Navigating the world around us might seem like a complex task, but it turns out, we all have something in common with an electric knifefish. Whether it’s a dog sniffing around or a human glancing in a new setting, the core behavior of trying to understand one’s surroundings remains the same.
Recent research reveals that such movements aren’t unique to humans or even larger animals. It extends across a wide spectrum of organisms, from single-celled amoeba to complex beings like us.
“Amoeba don’t even have a nervous system, and yet they adopt behavior that has a lot in common with a human’s postural balance or fish hiding in a tube,” said study co-author Noah Cowan, a professor of mechanical engineering at Johns Hopkins.
“These organisms are quite far apart from each other in the tree of life, suggesting that evolution converged on the same solution through very different underlying mechanisms.”
This fascinating discovery originated from a study that was focused on the workings of the nervous system during movement to enhance perception. Observations of the electric knifefish, a creature emitting weak electric discharges to sense its location, were key.
In darkness, the fish shimmied back and forth more frequently than in the light. The darkness made the fish increase their movement, mimicking a rapid ‘explore mode’ to understand their environment better.
The concept of switching between an “explore mode” during uncertainty and an “exploit mode” when familiar with the environment isn’t restricted to these fish.
“We found that the best strategy is to briefly switch into explore mode when uncertainty is too high, and then switch back to exploit mode when uncertainty is back down,” said study first author Debojyoti Biswas, a Johns Hopkins postdoctoral researcher.
Supported by a model simulating these key sensing behaviors, the team identified similar patterns in amoeba, moths, cockroaches, moles, bats, mice, and even humans.
“Not a single study that we found in the literature violated the rules we discovered in the electric fish, not even single-celled organisms like amoeba sensing an electric field,” said Cowan.
To further illustrate the ubiquitous nature of these movements, Cowan related it to everyday human behavior.
“If you go to a grocery store, you’ll notice people standing in line will change between being stationary and moving around while waiting,” said Cowan.
“We think that’s the same thing going on, that to maintain a stable balance you actually have to occasionally move around and excite your sensors like the knifefish. We found the statistical characteristics of those movements are ubiquitous across a wide range of animals, including humans.”
Beyond just understanding the natural world, the implications of this research are vast. The findings have the potential to revolutionize robotics, especially in applications like search and rescue drones and space rovers.
Next, the experts will test whether their insights hold true for other living things, including plants.
Electric knifefish are a diverse group with over 200 species that are found primarily in Central and South America. These fish are known for their ability to generate electric fields, which they use for navigation, communication, and sometimes for capturing prey.
Electric knifefish have an organ that produces weak electric discharges. This is distinct from the strong electric discharges produced by electric eels, a different group of fish.
They use their electric field to sense their surroundings. Objects around them distort this field, and the fish can detect these distortions to understand their environment, much like a sonar.
Different species, and even individual fish, may have unique electric organ discharge patterns. This allows them to communicate and recognize each other.
They are primarily found in freshwater environments, especially in slow-moving or stagnant waters like swamps, ponds, and riverbanks.
Electric knifefish are typically nocturnal, using their electric fields to navigate in the darkness.
The study is published in the journal Nature Machine Intelligence.
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