A multi-institutional team of researchers led by Virginia Tech's Fralin Biomedical Research Institute at VTC has for the first time identified specific patterns of brain chemical activity that predict how quickly individual honey bees learn new associations, offering important insights into the biological basis of learning and decision-making.
The findings, which were published in Sciences Advances , found that the balance between the neurotransmitters octopamine and tyramine can predict whether a bee will learn quickly, slowly, or not at all as they associate an odor with a reward.
Because the same ancient brain chemicals that guide learning in bees also shape attention and learning in people, the findings may help scientists better understand why individual humans learn at different speeds — and how those processes may go awry in a variety of brain disorders.
Specific patterns of brain chemical activity appear before learning begins and again when a learned behavior first emerges, signaling how quickly an individual bee will learn. The research can help explain how chemicals in the brain drive attention and reinforce learning, with implications for fundamental biology, medicine, and agriculture.
The study also highlights the power of combining neuroscience with machine learning to research complex brain chemistry in a living brain in real time. By measuring the release of multiple neurotransmitters simultaneously, researchers can begin to understand how complex interactions shape learning across species.
Probing the bee brain
Bees buzzed in the lab of computational neuroscientist Read Montague, a professor with the Fralin Biomedical Research Institute at VTC , who collaborated on the project with Brian Smith, a professor and behavioral neuroscientist at Arizona State University .
The work builds on Montague's earlier research on bee learning. In a paper published in Nature in 1995 that has been cited more than 400 times, Montague and colleagues devised a computer model that predicts how signals from a specific individual neuron helps bees forage in unknown environments by learning which sights and smells are worth pursuing.
That work was based on the earlier contributions of the late neuroscientist Martin Hammer, whose research advanced scientific understanding of the neural mechanisms of learning and memory in insects.
Bees live relatively short lives in complex social systems and offer researchers a model to study cognition in natural and lab settings.
The insects are surprisingly sophisticated.
"A bee cannot come into the world knowing what it has to know in order to find flowers and harvest nectar and pollen," Smith said. In addition, a forager bee has a lifespan measured in weeks, all spent within a three- to five-mile radius of its colony. "That's a huge area for an animal with a tiny brain."
The environment is also constantly changing, with flowers blooming and declining over hours and days and weeks.
"That bee has to be a learning machine," Smith said. "You have to be prepared to forget what you learned yesterday and learn something new today. And if they can't do that, they'll never be able to perform their task within the colony."
In Montague's computational model of learning, bees learn from a series of successive predictions that may lead to reward.
"Bees have sophisticated systems for pursuing this," Montague said. "They can use the systems to make cautious or risky choices."
The models matched the way the insects behaved in observed experiments. "I applied it to the bee brain and showed that you could, in theory, guide the bee from flower to flower in a way that completely matched the statistics of foraging of the bee," he said.
From humans to bees
During a visit with Smith and his colleagues at Arizona State University, Montague was sharing his latest research into new methods that he and his team at Virginia Tech had made to make real-time, sub-second measurements of monoamines, including dopamine and serotonin, in human patients undergoing deep-brain stimulation treatment for Parkinson's disease and essential tremor.
Smith was familiar with Montague's 1995 paper. His own research focuses on learning and memory in insects and mammals, including how animals learn about odors — research that can help inform neurological conditions.
When Smith learned about Montague's groundbreaking work in humans, he reached out with a question.
Could Montague put his electrodes in a bee brain?
Soon after, Smith and his bees were on a plane to the research institute in Roanoke.
Montague's earlier work had been theoretical.
"We didn't even have a method to measure monoamines and bee brains," Montague said. "Now we've taken this work in humans and we've transported it back down to teeny tiny electrodes that we can put in a bee brain while the bee is learning and conditioning."
Even though their brains are small, bees have a lot to teach us.
"We're trying to push the bee as a model for some surprisingly sophisticated kinds of learning and memory tasks," Smith said. "I've always been interested in being able to measure these neurochemicals as they're released in real time to understand what kind of signals they give rise to that cause a neural network to go into one state or another. To remember something, or literally to forget something."
Related brain chemicals are at play in addiction, major depression, and attention deficit disorder, among other conditions. These ancient chemicals and systems have evolved in bees over 130 million years.
"These are evolutionarily very, very old systems that we still have in our brains," Montague said. "You can condition the bee on stimuli in the world that are relevant in a person."
The chemical-learning connection
Honey bees are a well-established model for studying learning because they rapidly form associations between odors and food rewards. In the Roanoke lab, researchers studied the proboscis extension response, in which a bee extends its feeding tube when it learns that a particular odor predicts sugar.
Seth Batten, a senior research associate in Montague's lab, had experience measuring neurotransmitters in several organisms. "But never in a bee," he said. "Not only was it a fun and challenging engineering feat to create systems that could take the measurements in such a small brain, but it was remarkable to see how complex these creatures were and how quickly some of them learned."
While some bees learned after only a few odor-reward pairings, others require many repetitions or never learn in the same timeframe.
Researchers recorded sub-second estimates in four key neurotransmitters important to bees' sensory processing and learning: dopamine, serotonin, tyramine, and octopamine. The measurements were taken from the antennal lobe, an early processing center for smell, using a machine-learning technique that tracks multiple chemicals simultaneously.
They found that bees could be grouped into learners and non-learners based on whether they developed a conditioned response to odor. Among bees that learned, some formed the association after only three odor-and-sugar trials, while others required up to eight. That variation was strongly linked to the timing and strength of antagonistic signals between octopamine and tyramine.
Bees with an earlier, stronger signal during their first exposure to an odor tended to learn faster once rewards were introduced. This relationship held even though the odor had not yet been paired with sugar.
The same push-and-pull pattern between octopamine and tyramine appeared again when bees first showed a learned response, and it still reflected how quickly they learned. Dopamine and serotonin did not show this pattern.
As learning progressed, neurotransmitter patterns continued to diverge between learners and non-learners. In bees that learned, octopamine and tyramine responses changed markedly after the learned behavior appeared, while dopamine and serotonin levels gradually declined across training. Non-learners showed little change over time.
The findings suggest that signaling between octopamine and tyramine plays a central role in setting learning sensitivity and regulating how long learning continues once an association is formed.
"In terms of biomedicine, understanding neural networks gives us some insight into how larger brains work," Smith said.
In addition to informing basic science and human and animal health, the research has implications for the food supply because of bees' role as pollinators. "So much of our agriculture is dependent on bees," Smith said.
