The team, led by Yu Toyoshima and Yuichi Iino, sought to understand how these well-known connections produce patterns of activity. They measured the activity of each neuron as the worms moved around the microfluidic chip and responded to changes in salt concentration. Surprisingly, the team found significant individual differences in neural activity. Although general patterns emerged, researchers expected less variability given well-defined neural circuits.
This individuality arises from “noise” within the system, that is, probabilistic elements that affect how neurons work. Interestingly, computer simulations that removed this noise failed to accurately reproduce actual brain activity. “By adding noise to the model, we achieved an accurate representation,” says Iino. This finding suggests that randomness plays an important role in even the simplest brains.
In addition to directly examining the worms’ behavior, the study also has broader implications. The model can be used to analyze neural activity in situations where complete connectivity maps are not available.
Source: Ferra
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