Speaker
Description
The neural activity patterns underlying decision formation and execution have been extensively characterized, yet the connectivity motifs that generate these computations remain difficult to measure directly. Although theoretical models propose diverse circuit architectures for decision-making, inferences based on measurements of neural activity alone cannot readily distinguish among them or even determine whether decision signals arise from local microcircuit connections or are inherited from distributed inputs. Here we used influence mapping — combining single-cell photostimulation with calcium imaging — to measure effective connectivity motifs in posterior parietal cortex (PPC) during a navigation decision task. We found that PPC connectivity is dominated by two inhibitory motifs that dynamically switch PPC between distinct computational regimes within single trials. During sensory evidence accumulation, photostimulation preferentially excited neurons with matching choice selectivity while suppressing neurons with opposite selectivity, consistent with an opponent inhibition motif that supports decision formation. During the subsequent delay period, neurons instead preferentially suppressed neurons with matching selectivity, forming an asymmetric like-suppresses-like motif that propagated choice information sequentially across neuronal populations. Computational modeling showed that competition between these inhibitory motifs enables PPC circuits to switch from decision formation to propagation of decision-related activity. Together, these results localize core decision computations to PPC microcircuits and reveal that, unlike in traditional models, inhibitory connectivity motifs are critical for decision-making, including as drivers of transitions between decision formation and execution.