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> A very new development:
> 
> https://techxplore.com/news/2022-02-individual-neurons-future.html
> 
> "Researchers at the Canadian Centre for Behavioural Neuroscience in Lethbridge, Canada have recently carried out a study investigating how individual neurons learn and make predictions about the future.
> 
> ***
> 
> "Using mathematical equations, Luczak and his colleagues demonstrated that the predictive capabilities of individual neurons could provide a new learning mechanism, which could ultimately be replicated in machines. According to the researchers, this learning process could have a metabolic origin, as neurons might need to minimize their own synaptic activity, while maximizing their impact on local blood supply by recruiting other neurons.
> 
> "'Similarly, neurons may be able to learn that X amount of input activity is usually followed by Y amount of activity. By adjusting synapses to minimize surprise—that is, the difference between actual and expected activity—neurons can save energy by being active only as much as necessary. We showed that the predictive learning rule arises naturally, as a consequence of maximizing metabolic energy by a neuron."
> 
> "In their paper, Luczak refers to this learning mechanism as the "lazy neuron principle." The team is still unsure about the exact mechanisms that might allow a single neuron to make predictions, but they believe that they could be linked to calcium signaling (i.e., a process that entails the use of calcium ions to communicate and drive intercellular processes).
> 
> "'Interestingly, our results also suggest that spontaneous brain activity (e.g., during sleep) provides 'training data' for neurons to learn to predict X from Y," Luczak said."
> 
> Comment: I view this as an attempt to understand complexification.