Tuesday, November 6, 2018
Computational Aspects of the Human Brain
As part of my lectures on the neurobiology of addiction - I digress briefly to discuss the computational aspects of the brain. A lot of that discussion is focused on on the above graphic showing that overlaps in capacity with a list of the world's ten fastest supercomputers. At least that is the estimate of the AI Impacts group. It is basically a computation based on edges and nodes. I include power estimates for a brain from existing hardware to the actual power estimate of the human brain that I would guess every physical chemistry student from my era had to contemplate at one time. And then I try to stimulate some discussion of supercomputers versus the human brain and it generally falls flat. My Socratic process goes something like this:
"OK so we know that humans can't really beat computers on straightforward calculations so what advantages do we have?"
"I will give you a hint - why do we all go thorough residency training? Why can't you learn your specialty by reading about it in a book?"
The first lesson is pattern matching. The human brain is designed not only to match patterns but to be trained to match a lot of them. Some research article suggest about 88,000, but when you consider what has to be matched that has be very a very low estimate. I quote references from 15-20 years ago and a course I used to teach on diagnostics and diagnostic decision making. Ophthalmologists correctly diagnosing diabetic retinopathy at a much higher rate than nonspecialists. Dermatologists diagnosing rashes faster and correctly classifying ambiguous rashes with greater precision than nonspecialists. If I am really on a roll I might digress to talk about Infection Disease rounds at the Milwaukee VA sometime during 1982. I was the medical student on a team of residents and fellows doing a consult for possible subacute bacterial peritonitis. As the attending listening to the presentation he was also looking at a rash on the patient's shin. By the time we were done he had also diagnosed a strep infection in addition to the peritonitis. When you have significant pattern matching capacity, and you have been exposed to relevant patterns you can recognize them quickly and improve the speed and accuracy of the diagnosis.
I move on at that point to illustrate that the computers are catching up. The simple captcha is less robust in discriminating machines from humans. Opening an account may take more that checking the "I am not a computer" box. Now you might have to look at 8 pictures and check the one that contains an automobile or a stop sign. Some of these photos are often difficult for humans to decipher.
At that point I touch on human consciousness - both the unique aspects and computational power it takes to generate. About a decade ago I started saying that if there are 8 billion people on the planet - there are 8 billion unique conscious states. It makes sense at a number of levels especially when I put up hard numbers on cell types, protein types, the genetic information represented, and the typical stream of consciousness that every person experiences every day. What is the content and flow of that activity? How does it get biased in psychiatric disorders and addictions? How much computational power does it take to generate all of this information?
My latest step is what I like to consider The Matrix observation. If I am standing in front of a room of 15-20 residents - what does it take to generate the physical representation of all of the people and all of the objects in that room? What does it take to make all of those representations unique? There can be a general consensus about what is happening - but just looking around it is clear that there are obvious different experiences. One person looks very interested and one semi-interested. One person is more focused on her Smartphone and is indifferent to my presentation. Some people look sleepy. Others look irritated. They also appear to be indifferent to the context. I know that my job is to try to get this information across and make is semi-interesting. There is no real expectation on the residents. It is clear from the questions I ask that they really don't know too much about the brain. There are parallel streams of information processing that allow us all to evaluate what is occurring on the fly both the information content and emotion. In some case there are pre-existing heuristics and in other cases associative memories and biases. All of this represents a tremendous amount of information or computational power depending on how you may want to discuss it.
I have been preoccupied myself with the computational power and estimating it accurately. I used to try to model it in terms of electrical buses and neuronal firing rates - but the numbers I got were far too low. There really are no good equivalents in the physical world with the possible exception of the Transversed Edges per Second (TEPS) metric used by the AI Impacts group for the above graphic. You can't really use estimates of typical audio or visual information and concluding that is what is being processed by the brain. I have never really seen an accurate estimate of all of the sensory information that the brain is handling in real time.
I went to bed last night and waited for sleep reverie or that period of time where you stream of thinking is jumbled and illogical just before you fall asleep. As a chronic insomniac it is one of the few reliable cues that I am probably getting some sleep. It happened when I had a sudden image of a baby high up on a brick wall, followed immediately by a person who seemed to be me sitting in a single seat futuristic car. The salesperson was describing it to me and suddenly the car and everything else was being swept down what appeared to be a very sophisticated hydraulic roadway. The roadway was bright orange and the salesman shifted his pitch to tell me the advantages of this kind of a roadway with this car. The roadway was moving at about 20 miles per hour.
I shifted briefly and remembered it was 2018 and I was in my bedroom in Minnesota.
And for a minute I thought about being able to estimate the information necessary to generate that brief full color science fiction scene and the three or four more I would encounter that night.
George Dawson, MD, DFAPA