This paper from a couple of weeks ago suggested that neurons may learn as part of maximizing their energy efficiency. Which suggests that each neuron learns on its own, but also helps neurons by giving some feedback. I don't think it fully answered how that feedback happens (backprop or multiple forward passes), but it may be of interest to you nonetheless.

https://www.nature.com/articles/s42256-021-00430-y

I think there's a chance it's similar, I'm not a neuroscientist though. The paper says:

"This could potentially change the computational complexity of typical ML training pipelines, reduce the time and energy costs of training, influence ML hardware design, and even have implications regarding the biological plausibility of backpropagation in the brain (Bengio et al., 2015; Lillicrap et al., 2020)."