Showcasing the Computing Ability of Ecosystems through Eco-Computing

The development of neural networks or artificial intelligence tools for data analysis is increasing exponentially. However, networks found in natural ecosystems, such as networks of relationships between species, have information processing potential that has remained largely untapped.

Now, a study conducted at Kyoto University has demonstrated the computational power of ecosystems, providing a new direction for the rapid development of AI technologies. Simulations confirmed that ecological networks, such as prey-predator interactions, can efficiently process information and use it as a computational resource.

“We’ve called this approach ecological reservoir computing,” says Kyoto University lead author Masayuki Ushio.

Researchers have developed two types of environmental reservoir computing as a proof of concept that environmental networks have computational power.

One type is a computer-based approach called in silico environmental reservoir computing, which models hypothetical ecosystem dynamics and simulates system response. The second is an experimental system called Real-Time Ecological Repository Computing, which uses the real-time population dynamics of the single-celled organism Tetrahymena thermophila.

In a second approach, to confirm the computational power of a natural ecosystem, Ushio’s team created an experimental design using Tetrahymena thermophila. After entering the values ​​as the temperature of the culture medium – or the input data – the team got the cell numbers as the system’s output. The study confirmed the possibility that tetrameric populations can make predictions of the near future of environmental time series.

“Our results also suggest that there may be a link between high biodiversity and high computational power, which sheds light on new values ​​of previously unknown biodiversity,” adds Ushio, who is currently a principal investigator at the Hong Kong University of Science and Technology.

“The direct relationship between community diversity and computational ability may enhance biodiversity quotient.”

Ecological communities process a large amount of information in real time in a natural ecosystem, where the potential for ecological interactions as a new computing method is exponentially high.

Ushio concludes, “Our new computational method may lead to the invention of new types of computers. Also, in developing a method for measuring information processing capacity in a natural ecosystem, we may find clues as to how ecosystem dynamics are maintained.”

The paper “Computational Ability of Environmental Dynamics” was published in Royal Society for Open Science.