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Physical effects of learning
Menachem Stern1, Andrea J Liu1,2, Vijay Balasubramanian1,3,4
1Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.
Physical Review. E
|March 16, 2024
Summary
Learning in physical systems, like neural networks, leaves imprints on their structure. These imprints, observable in the system
Area of Science:
- Complex systems
- Physical networks
- Machine learning
Background:
- Interacting many-body physical systems exhibit learning capabilities.
- Learning occurs via evolutionary selection or active learning from experience.
Purpose of the Study:
- Investigate the structural imprints of learning in linear physical networks.
- Analyze the effects of weak input signals on system architecture.
Main Methods:
- Analysis of the Hessian matrix in physical systems.
- Characterization of system response to weak input signals.
Main Results:
- Learning decreases the effective physical dimension of input response.
- Learning increases the susceptibility of physical degrees of freedom.
- Low-eigenvalue eigenvectors of the Hessian align with learned tasks.
Conclusions:
- Observed effects characterize learning in physical systems under weak input conditions.
- These findings suggest methods for identifying trained physical networks.
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