Electric fish keep from blinding themselves with these brain cells
The brain cells you see in the above image help an African fish decode electric signals to spot prey, scan its murky surroundings and communicate with its fellows. By rigorously mapping how these cells in the electrosensory lobe are wired together, scientists at Columbia's Zuckerman Institute and their colleagues reveal how this brain area continually learns to filter out interference that would…
Scientists have discovered how African electric fish maintain their ability to sense their surroundings without being blinded by their own electric signals. These unique fish emit electric fields both for navigation and communication, just as bats and dolphins use sound waves. However, these self-generated electric signals interfere with their detection of external electric fields, somewhat like background noise interfering with hearing.
In the fish's electrosensory lobe, a specialized brain region, specialized cells form a circuit that learns to predict and filter out these interfering signals. Using high-resolution microscopy, researchers mapped the connections between these brain cells. They found an intriguing pattern - the circuit consistently pairs fast-learning cells with slow-learning cells.
The fast-learning cells adapt quickly but are more susceptible to random noise, while the slow-learning cells provide stability, cancelling out the persistent interfering signals. This combination allows the electrosensory lobe to continually learn and filter out self-generated interference, enhancing the fish's ability to detect external signals.
Understanding this brain circuitry could provide valuable insights for developing artificial intelligence systems that are better at continual learning. Currently, AI often struggles with retaining information and can catastrophically forget previously learned data when new information is introduced. The study, led by researchers from Columbia University's Zuckerman Institute, could contribute to the development of more robust and adaptable artificial intelligence systems by mimicking the efficient learning mechanisms found in nature.
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