It’s surprising that some prominent ML practitioners still liken transformer ‘neurons’ to actual biological neurons...

Real neurons rely on spiking, ion gradients, complex dendritic trees, and synaptic plasticity governed by intricate biochemical processes. None of which apply to the simple, differentiable linear layers and pointwise nonlinearities in transformers.

Are there any reputable neuroscientists or biologists endorsing such comparisons, or is this analogy strictly a convention maintained by the ML community? :-)