most interesting insights from the framework:

• This algorithm provides an explanation for how neurons in the brain without error connections receive learning signals.
• It works for continuous networks with hebbian learning. This provides evidence for this algorithm as model of learning in the brain.
• It works for spiking neural networks using only the membrane potential (aka voltage in hardware). This supports applying this algorithm for learning on neuromorphic chips.

The Signal Propagation framework paper: https://arxiv.org/abs/2204.01723 . The Forward-Forward algorithm is an implementation of this framework.

I am an author of this work. I was presenting this work at a reading group when one of the members pointed out the connection between signal propagation and forward forward.