A University of Michigan-led research team has 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. The research, described in Current Biology, helps to decode the biological mechanism that the fruit fly nervous system uses to convert a gradient of sensory information into a binary behavioral response. The findings offer up new insights that may be relevant to how such decisions work in other species, and could possibly even be applied to help artificial intelligence machines learn to categorize information.

Senior study author Bing Ye, PhD, a faculty member at the University of Michigan Life Science Institute (LSI), believes the mechanism uncovered could have far-reaching applications. “There is a dominant idea in our field that these decisions are made by the accumulation of evidence, which takes time,” Ye said. “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.”

Ye and colleagues describe their research in a paper titled, “A Neural Basis for Categorizing Sensory Stimuli to Enhance Decision Accuracy.”

Imagine working near an open window. If the outside noise is low it may be hardly noticeable. But as the noise level gradually increases, it starts to become more noticeable, and eventually, the brain makes a decision about whether to get up and close the window. So how does the nervous system translate that gradual, linear increase in intensity into to a binary, “yes/no” behavioral decision? “Whereas sensory stimuli are typically present in wide and graded intensity ranges, animals’ decisions on whether to respond to the stimuli are often binary,” the authors noted. “A fundamental question in neuroscience is how such graded-to-binary conversions in perceptual decision making occur in the central nervous system (CNS).” As neuroscientist Ye pointed out, “That’s a really big question. Between the sensory input and the behavior output is a bit of ‘black box.’ With this study, we wanted to open that box.”

Brain imaging in humans or other mammals can identify certain regions of the brain that respond to particular stimuli. But the large size of the mammalian central nervous system can be a drawback. “Although perceptual decision making has primarily been studied in mammals, the large size of the mammalian CNS limits spatiotemporal resolution in assessing CNS-wide emergent activities,” the authors noted. To determine how and when the neurons transform linear information into a nonlinear decision, they needed a much deeper, more quantitative analysis of the nervous system, Ye said.

The team chose to work with the model organism Drosophila, for which available genetic tools make it possible to identify individual neurons responding to stimuli. Using an imaging technique that detects neuronal activity through calcium signaling between neurons, the scientists were able to produce 3D neuroactivity imaging of the flies’ entire central nervous system. “ … the small size of the Drosophila larval CNS, combined with recent advances in genetically encoded calcium indicators (GECIs), allows functional imaging of the entire larval CNS at subcellular and subsecond resolution, which makes Drosophila larvae an ideal model for assessing the CNS-wide neural activity in perceptual decision making,” the investigators stated.

“What we saw was that, when we stimulate the sensory neurons that detect harmful stimuli, quite a few brain regions light up within seconds,” said Yujia Hu, PhD, a research investigator at the LSI and one of the lead authors on the study. “But these brain regions perform different functions. Some are immediately processing sensory information, some spark the behavioral output—but some are more for this transformation process that occurs in between.”

Researchers captured 3D images of the regions of the Drosophila central nervous system that are activated in response to noxious stimulation. The posterior medial center (red), which is located between sensory- and motor-related regions of the nervous system, is important for making behavioral decisions. [Yujia Hu, U-M Life Sciences Institute]

The studies showed that when sensory neurons detect the harmful external stimuli, they send information to second-order neurons in the central nervous system. One region of the nervous system in particular, called the posterior medial core, was found to respond 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 thus get amplified through increased recruitment of second-order neurons to the neural network—what the researchers refer to as escalated amplification. A mild stimulus might activate two second-order neurons, for example, while a more intense stimulus may activate 10 second-order neurons in the network. This larger network can then prompt a behavioral response.

But to make a “yes/no” decision, the nervous system needs a way not just to amplify information (for a “yes” response), but to also suppress unnecessary or less harmful information (for a “no” response). “Our sensory system detects and tells us a lot more than we realize,” said Ye, who is also a professor of cell and developmental biology in the U-M Medical School. “We need a way to quiet that information, or we would just constantly have exponential amplification.”

Using the 3D 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. In effect, the neural network suppresses neural signals caused by ‘weaker’ noxious stimuli, and amplifies those caused by intense stimuli. “… allowing animals to ignore weak stimuli and escape only from real harms.”

This mechanism effectively enhances the accuracy in animals’ decisions on whether or not to escape from noxious stimuli. “In this study, we identify a neural network that categorizes noxious stimuli of graded intensities to generate binary escape decisions in Drosophila larvae, and unravel a gated amplification mechanism that underlies such binary categorization,” the authors concluded. “In responding to the noxious stimuli, whereas failure in prompt responses may cause harm, excessive escape responses to negligible stimuli would lead to the loss of resources for survival. The gated amplification mechanism could reduce the responses to negligible stimuli whereas enhancing the responses to intense stimuli. In this way, the accuracy in deciding whether to escape from the stimuli is enhanced.”

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