Dimensionless Controls of Plasticity Under Alternating Tasks: From Evolutionary Biology to Continual Learning
Plasticity under changing environments is central to both evolutionary biology and continual learning. Motivated by recent work on genotype--phenotype maps, we study a minimal deep-learning analogue where a network is trained alternately on two Boolean label sets, and ask which biological controls of plasticity survive the translation to gradient descent. Reinterpreting four proposed biological factors as quantities of training dynamics, we find the system reduces to two dimensionless controls: the task disagreement $r$, the fraction of disagreeing labels, and the reach $ηT$, the product of learning rate and switching period. We derive two bounds on plasticity: $r$ alone fixes an extremal...
| Authors | Owen Skriloff |
|---|---|
| Primary category | math.OC |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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