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
I remember an interview with one neurologist who stated humanity has for centuries compared the functioning of the brain to the most complex technology devised yet. First it was compared to mechanical devices, then pipes and steam, then electrical circuits, then electronics and now finally computers. But he pointed out, the brain works like none of these things so we have to be aware of the limitations of our models.
Based on the stuff I've read, it's almost for sure too simple a model.
One example is that single dendrites detect patterns of synaptic activity (sequences over time) which results in calcium signaling within the neuron and altered spiking.