Many people consider Shinjuku and Shibuya as some of the most challenging stations to navigate, due to the high traffic and the convergence of over 10 lines. And I agree! It's overwhelming to exit the station amid a sea of people. And if you've ever used the Yamanote line, you'll know that missing a train isn't a big deal since another arrives in just 3 minutes. Considering that 15 million people commute daily across the city in all directions, managing this network must be incredibly stressful. As a passenger, I simply relax and appreciate the trains' efficiency, without delving into the complexities behind the scenes. It's fascinating to think about how Tokyo developed such an exceptional railway network.

In my previous article titled "How Do Japanese Trains Stay on Time?" , I explored the complexity of Japan's efficient train network. This newsletter continues that journey, seeking to uncover the mysteries behind this remarkable system. My interest was piqued by a conversation on Joe Rogan's podcast featuring mycologist Paul Stamets and it was featured many years ago. (Sorry to find out late :D) Surprisingly, Stamets suggested that the development and refinement of Japan's train system were influenced by the patterns of slime mold.

Upon hearing this, I was initially skeptical: Wait, how can this be true?

Driven by curiosity, I embarked on a deeper investigation to verify the accuracy of this claim. This newsletter delves into the research of three scientists who conducted an experiment with slime mold, focusing on developing the most efficient routes for the Kanto region. The information presented here is taken from the original Japanese post authored by these researchers.

Tokyo stands out among other major cities, both in Japan and internationally, due to its heavy reliance on railways. Essentially, the city's development has been closely intertwined with its railway system. While railways are crucial to Tokyo's infrastructure, they also face significant challenges. Finding the most efficient path is one of them and the research was based on this question. (Reference)

Previously, Dijkstra's algorithm was commonly used to solve shortest path problems in road networks. This is a fundamental algorithm in computer science, which is designed to determine the shortest path between nodes in a graph. However, its effectiveness reduces as the number of nodes increases, resulting in significantly longer computational times. To address this, two biologically-inspired methods were introduced: the genetic algorithm and the ant algorithm. Despite their innovation, these methods fall short in consistently offering the shortest path. Another difficulty, was their inability to adapt flexibly, particularly in situations like traffic congestion or accidents. Therefore, the researchers’s challenge was to developing a method that not only rapidly computes all possible paths and identifies the shortest route, but also adapts to changing conditions.

For addressing this challenge, a novel approach inspired by the amoeboid organism, the true slime mold, emerged. This method is encapsulated in a mathematical path-finding model that mimics the behavior of slime mold or known as 'Physarum solver'. This biologically inspired method offers a solution for efficient road-network navigation, effectively meeting the requirements of speed, accuracy, and adaptability in route finding. (Reference: Physarum solver : A biologically inspired method of road-network navigation)

Hold on, some of you might be wondering, what exactly is a slime mold? Ah, pardon me, let me backtrack for a moment. Slime mold or Physarum Polycephalum is a soil-dwelling amoeba, is a brainless, single-celled organism that often contains multiple nuclei. Is also slick and sticky as shown in yellow color of the photo below. (Reference)

This photo shows a slime mold with two parts: a sheet-like structure around the edge and a tubular structure resembling a line. The sheet's thickness pulsates every 1-2 minutes, aiding the transport of the slime mold body and nutrients through the tubes. These tubes thicken with increased transport and thin out when the flow is less.

Background and history of research

A research team from Japan, including Toshiyuki Nakagaki, a former scientist at RIKEN and now an associate professor at Hokkaido University, received the Ig Nobel Prize in cognitive neuroscience. This prize, known for its playful nature and as a humorous counterpart to the Nobel Prizes, was awarded for their discoveries about the intelligence of Physarum polycephalum. This single-celled, amoeba-like organism, despite lacking a brain or nervous system, demonstrated an ability to find the shortest path through a maze. It naturally spread across all available paths, forming a network of tubes that efficiently connected various food sources. Through this process, it inadvertently identified the most direct routes, effectively 'solving' the maze. The ability of this organism to create optimal networks invites us to reconsider conventional views on problem-solving and intelligence in living beings.

Although slime molds are unicellular organisms(*composed by a single cell), they have an adaptive network that transports nutrients within the body. They are also living creatures with many internal cores and have a collective nature. For example, if you cut it into several parts with a knife, each part can live as a separate individual; on the other hand, if you put them together, they can live as a single individual. Slime molds can be cut and pasted freely in this way, making them an excellent material for understanding adaptive networks.

