that new brain scanner my company built for you working out?” Peter asked.
“Great. It’s going to really simplify our neural-net studies. Wonderful machine.”
“Glad to hear it,” said Peter. “I’ve been working on refining it, trying to get a higher level of resolution.”
“The current resolution is more than adequate for the kind of work I do,” said Sarkar. “Why would you want more?”
“Remember when I was doing my practicum at U of T? I told you about that transplant donor who woke up on the operating table?”
“Oh, yes.” Sarkar shivered. “You know my religion is suspicious of transplants. We feel the body should be returned to the Earth whole. Stories like that make me believe that even more.”
“Well, I still have nightmares about it. But I think I’m finally going to be able to put that demon to rest.”
“Oh?”
“That scanner we developed for your work was just a first-stage unit. I really wanted to develop a — a superEEG, if you will, that can detect any electrical activity at all in the brain.”
“Ah,” said Sarkar, his eyebrows lifting, “so you can tell when someone is really dead?”
“Precisely.”
The server arrived with their main courses. Peter had a stack of Montreal smoked meat and rye bread, accompanied by a little carousel rack of various mustards and a side order of latkes — what Sarkar referred to as Peter’s heart-attack kit. Sarkar had gefilte fish.
“That’s right,” said Peter. “I’ve been poking at this for years now, but I’ve finally had the breakthrough I needed. Signal-to-noise-ratio problems were killing me, but while scanning the net I found some algorithms created for radio astronomy that finally let me solve the problem. I’ve now got a working prototype superEEG.”
Sarkar put down his fork. “So you can see the last neural gasp, so to speak?”
“Exactly. You know how a standard EEG works: each of the brain’s billions of neurons is constantly receiving excitatory synaptic input, inhibitory input, or a combination of the two, right? The result is a constantly fluctuating membrane potential for each neuron. EEGs measure that potential.”
Sarkar nodded.
“But in a standard EEC, the sensor wires are much bigger in diameter than individual neurons. So, rather than measuring the membrane potential of any one neuron, they measure the combined potential for all the neurons in the part of the brain beneath the wire.”
“Right,” said Sarkar.
“Well, that coarseness is the source of the problem. If only one neuron, or a few dozen or even a few hundred are reacting to synaptic input, the voltage will be orders of magnitude below what an EEC can read. Even though the EEC shows a flat line, brain activity — and therefore life — may still be continuing.”
“A crisp problem,” said Sarkar. “Crisp” was his favorite word; he used it to mean anything from well-defined to delicate to appealing to complex. “So you say you’ve found the solution?”
“Yes,” said Peter. “Instead of the small number of wires used by a standard EEG, my superEEG uses over one billion nanotech sensors. Each sensor is as tiny as an individual neuron. The sensors blanket the skull, like a bathing cap. Unlike a standard EEG, which picks up the combined signal of all the neurons in a given area, these sensors are highly directional and pick up only the membrane potential from neurons directly beneath them.” Peter held up a hand. “Of course, a straight line drawn through the brain will intersect thousands of neurons, but by cross-referencing the signals from all the sensors, I can isolate the individual electrical activity of each and every neuron in the entire brain.”
Sarkar ate another fish ball. “I see why you were having signal-to-noise problems.”
“Exactly. But I’ve solved that now. With this equipment, I should be able to detect any electrical activity at all in the brain, even if it’s just one lone neuron