zlacker

>>geox+(OP)
> The 3D map covers a volume of about one cubic millimetre, one-millionth of a whole brain, and contains roughly 57,000 cells and 150 million synapses — the connections between neurons.

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

>>throwu+J7
Except you’d be missing the part that a neuron is not just a node with a number but a computational system itself.

>>gibson+v9
Computation is really integrated through every scale of cellular systems. Individual proteins are capable of basic computation which are then integrated into regulatory circuits, epigenetics, and cellular behavior.

Pdf: “Protein molecules as computational elements in living cells - Dennis Bray” https://www.cs.jhu.edu/~basu/Papers/Bray-Protein%20Computing...