You're right, I doubt that the current models are enough to capture the complexity of the brain.
But models based on incredibly simple neurons can already produce quite complex behaviors. They show how many simple computing units interacting with each others can lead to things like vision. And I do believe that this is a fundamental principle.
Maybe we should explore that idea and scale this model up, instead of rejecting it as "too simple" and hoping that the complexity of the brain will be fully explained by the discovery of some quantum effect in neurons.
> As it is, ML is running away rather fast from the integrator model by introducing explicit gating and nonlinearities in the neurons.
I think the idea of a non-linear activation function has always been around. But for the rest I agree.
But models based on incredibly simple neurons can already produce quite complex behaviors. They show how many simple computing units interacting with each others can lead to things like vision. And I do believe that this is a fundamental principle.
Maybe we should explore that idea and scale this model up, instead of rejecting it as "too simple" and hoping that the complexity of the brain will be fully explained by the discovery of some quantum effect in neurons.
> As it is, ML is running away rather fast from the integrator model by introducing explicit gating and nonlinearities in the neurons.
I think the idea of a non-linear activation function has always been around. But for the rest I agree.