University of Michigan (UM) biologists have uncovered a neural network that enables Drosophila melanogaster fruit flies to convert external stimuli of varying intensities into a "yes or no" decision about when to act.

Using an imaging technique that detects neuronal activity through calcium signalling between neurons, the scientists were able to produce 3-D neuroactivity imaging of the flies' entire central nervous system.

When sensory neurons detect the harmful external stimuli, they send information to second-order neurons in the central nervous system. The researchers found that one region of the nervous system in particular, called the posterior medial core, responds to sensory information by either muting less intense signals or amplifying more intense signals, effectively sorting a gradient of sensory inputs into "respond" or "don't respond" categories.

The signals get amplified through increased recruitment of second-order neurons to the neural network, a process the researchers refer to as escalated amplification. A mild stimulus could activate two second-order neurons, while a more intense stimulus may activate 10 second-order neurons in the network. The larger network can then prompt a behavioural response.

But to make a "yes/no" decision, the nervous system needs a way not just to amplify information for a "yes" response, but also to suppress unnecessary or less harmful information for a "no" response.

Using the 3-D imaging, the researchers found that the sensory neurons actually do detect the less harmful stimuli, but that information is filtered out by the posterior medial core, through the release of a chemical that represses neuron-to-neuron communication.

"There is a dominant idea in our field that these decisions are made by the accumulation of evidence, which takes time," said Bing Ye, a professor of cell and developmental biology in the UM Medical School. "In the biological mechanism we found, the network is wired in a way that it does not need an evidence accumulation phase. We don't know yet, but we wonder if this could serve as a model to help AI learn to sort information more quickly."

(Source: Xinhua)