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Phil and I have decided to look at some of the tools and techniques used to try and understand the human brain. Today, we look at the MRI. Unfortunately, Brad couldn’t follow the physics, and it shows.
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In a previous post, I said that adding energy to the system would speed it up. Between that and adjusting the mutation rate, I was right. The figures are adapting to changes within minutes, instead of taking hours.
In the most recent post, I managed to get the figures to push a button when they see a light. Take a look at a typical run, after turning off the energy requirements and mutation, running the population that was produced, guesser0.pop.
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Professor Steve Stewart-Williams, one of my twitter folk posted this thing about perceptual illusions. They were all visual. I can’t see them, but I remember seeing some back when I could see well enough to see them. Even then, not all of them would work. In fact, I had different illusions of my own.
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The neuron is not the only type of brain cell. From a celebrity missing body parts, to human cells in a mouse brain, we take a look at your glia.
Here’s a video about the brain, that includes the animation I mentioned in the episode, and information on your Glia.
A Blueprint for the Human Brain
Here’s a simple and quick article about human Glia being placed into mice.
The smart mouse with the half-human brain
Here’s a less simple, less quick article about the same experiments.
Here are a couple of articles about Einstein’s brain.
The Tragic Story of How Einstein’s Brain Was Stolen and Wasn’t Even Special
The strange afterlife of Einstein’s brain
I said that myelinization is an invention of the vertebrates. Well, there are exceptions. It wasn’t in the show, but it’s interesting—something on the evolution of myelination that includes a couple of invertebrates who’ve come up with the same trick.
When I posted the last post I posted, I was smugly certain that I’d be posting another post, (that very day!)all about how success had been achieved. It had been a while since I’d last looked at my figures. I had episode 200 to kick out the door, and some research to do for a new project. It wasn’t that long of a break, but just long enough to mean I had to read my journal to figure out where I was, and what I should do next. Once I knew what was what, I realized that what I had planned to do was pointless and useless, at least compared to the shiny new idea in my head.
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I’m phrasing it as though they are looking at a light, because that’s what I’m doing—experimenting with implementing vision in an artificial life system.
There’s a factoid floating around that evolution cannot explain the eye. It’s based on a phrase written by Darwin. He had said that it is difficult to believe that something as complicated and well-constructed as the human eye could have come about by evolution. It was a rhetorical device. In the very next paragraph, Darwin lays out how evolution could produce an eye, or set of eyes, or whatever the given creature might happen to need.
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If we were to create an artificial intelligence that’s more intelligent than we are, would it take over and force us into extinction? Can a machine have a mind equal to or even better than our own? Today, we take a look at such questions, along with some side trips to sharks and whales and monkeys, and rocks and chocolate. I swear the candy thing really was relevant; I just got distracted.
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So, here’s the game, implemented in game.java. A certain number of cycles is chosen, between a minimum amount, 10 at the moment, and a maximum, 50. Then a coin is flipped that determines whether the light is on or off for this round. One port will, when called, set b to 1 if the light is on, and -1 if it’s off. So far so good. This is enough for the figures to have the simplest eye imaginable. They can detect whether or not the light is on, something that even single cells are capable of. Next, we attempt to get them to react to whether or not the light is on, again, something that some of the simplest life forms can do.
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