*because confidently relying on the model (in this case informational) prediction like ooh, we could do brain no problem in computer space, you are not exactly making a good scientific prediction. Good scientific prediction is that model is likely garbage, until proven otherwise, and thus shouldn’t be end all be all.

But then if you take information processing model, what it gives you, exactly, in understanding of the brain? The author contention that it is hot garbage framework, it’s doesn’t fit with how the brain works, your brain is not tiny hdd with ram and cpu, and until you think that it is, you will be searching for mirages.

Yes neural networks are much closer (because they are fucking designed to be), and yet even they has to be force fed random noise to introduce fuzziness in responses, or they’ll do the same thing every time. You reboot and reload neural net, it will do the same thing every time. But brain is not just connections of axons, it’s also extremely complicated state of the neuron itself with point mutations, dna repairs, expression levels, random rna garbage flowing about, lipid rafts at synapses, vesicles missing cause microtubules decided to chill for a day, the hormonal state of the blood, the input from the sympathetic neural system etc

We haven’t even fully simulated one single cell yet.