- What is the Purpose of the Book of Daniel? An Excerpt from the Story of God Bible Commentary
- Daniel Wolpert - Probabilistic models of sensorimotor control (CCN 2017)
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- Daniel woolpert the real reason for brains summary of the book
- The real reason for brains
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I'm a neuroscientist.
What is the Purpose of the Book of Daniel? An Excerpt from the Story of God Bible Commentary
And in neuroscience, we have to deal with many difficult questions about the brain. But I want to start with the easiest question and the question you really should have all asked yourselves at some point in your life, because it's a fundamental question if we want to understand brain function. And that is, why do we and other animals have brains?
Not all species on our planet have brains, so if we want to know what the brain is for, let's think about why we evolved one. Now you may reason that we have one to perceive the world or to think, and that's completely wrong.
If you think about this question for any length of time, it's blindingly obvious why we have a brain. We have a brain for one reason and one reason only, and that's to produce adaptable and complex movements. There is no other reason to have a brain. Think about it. Movement is the only way you have of affecting the world around you. Now that's not quite true.
Daniel Wolpert - Probabilistic models of sensorimotor control (CCN 2017)
There's one other way, and that's through sweating. But apart from that, everything else goes through contractions of muscles. So think about communication — speech, gestures, writing, sign language — they're all mediated through contractions of your muscles.
So it's really important to remember that sensory, memory and cognitive processes are all important, but they're only important to either drive or suppress future movements. There can be no evolutionary advantage to laying down memories of childhood or perceiving the color of a rose if it doesn't affect the way you're going to move later in life.
Now for those who don't believe this argument, we have trees and grass on our planet without the brain, but the clinching evidence is this animal here — the humble sea squirt. Rudimentary animal, has a nervous system, swims around in the ocean in its juvenile life. And at some point of its life, it implants on a rock.
And the first thing it does in implanting on that rock, which it never leaves, is to digest its own brain and nervous system for food. So once you don't need to move, you don't need the luxury of that brain. And this animal is often taken as an analogy to what happens at universities when professors get tenure, but that's a different subject.
So I am a movement chauvinist. I believe movement is the most important function of the brain — don't let anyone tell you that it's not true. Now if movement is so important, how well are we doing understanding how the brain controls movement?
And the answer is we're doing extremely poorly; it's a very hard problem. But we can look at how well we're doing by thinking about how well we're doing building machines which can do what humans can do.
Think about the game of chess. How well are we doing determining what piece to move where? That problem is solved. What about the problem of picking up a chess piece, dexterously manipulating it and putting it back down on the board?
If you put a five year-old child's dexterity against the best robots of today, the answer is simple: the child wins easily. There's no competition at all. Now why is that top problem so easy and the bottom problem so hard? One reason is a very smart five year-old could tell you the algorithm for that top problem — look at all possible moves to the end of the game and choose the one that makes you win.
So it's a very simple algorithm. Now of course there are other moves, but with vast computers we approximate and come close to the optimal solution. When it comes to being dexterous, it's not even clear what the algorithm is you have to solve to be dexterous.
And we'll see you have to both perceive and act on the world, which has a lot of problems. But let me show you cutting-edge robotics. Now a lot of robotics is very impressive, but manipulation robotics is really just in the dark ages.
So this is the end of a Ph. And the student has trained this robot to pour this water into a glass.
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It's a hard problem because the water sloshes about, but it can do it. But it doesn't do it with anything like the agility of a human. Now if you want this robot to do a different task, that's another three-year Ph. There is no generalization at all from one task to another in robotics. Now we can compare this to cutting-edge human performance. So what I'm going to show you is Emily Fox winning the world record for cup stacking.
Now the Americans in the audience will know all about cup stacking. It's a high school sport where you have 12 cups you have to stack and unstack against the clock in a prescribed order. And this is her getting the world record in real time. Laughter Applause And she's pretty happy. We have no idea what is going on inside her brain when she does that, and that's what we'd like to know. So in my group, what we try to do is reverse engineer how humans control movement. And it sounds like an easy problem.
You send a command down, it causes muscles to contract. Your arm or body moves, and you get sensory feedback from vision, from skin, from muscles and so on. The trouble is these signals are not the beautiful signals you want them to be.
Daniel woolpert the real reason for brains summary of the book
So one thing that makes controlling movement difficult is, for example, sensory feedback is extremely noisy. Now by noise, I do not mean sound. We use it in the engineering and neuroscience sense meaning a random noise corrupting a signal.
So the old days before digital radio when you were tuning in your radio and you heard "crrcckkk" on the station you wanted to hear, that was the noise. But more generally, this noise is something that corrupts the signal.
So for example, if you put your hand under a table and try to localize it with your other hand, you can be off by several centimeters due to the noise in sensory feedback.
Similarly, when you put motor output on movement output, it's extremely noisy. Forget about trying to hit the bull's eye in darts, just aim for the same spot over and over again. You have a huge spread due to movement variability. And more than that, the outside world, or task, is both ambiguous and variable. The teapot could be full, it could be empty. It changes over time. So we work in a whole sensory movement task soup of noise.
Now this noise is so great that society places a huge premium on those of us who can reduce the consequences of noise.
So if you're lucky enough to be able to knock a small white ball into a hole several hundred yards away using a long metal stick, our society will be willing to reward you with hundreds of millions of dollars. Now what I want to convince you of is the brain also goes through a lot of effort to reduce the negative consequences of this sort of noise and variability. And to do that, I'm going to tell you about a framework which is very popular in statistics and machine learning of the last 50 years called Bayesian decision theory.
And it's more recently a unifying way to think about how the brain deals with uncertainty. And the fundamental idea is you want to make inferences and then take actions.
The real reason for brains
So let's think about the inference. You want to generate beliefs about the world. So what are beliefs? Beliefs could be: where are my arms in space? Am I looking at a cat or a fox? But we're going to represent beliefs with probabilities. So we're going to represent a belief with a number between zero and one — zero meaning I don't believe it at all, one means I'm absolutely certain. And numbers in between give you the gray levels of uncertainty.
And the key idea to Bayesian inference is you have two sources of information from which to make your inference. You have data, and data in neuroscience is sensory input. So I have sensory input, which I can take in to make beliefs.
But there's another source of information, and that's effectively prior knowledge. You accumulate knowledge throughout your life in memories. And the point about Bayesian decision theory is it gives you the mathematics of the optimal way to combine your prior knowledge with your sensory evidence to generate new beliefs.
And I've put the formula up there. I'm not going to explain what that formula is, but it's very beautiful. And it has real beauty and real explanatory power.
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And what it really says, and what you want to estimate, is the probability of different beliefs given your sensory input. So let me give you an intuitive example. Imagine you're learning to play tennis and you want to decide where the ball is going to bounce as it comes over the net towards you.