Even the humble fruit fly craves a dose of the happiness hormone. This is according to a new study from the University of Sussex showing how they can use dopamine to learn in ways similar to humans.
Computer science experts at the University of Sussex have developed a new computational model that demonstrates a long-awaited link between insect and mammalian learning, as described in a new article published today in Nature Communications.
Dr. James Bennett and colleagues built in anatomical and functional data from recent experiments and modeled how the anatomy and physiology of the fruit fly brain can support learning according to the RPE (reward prediction error) hypothesis.
The computational model shows how dopamine neurons in an area of the brain of a fruit fly known as the mushroom body can generate signals similar to dopamine neurons in mammals, and how these dopamine signals can reliably instruct learning.
Scientists believe that determining whether flies also use prediction errors to learn could lead to more humane animal research, allowing researchers to replace animals with simpler insect species in order to study the mechanisms of learning in the future.
By opening up new ways to study neural learning mechanisms, the researchers hope that the model could also be helpful in providing a better understanding of mental health problems such as depression or addiction, which are based on the RPE hypothesis.
Dr. Bennett, a research fellow at the School of Engineering and Informatics at the University of Sussex, said: “With our computational model, we were able to show that data from insect experiments did not necessarily conflict with predictions from the RPE hypothesis, as previously assumed.
“Bridging the gap between insect and mammalian studies for learning could open up the possibility of harnessing the powerful genetic tools available to conduct insect experiments and the smaller scale of their brains to assess brain function and disease in mammals , including people, understand. “
Understanding how mammals learn has come a long way thanks to the RPE hypothesis, which suggests that associative memories are learned in proportion to their inaccuracy.
The hypothesis has had considerable success in explaining experimental data on learning in mammals and has been extensively applied to decision-making and mental illnesses such as addiction and depression. However, scientists have struggled to apply the hypothesis to insect learning due to conflicting results from various experiments.
The University of Sussex research team created a computational model to show how the main features of the anatomy and physiology of the mushroom body can implement learning according to the RPE hypothesis.
The model simulates a simplification of the mushroom body, including different types of neurons and the connections between them, and how the activity of these neurons promotes learning and influences the decisions a fly makes when certain decisions are rewarded.
To better understand learning in the fly’s brain, the research team used their model to make five novel predictions about the influence of different neurons in the mushroom body on learning and decision-making, in the hope that they will fuel future experimental work.
Dr. Bennett said, “While other models of the mushroom body have been made, to the best of our knowledge, no other model has shown connections between dopamine neurons and another group of neurons that predict behavior and lead to rewards. For example, when the reward is sugar content These correlations would make it possible to compare the predicted sugar availability with the actual sugar ingested, whereby more precise predictions and suitable behaviors in the search for sugar could be learned.
“The model can explain a variety of behaviors that fruit flies exhibit when experiments are carried out to either silence or artificially activate the activity of certain neurons in their brain. We also suggest connections between dopamine neurons and other neurons in the mushroom body that do this have not yet been reported in experiments, but would help explain even more experimental data. “
Thomas Nowotny, Professor of Computer Science at the University of Sussex, said: “The model combines learning theory and experimental knowledge in a way that allows us to systematically think about how fly brains actually work. The results show how learning in simple flies can be more similar to how we learn than previously thought. “
Source of the story:
Materials provided by University of Sussex. Originally written by Neil Vowles. Note: The content can be edited by style and length.
