Hi Eric, I haven't read the paper in depth yet, but we've discussed it some as a lab. I love the concept, but I'm a little perplexed by something I hadn't thoug

Hi Eric, I haven't read the paper in depth yet, but we've discussed it some as a lab. I love the concept, but I'm a little perplexed by something I hadn't thought of when we first discussed it. It looks like many analyses were performed by binning transistor switching as you might spikes from neurons, or by linearly combining transistor activity across transistors or time. This is slightly puzzling to me, because transistors would appear to not work on the basis of average activity at their level of computational composition. I would be surprised if you could really understand circuit activity at a computational level with most of the measurements you describe. On the other hand, I suspect the methods used work slightly better in a real brain. Of course, the computational purpose of many neurons is difficult to parse or even construct useful hypotheses about. But at the very least, neurons in the outer periphery seem to behave in a way that is at least consistent with firing rate, e.g. impulses at the neuromuscular junction to cause muscle contraction, and retinal ganglion cells to fire at higher rates when the input matches their certain luminance features.