Even without a central brain, jellyfish can learn from past experiences like humans, mice, and flies, scientists report for the first time on September 22 in the journal Current Biology. They trained Caribbean box jellyfish (Tripedalia cystophora) to learn to spot and dodge obstacles. The study challenges previous notions that advanced learning requires a centralized brain and sheds light on the evolutionary roots of learning and memory.

No bigger than a fingernail, these seemingly simple jellies have a complex visual system with 24 eyes embedded in their bell-like body. Living in mangrove swamps, the animal uses its vision to steer through murky waters and swerve around underwater tree roots to snare prey. Scientists demonstrated that the jellies could acquire the ability to avoid obstacles through associative learning, a process through which organisms form mental connections between sensory stimulations and behaviors.

“Learning is the pinnacle performance for nervous systems,” says first author Jan Bielecki of Kiel University, Germany. To successfully teach jellyfish a new trick, he says “it’s best to leverage its natural behaviors, something that makes sense to the animal, so it reaches its full potential.”

The team dressed a round tank with gray and white stripes to simulate the jellyfish’s natural habitat, with gray stripes mimicking mangrove roots that would appear distant. They observed the jellyfish in the tank for 7.5 minutes. Initially, the jelly swam close to these seemingly far stripes and bumped into them frequently. But by the end of the experiment, the jelly increased its average distance to the wall by about 50%, quadrupled the number of successful pivots to avoid collision and cut its contact with the wall by half. The findings suggest that jellyfish can learn from experience through visual and mechanical stimuli.

“If you want to understand complex structures, it’s always good to start as simple as you can,” says senior author Anders Garm of the University of Copenhagen, Denmark. “Looking at these relatively simple nervous systems in jellyfish, we have a much higher chance of understanding all the details and how it comes together to perform behaviors.”

The researchers then sought to identify the underlying process of jellyfish’s associative learning by isolating the animal’s visual sensory centers called rhopalia. Each of these structures houses six eyes and generates pacemaker signals that govern the jellyfish’s pulsing motion, which spikes in frequency when the animal swerves from obstacles.

The team showed the stationary rhopalium moving gray bars to mimic the animal’s approach to objects. The structure did not respond to light gray bars, interpreting them as distant. However, after the researchers trained the rhopalium with weak electric stimulation when the bars approach, it started generating obstacle-dodging signals in response to the light gray bars. These electric stimulations mimicked the mechanical stimuli of a collision. The findings further showed that combining visual and mechanical stimuli is required for associative learning in jellyfish and that the rhopalium serves as a learning center.

Next, the team plans to dive deeper into the cellular interactions of jellyfish nervous systems to tease apart memory formation. They also plan to further understand how the mechanical sensor in the bell works to paint a complete picture of the animal’s associative learning.

“It’s surprising how fast these animals learn; it’s about the same pace as advanced animals are doing,” says Garm. “Even the simplest nervous system seems to be able to do advanced learning, and this might turn out to be an extremely fundamental cellular mechanism invented at the dawn of the evolution nervous system.”

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This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), the Danish Research Council (DFF), and the Villum Foundation.

Current Biology, Bielecki et al. “Associative learning in the box jellyfish Tripedalia cystophora” https://www.cell.com/current-biology/fulltext/S0960-9822(23)01136-3

Current Biology (@CurrentBiology), published by Cell Press, is a bimonthly journal that features papers across all areas of biology. Current Biology strives to foster communication across fields of biology, both by publishing important findings of general interest and through highly accessible front matter for non-specialists. Visit http://www.cell.com/current-biology. To receive Cell Press media alerts, contact press@cell.com.

Artificial intelligence (AI) can help plant scientists collect and analyze unprecedented volumes of data, which would not be possible using conventional methods. Researchers at the University of Zurich (UZH) have now used big data, machine learning and field observations in the university’s experimental garden to show how plants respond to changes in the environment.

Climate change is making it increasingly important to know how plants can survive and thrive in a changing environment. Conventional experiments in the lab have shown that plants accumulate pigments in response to environmental factors. To date, such measurements were made by taking samples, which required a part of the plant to be removed and thus damaged. “This labor-intensive method isn’t viable when thousands or millions of samples are needed. Moreover, taking repeated samples damages the plants, which in turn affects observations of how plants respond to environmental factors. There hasn’t been a suitable method for the long-term observation of individual plants within an ecosystem,” says Reiko Akiyama, first author of the study.

With the support of UZH’s University Research Priority Program (URPP) “Evolution in Action”, a team of researchers has now developed a method that enables scientists to observe plants in nature with great precision. PlantServation is a method that incorporates robust image-acquisition hardware and deep learning-based software to analyze field images, and it works in any kind of weather.

Millions of images support evolutionary hypothesis of robustness

Using PlantServation, the researchers collected (top-view) images of Arabidopsis plants on the experimental plots of UZH’s Irchel Campus across three field seasons (lasting five months from fall to spring) and then analyzed the more than four million images using machine learning. The data recorded the species-specific accumulation of a plant pigment called “anthocyanin” as a response to seasonal and annual fluctuations in temperature, light intensity and precipitation.

PlantServation also enabled the scientists to experimentally replicate what happens after the natural speciation of a hybrid polyploid species. These species develop from a duplication of the entire genome of their ancestors, a common type of species diversification in plants. Many wild and cultivated plants such as wheat and coffee originated in this way.

In the current study, the anthocyanin content of the hybrid polyploid species A. kamchatica resembled that of its two ancestors: from fall to winter its anthocyanin content was similar to that of the ancestor species originating from a warm region, and from winter to spring it resembled the other species from a colder region. “The results of the study thus confirm that these hybrid polyploids combine the environmental responses of their progenitors, which supports a long-standing hypothesis about the evolution of polyploids,” says Rie Shimizu-Inatsugi, one of the study’s two corresponding authors.

From Irchel Campus to far-flung regions

PlantServation was developed in the experimental garden at UZH’s Irchel Campus. “It was crucial for us to be able to use the garden on Irchel Campus to develop PlantServation’s hardware and software, but its application goes even further: when combined with solar power, its hardware can be used even in remote sites. With its economical and robust hardware and open-source software, PlantServation paves the way for many more future biodiversity studies that use AI to investigate plants other than Arabidopsis – from crops such as wheat to wild plants that play a key role for the environment,” says Kentaro Shimizu, corresponding author and co-director of the URPP Evolution in Action.

The project is an interdisciplinary collaboration with LPIXEL, a company that specializes in AI image analysis, and Japanese research institutes at Kyoto University and the University of Tokyo, among others, under the Global Strategy and Partnerships Funding Scheme of UZH Global Affairs and the International Leading Research grant program of the Japan Society for the Promotion of Science (JSPS). The project also received funding from the Swiss National Science Foundation (SNSF).

Strategic Partnership with Kyoto University

Kyoto University is one of UZH’s strategic partner universities. The strategic partnership ensures that high-potential research collaborations will receive the necessary support to thrive, for instance through the UZH Global Strategy and Partnership Funding Scheme. Over the last years, several joint research projects between Kyoto University and UZH have already received funding, among them “PlantServation”.