One of the biggest differentiators between various types of neurons described in this paper is their reach and how they branch. I would think this would make having (x,y,z,type) or at least a density of various types within an area to be the biggest indicator of network structure. Are either of these possible with current techniques or are we limited to correlations between much larger regions (lower resolution)?
Similarly I know different neuron types have very different temporal characteristics (fast firing, refractory periods etc) on the order of 1-2 ms. Is it possible to classify different types/see correlation between areas at these timescales?
One of the biggest differentiators between various types of neurons described in this paper is their reach and how they branch. I would think this would make having (x,y,z,type) or at least a density of various types within an area to be the biggest indicator of network structure. Are either of these possible with current techniques or are we limited to correlations between much larger regions (lower resolution)?
Similarly I know different neuron types have very different temporal characteristics (fast firing, refractory periods etc) on the order of 1-2 ms. Is it possible to classify different types/see correlation between areas at these timescales?