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Networked Learning
This lesson describes how we can use Slime Mold to describe Networked Learning and apply that description to Classroom structure.

Learning Meta Data

Content Source

ASC Staff

First Published

November 8, 2023

Learning Topics

Learning Blockchain

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Networked Learning is the 21st Century way to learn.

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Talented and dedicated engineers spent countless hours designing Japan’s rail system to be one of the world’s most efficient. Could have just asked a slime mold.

When presented with oat flakes arranged in the pattern of Japanese cities around Tokyo, brainless, single-celled slime molds construct networks of nutrient-channeling tubes that are strikingly similar to the layout of the Japanese rail system, researchers from Japan and England report Jan. 22 in Science.

A new model based on the simple rules of the slime mold’s behavior may lead to the design of more efficient, adaptable networks, the team contends.

Some of the smartest folks in the world, using some of the smartest tools in the world, are, right now, working to optimize the Tokyo Rail System.

In contrast, the slime mold has no central brain or indeed any awareness of the overall problem it is trying to solve, but manages to produce a structure with similar properties to the real rail network.”

The yellow slime mold Physarum polycephalum grows as a single cell that is big enough to be seen with the naked eye. When it encounters numerous food sources separated in space, the slime mold cell surrounds the food and creates tunnels to distribute the nutrients. In the experiment, researchers led by Toshiyuki Nakagaki, of Hokkaido University in Sapporo, Japan, placed oat flakes (a slime mold delicacy) in a pattern that mimicked the way cities are scattered around Tokyo, then set the slime mold loose.

slime_mold_2Initially, the slime mold dispersed evenly around the oat flakes, exploring its new territory. But within hours, the slime mold began to refine its pattern, strengthening the tunnels between oat flakes while the other links gradually disappeared. After about a day, the slime mold had constructed a network of interconnected nutrient-ferrying tubes. Its design looked almost identical to that of the rail system surrounding Tokyo, with a larger number of strong, resilient tunnels connecting centrally located oats. “There is a remarkable degree of overlap between the two systems,” Fricker says.

The researchers then borrowed simple properties from the slime mold’s behavior to create a biology-inspired mathematical description of the network formation. Like the slime mold, the model first creates a fine mesh network that goes everywhere, and then continuously refines the network so that the tubes carrying the most cargo grow more robust and redundant tubes are pruned.

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The behavior of the plasmodium “is really difficult to capture by words,” comments biochemist Wolfgang Marwan of Otto von Guericke University in Magdeburg, Germany. “You see they optimize themselves somehow, but how do you describe that?” The new research “provides a simple mathematical model for a complex biological phenomenon,” Marwan wrote in an article in the same issue of Science.

Fricker points out that such a malleable system may be useful for creating networks that need to change over time, such as short-range wireless systems of sensors that would provide early warnings of fire or flood. Because these sensors are destroyed when disaster strikes, the network needs to efficiently re-route information quickly. Decentralized, adaptable networks would also be important for soldiers in battlefields or swarms of robots exploring hazardous environments, Fricker says.

The new model may also help researchers answer biological questions, such as how blood vessels grow to support tumors, Fricker says. A tumor’s network of vessels start out as a dense, unstructured tangle, and then refine their connections to be more efficient.

Images: Science/AAAS

See Also:

Networked Learning Bottom Line

Slime Mold spreads out, leaving either “attractant” or “repellent” “meta-data” for the next generation to use.

Over time and millions of iterations, the best path is found. And that best path is found without a “Controlling Mind.”

The Best path is found using a simple what is good for me approach. The key is that there is transparency; the previous generation must be honest on what is good and bad.

This should work for learning.