Experiment

For their experiment as shown on photo below, the professors prepared a 30 cm square agar plate, marking a map of the Kanto region on it. They placed slime mold in a Kanto*-shaped container with bait at major city locations and a larger bait inside the Yamanote Line area (Figure A). To guide the mold, they exposed areas representing mountains, rivers, and the ocean to light, which slime molds avoid. Then introduced slime mold near the Tokyo Station area.

This led to the formation of a network linking Tokyo Station to the food sites, mirroring the actual JR railway map. The mold spreads towards the bait (Figures B and C), eventually formed a network akin to the human-designed railway system, efficiently connecting cities by forming a tubular network connecting the baits (Figures D and E).

In Figure F, the transport network is advantageous for slime molds. By selectively connecting or disconnecting, an efficient network connecting major stations was formed. The experiments from figure A to F used slime molds in a flat, uniform setting. However, real terrain varies with high mountains and lakes making railway construction challenging. These areas, often costly, were simulated in the experiment by projecting light onto regions representing high altitudes and lakes, as slime molds avoid light. Thus, areas which are difficult for railway construction due to terrain or cost, are areas where slime molds are less likely to be found. (Reference)
*Kanto is the largest island of Japan including: Tokyo, Kanagawa, Tochigi, Chiba, Saitama, Ibaraki and Gunma prefecture.

So now we have the network system built. In the image below the left image (a) is the actual JR network. On the right side (b) is the network created by slime molds, which is very similar to the network planned and created by humans (Illustration provided by Associate Professor Seiji Takagi of Future University Hakodate). (Reference)

Other Facts

Professor Toshiyuki Nakagaki discussed in another article the intriguing capabilities of slime molds. Despite being single-celled organisms without brains, slime molds exhibit behaviors as if they possess cognitive abilities. Nakagaki explained that these organisms also have a form of memory related to time. In a conducted experiment, a passageway was created, and a slime mold was placed at one end. Initially, the external conditions were set to a comfortable environment for the slime mold—25 degrees Celsius and 90% humidity—prompting it to move towards the other end of the passageway.

However, an hour into the experiment, the temperature was suddenly lowered to 22 degrees and the humidity to 60%, causing the slime mold to halt its movement. After 10 minutes, the conditions were restored to the original settings, and the slime mold resumed its motion. This process of altering the temperature and humidity for 10 minutes every hour was repeated three times. Each time, the slime mold responded by stopping its movement when the conditions became unfavorable and then resuming once the environment returned to its preferred state. This pattern suggests that slime molds have the ability to remember and react to time-based changes in their environment. (Reference)

In the fourth iteration of the experiment, despite the temperature and humidity remaining stable and comfortable for the slime molds, they unexpectedly ceased movement. This response occurred even though there was no actual change in the external conditions. When this experiment was replicated 100 times with different slime molds, approximately half of them exhibited the same behavior, anticipating a fourth change and altering their movements accordingly. This consistent pattern of behavior among a significant portion of the slime molds suggests that they possess a capability akin to "memorizing time." This implies that slime molds can remember and anticipate the timing of regular environmental changes, even in the absence of immediate sensory cues.

The ability of slime molds to predict and react to expected environmental changes, based on previous experience, indicates a form of biological memory or time perception that operates on a cellular level. This discovery not only sheds light on the complex behaviors of simple organisms but also opens new avenues for exploring how memory and anticipation can manifest in biological systems.

Regarding my initial question in the newsletter and the statement made by the mycologist Paul Stamets, it appears that he was indeed correct in his assertion. His insights about the capabilities of fungi or similar organisms are validated by the findings from Nakagaki's research. I’m excited to see other use cases how they are applied in various real-world scenarios.

If you are curios to listen more about slime molds, there is a TED Talk hosted by Professor Toshiyuki Nakagaki himself where he explains in details the experiment conducted for the train networking system and more:

References used for this article:

https://www.jps.or.jp/books/jpsjselectframe/2011/files/11-7-1.pdf

https://www.jst.go.jp/kisoken/presto/research/H22mise_terou.pdf

https://www.imi.kyushu-u.ac.jp/post-catalog/catalog-3116/

https://www.rikelab.jp/post/3252.html

https://www.estfukyu.jp/pdf/2019kotsukankyotaisho/kotsukankyotaisho11_digest.pdf

https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/28042/1/PASMA363-1.pdf

https://www.nature.com/articles/35035159

https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/28042/1/PASMA363-1.pdf