I agree that the comparison is apple to orange (or apple to pine tree). I'm not a neuroscientist, but it's a hobby area that I read a lot of the research about

I agree that the comparison is apple to orange (or apple to pine tree). I'm not a neuroscientist, but it's a hobby area that I read a lot of the research about the brain and the cells in the brain, etc. With each new study it seems that the iceberg just keeps getting bigger and neuroscientists still just understand a minority. Some interesting examples: 1-Individual Purkinje cells, in isolation, learn to respond to input patterns. Meaning no synapses or other cells, the learning is happening within that cell. 2-Until relatively recently, the equipment to detect electrical activity wasn't sensitive enough so it wasn't known that dendrites have localized spiking. The collection of inputs to the dendrites are getting pre-processed either linearly or non-linearly (depending on region/function/type of cell/etc.) before being forwarded to the neurons nucleus. 3-Astrocytes have intra and inter calcium wave signaling. The intra cell signaling is localized within "proceses" (small extensions and/or compartments of the larger cell). This signaling has now been linked to sensory information processing as well as learning, meaning it's computational and separate from neuronal computation. The equipment to directly capture this activity doesn't exist so understanding is limited (studies have been based on knocking out certain capabilities and seeing the result vs direct sampling of the waves). My opinion on comparisons: 1-A single cell in the brain is performing many more valuable computations than a single artificial neuron. 2-The brain probably also has many excess cells so a loss of a single cell doesn't have an impact. Which partially negates #1. 3-The way the brain process information could be more effective than an ANN for some functions, and it could be less effective for other functions. I think there are too many unknowns in this area to try to line it all up and compare. > However, current visual recognition and image generation is already really good, and seems closer to parity with humans I doubt that visual recognition is close to parity with humans. I think someone would have to define precisely what is included in "visual recognition" and what is not. It seems that "recognition" for humans means more (resolves more info about the object/context/environment) than what it means to an ANN.