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Dish of brain cells learns to play Pong
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Dish of brain cells learns to play Pong
"What I pray of you is, to keep your eye upon Him, for that is everything. Do you say, 'How am I to keep my eye on Him?' I reply, keep your eye off everything else, and you will soon see Him. All depends on the eye of faith being kept on Him. How simple it is!" (J.B. Stoney)Tags: None
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Originally posted by lee_merrill View Post
Highlights- Improvements in performance or “learning” over time following closed-loop feedback
- Learning observed from both human and primary mouse cortical neurons
- Systems with stimulus but no feedback show no learning
- Dynamic changes observed in neural electrophysiological activity during embodiment
Integrating neurons into digital systems may enable performance infeasible with silicon alone. Here, we develop DishBrain, a system that harnesses the inherent adaptive computation of neurons in a structured environment. In vitro neural networks from human or rodent origins are integrated with in silico computing via a high-density multielectrode array. Through electrophysiological stimulation and recording, cultures are embedded in a simulated game-world, mimicking the arcade game “Pong.” Applying implications from the theory of active inference via the free energy principle, we find apparent learning within five minutes of real-time gameplay not observed in control conditions. Further experiments demonstrate the importance of closed-loop structured feedback in eliciting learning over time. Cultures display the ability to self-organize activity in a goal-directed manner in response to sparse sensory information about the consequences of their actions, which we term synthetic biological intelligence. Future applications may provide further insights into the cellular correlates of intelligence
I'm always still in trouble again
"You're by far the worst poster on TWeb" and "TWeb's biggest liar" --starlight (the guy who says Stalin was a right-winger)
"Overall I would rate the withdrawal from Afghanistan as by far the best thing Biden's done" --Starlight
"Of course, human life begins at fertilization that’s not the argument." --Tassman
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Good to know more, thanks!"What I pray of you is, to keep your eye upon Him, for that is everything. Do you say, 'How am I to keep my eye on Him?' I reply, keep your eye off everything else, and you will soon see Him. All depends on the eye of faith being kept on Him. How simple it is!" (J.B. Stoney)
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Originally posted by lee_merrill View Post
Jorge: Functional Complex Information is INFORMATION that is complex and functional.
MM: First of all, the Bible is a fixed document.
MM on covid-19: We're talking about an illness with a better than 99.9% rate of survival.
seer: I believe that so called 'compassion' [for starving Palestinian kids] maybe a cover for anti Semitism, ...
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Originally posted by Sparko View PostThey should have rewarded them with bacon.
This sort of work has been done before with artificial neural networks, which have taught themselves to play everything from chess to Starcraft without being given the rules of the game. The key thing there is that there's a way of keeping score of the game, and you tell the neural network to maximize the score, and penalize it for attempts to make an illegal move. Given that sort of reward system, you can get these things to learn the rules and play effectively - better than humans in almost all cases so far.
But we don't have any way of telling neurons to maximize anything. So, on the face of it, this shouldn't work. Why does it?
That's where the "free energy principle" comes into play (which is a terrible name, since it has nothing to do with the thermodynamics of the neurons' metabolisms). The idea is that neurons will natively generate expectations of their future state, in terms of what sort of signals they expect to be receiving. When the future doesn't match expectations, then they're more likely to revise the weightings they give to different signals in order to try to minimize that discrepancy. At the moment, this is purely a theoretical idea, and hasn't been demonstrated in any living system.
This work can be viewed as a test of that idea. If this is right, on their own, the neurons that get input regarding the state of a system (with the input generated by a computer) are likely to figure out the "physics" of Pong just by trying to match future states with the input. To get them to actually play Pong, they were given random inputs (which should never match a future state) whenever they let the ball cross the end line.
The fact that this worked is consistent with the theoretical ideas. Obviously, there are other possible explanations that haven't been ruled out, but it's a fascinating way of getting evidence in a very complicated system."Any sufficiently advanced stupidity is indistinguishable from trolling."
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Originally posted by TheLurch View PostI'm quoting Sparko because it actually gets at the most interesting aspect of this work: how do you reward a neuron in a dish?
This sort of work has been done before with artificial neural networks, which have taught themselves to play everything from chess to Starcraft without being given the rules of the game. The key thing there is that there's a way of keeping score of the game, and you tell the neural network to maximize the score, and penalize it for attempts to make an illegal move. Given that sort of reward system, you can get these things to learn the rules and play effectively - better than humans in almost all cases so far.
But we don't have any way of telling neurons to maximize anything. So, on the face of it, this shouldn't work. Why does it?
That's where the "free energy principle" comes into play (which is a terrible name, since it has nothing to do with the thermodynamics of the neurons' metabolisms). The idea is that neurons will natively generate expectations of their future state, in terms of what sort of signals they expect to be receiving. When the future doesn't match expectations, then they're more likely to revise the weightings they give to different signals in order to try to minimize that discrepancy. At the moment, this is purely a theoretical idea, and hasn't been demonstrated in any living system.
This work can be viewed as a test of that idea. If this is right, on their own, the neurons that get input regarding the state of a system (with the input generated by a computer) are likely to figure out the "physics" of Pong just by trying to match future states with the input. To get them to actually play Pong, they were given random inputs (which should never match a future state) whenever they let the ball cross the end line.
The fact that this worked is consistent with the theoretical ideas. Obviously, there are other possible explanations that haven't been ruled out, but it's a fascinating way of getting evidence in a very complicated system.
I'm always still in trouble again
"You're by far the worst poster on TWeb" and "TWeb's biggest liar" --starlight (the guy who says Stalin was a right-winger)
"Overall I would rate the withdrawal from Afghanistan as by far the best thing Biden's done" --Starlight
"Of course, human life begins at fertilization that’s not the argument." --Tassman
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