> Another salient property of the brain ... is that the connection strengths between neurons can be modified in response to activity and experience—a process that is widely believed by neuroscientists to be the basis for learning and memory. Repetitive training enables the neuronal circuits to become better configured for the tasks being performed, resulting in greatly improved speed and precision.

This seems like a clear description of a bayesian process. The new connections are a posterior updated from the likelihood informed from input sensory data. But the fact that they don't describe it as such makes me wonder if there's other reasonable ways to model this learning behaviour.

I am familiar with Karl Friston's work (as a layperson) of animal behaviour and learning as a form of bayesian update via variational inference. Does anyone know if his ideas, or the general idea of learning as a bayesian process well accepted in the neuroscience industry?