A new study published in the journal neuron shows that networks of brain cells cultured in a Petri dish can learn to play the arcade game Pong demonstrating, for the first time, what the researchers call “synthetic biological intelligence”. The study was led by Brett Kagan of Cortical Labs, a biological computing startup based in Melbourne, Australia, which integrates living brain cells with computer chips.
Teaching Pong Brain Cells
Kagan and his colleagues grew cortical neurons dissected from embryonic mouse brains, or human stem cells reprogrammed into neurons, on high-density microelectrode array chips that can simultaneously record the cells’ electrical activity and stimulate them. On the chip, cells mature and connect with each other to form neural networks that then exhibit spontaneous electrical activity.
The researchers developed their so-called “DishBrain” system by connecting the chip to a computer running the paddle and ball game. The chip provided the cells with feedback on the game, so they received a predictable electrical stimulus when the racket made contact with the ball, and an unpredictable stimulus when it did not.
The cells began to “learn” and improved their performance within five minutes of play. With each successful interception of the ball, the synchronized “spikes” of electrical activity on the network increased in size. The more feedback they received, the better their performance improved. Under conditions where they received no feedback, the networks completely failed to learn how to play the game.
Pong predictability
The study shows that a single layer of neurons can organize and coordinate its activity towards a specific goal, and can learn and adapt behavior in real time. Interestingly, human neural networks outperformed those of mouse cells, which is consistent with previous work suggesting that human neurons have greater information-processing capacity than those of rodents.
The researchers describe this “learning” in terms of the free energy principle, whereby the brain seeks to minimize the entropy, or unpredictability, of its environment.
Thus, the unpredictable stimuli delivered when neural networks fail to intercept the bullet increase entropy within the system, and thus cells adapt their behavior in order to receive predictable stimuli. This, in turn, reduces entropy and minimizes uncertainty. In other words, they have learned to make the sensory results of their behavior as predictable as possible.
The ability of neural networks to respond and adapt to environmental stimuli underlies learning in humans and other animals. The sensory stimulation delivered to the cells was far grosser than even a single organism would receive. Nonetheless, the researchers say this is the first study to show this behavior in cultured neurons, and they suggest their findings demonstrate intelligence. silicone.
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They add that their results confirm the importance of environmental feedback on the consequences of actions, which seems vital for proper brain development. These processes can take place at the cellular level.
brain in a box
Future work could reveal more about why human neurons have greater computing power than mouse cells, as well as provide a simulated model of biological learning. The DishBrain system could also be used in drug screening, to examine cellular responses to new compounds, and to improve machine learning algorithms.
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