Functional compatibility as a determinant of persistent neural learning

The paper demonstrates that functional compatibility—how well new learning can coexist with behavior that must be preserved—is a causal determinant of persistent neural learning. By deliberately altering compatibility between matched neural states, the authors show that persistent learning can be measured under a common retention requirement across different learning directions, architectures, and data modalities. The study reveals that learning rules vary in how efficiently they exploit compatibility, and that retention constraints and finite updates limit what can be stored, with nonlinear geometry ultimately preventing full compatibility realization.

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