This is great and provides a hard data point for some napkin math on how big a neural network model would have to be to emulate the human brain. 150 million synapses / 57,000 neurons is an average of 2,632 synapses per neuron. The adult human brain has 100 (+- 20) billion or 1e11 neurons so assuming the average rate of synapse/neuron holds, that's 2.6e14 total synapses.
Assuming 1 parameter per synapse, that'd make the minimum viable model several hundred times larger than state of the art GPT4 (according to the rumored 1.8e12 parameters). I don't think that's granular enough and we'd need to assume 10-100 ion channels per synapse and I think at least 10 parameters per ion channel, putting the number closer to 2.6e16+ parameters, or 4+ orders of magnitude bigger than GPT4.
There are other problems of course like implementing neuroplasticity, but it's a fun ball park calculation. Computing power should get there around 2048: >>38919548
The calculation is intentionally underestimating the neurons, and even with that the brain ends up having more parameters than the current largest models by orders of magnitude.
Yes the estimation is intentionally modelling the neurons simpler than they are likely to be. No, it is not “missing” anything.
From the sibling comment:
> Individual proteins are capable of basic computation which are then integrated into regulatory circuits, epigenetics, and cellular behavior.
If this is true, then there may be many orders of magnitude unaccounted for.
Imagine if our intelligent thought actually depends irreducibly on the complex interactions of proteins bumping into each other in solution. It would mean computers would never be able to play the same game.