Dubbed "liquid" networks, these flexible algorithms change their underlying equations to continuously adapt to new data inputs. This advance, say the researchers, could aid decision making based on data streams that change over time, including those involved in medical diagnosis and autonomous driving.
"This is a way forward for the future of robot control, natural language processing, video processing - any form of time series data processing," says Ramin Hasani, the lead author of a study on the research. "The potential is really significant."
Time series data - a sequence of data points indexed in time order - are both ubiquitous and vital to our understanding the world, say the researchers.
"The real world is all about sequences," says Hasani. "Even our perception - you're not perceiving images, you're perceiving sequences of images. So, time series data actually create our reality."
Time series examples such as in video processing, financial data, and medical diagnostic applications are central to society, say the researchers, but such data streams are ever changing and can be unpredictable. Yet analyzing these data in real time, and using them to successfully anticipate future behavior, can boost the development of emerging technologies like self-driving cars.
With this goal in mind, the resaerchers designed a neural network that can adapt to the variability of real-world systems. Neural networks are algorithms that recognize patterns by analyzing a set of "training" examples, and are often said to mimic the processing pathways of the brain.
In this case, say the researchers, they drew inspiration directly from the microscopic nematode, C. elegans .
"It only has 302 neurons in its nervous system," says Hasnai, "yet it can generate unexpectedly complex dynamics."
The researchers coded the neural network with careful attention to how C. elegans neurons activate and communicate with each other via electrical impulses, while allowing the parameters to change over time based on the results of a nested set of