State-Gated Learning Shapes the Geometry of Cooperation and Competition in Recurrent Networks

Learning reshapes how units in a recurrent network cooperate and compete. When learning conditions change, can interactions formed earlier remain functionally relevant under the same present conditions? We address this question with State-Gated Explaining-Away Plasticity (SG-EAP), a normative recurrent model in which a supplied state changes activity regularization and inhibitory gain during reconstruction learning. Different learning states bias connectivity among similarly driven units toward cooperative or competitive geometry. These biases remain detectable during adaptation even when networks with different histories share the same final learning condition and evaluation inputs. We then intervene on the retained geometry within the same matched-history pair. Under a common low-gate condition, implanting the similarity-aligned component from the previously high-gate network improves classification with missing features, whereas replacing that component with the low-history value reduces it. Each edited representation is evaluated with a linear readout refitted on complete responses. The reciprocal effect exceeds all 16 tested equal-norm orthogonal exchanges in each of ten independent input batches. It explains part of the history-dependent performance difference and depends on the recurrent background. These results identify an interpretable structural trace through which earlier learning can partially shape computation under feature loss during ongoing adaptation.