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Learning Matters! The physics of continual learning in biological and soft matter

Sector: Power Generation (CCGT) • Location: Germany

Source: EU Funding & Tenders Portal

Project
Forthcoming

How many times have you wished you could simply pause your brain, only to find, to your frustration, that it is still busy, revisiting and processing inputs? Response to continual inputs is normal and essential, it is how we learn. In fact, any matter continually responds to input. Yet, our understanding of learning struggles with continual input. I here break with the current focus on the brain

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The project “Learning Matters! The physics of continual learning in biological and soft matter” is an infrastructure initiative in the Power Generation (CCGT) sector, located in Germany. Taiyo aggregates data on it from EU Funding & Tenders Portal.

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How many times have you wished you could simply pause your brain, only to find, to your frustration, that it is still busy, revisiting and processing inputs? Response to continual inputs is normal and essential, it is how we learn. In fact, any matter continually responds to input. Yet, our understanding of learning struggles with continual input. I here break with the current focus on the brain to uncover the physics of continual learning matter instead by investigating the emergence of learning bottom-up in life reduced in complexity to a network-shaped single cell – Physarum. Lacking any neurons, flows flushing throughout Physarum’s tubular network propagate input packaged as chemical concentration and flow shear force. Tube wall visco-elasticity re-organises in response – continually – learning its future response. Such mechanical learning serves as the blueprint for implementing learning in soft matter, as Physarum’s responsiveness in wall visco-elasticity has its soft matter twin in synthetic hydrogels. Implementing Physarum’s learning mechanics, cast in the language of theoretical physics, directly into soft matter, I uncover the physics of continual learning matter and, thus, develop biocompatible learning matter. My ambitious aim requires an interdisciplinary approach spanning biology, soft matter, and theoretical physics to 1) reveal how Physarum learns as its matter mechanically responds to input, 2) manufacture synthetic, soft matter flow networks that mimic Physarum’s learning mechanics, 3) derive a theory of continual learning from both biological and soft matter learning data to identify how learned behaviour is selected to last or vanish and 4) apply soft matter learning to initiate proof-of-concept vascularised tissue implants. My conceptual insight may inspire artificial intelligence but unfolds its ground-breaking power by making learning available in biocompatible soft matter with massive potential for medical treatment with tissue implants.

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