Dendritic Computation Shapes How Networks Learn to Use Context

Flexible behavior requires learning how to use sensory and contextual information. A recent in vivo study found that activating dendrite-inhibiting interneurons impaired rule relearning while sparing established behavior. How dendritic input organization shapes learned sensorimotor mappings remains unclear. Here we develop a trainable framework of recurrent circuits built from biophysically detailed neurons to manipulate input location and dendritic conductance during learning and execution. In a controlled task, sensory input encodes both direction and rule, while a separate context stream repeats the rule. Apical and basal routing yield similar performance with all inputs present, but apical routing produces less dependence on context after learning. Exchanging sensory weights after training and sharing them during training show that learned sensory parameters contribute to both the expression and formation of this difference. Blocking the apical calcium conductance during training partially narrows the post-withdrawal performance gap even after conductance restoration, whereas acute blockade has much smaller effects on performance without context. Within apical-context circuits, exchanging sensory weights between these training histories also transfers this persistent effect, linking conductance conditions during learning to functionally consequential sensory parameters. A simulated robotic pushing task further shows that context availability during learning affects subsequent closed-loop control under common execution conditions. These findings identify dendritic computation as a source of learning bias, linking cellular input organization and biophysics to learned sensorimotor mappings, with a proof of concept in artificial control.