In a virtual navigation task, the posterior parietal cortex (PPC) of mice exhibited choice-specific sequences of neuronal activation. Neurons activated in transient, staggered patterns across the task, forming sequences that varied depending on the behavioral choice. These sequences were distinct and followed divergent trajectories in the state space of neuronal activity. Cells involved in different sequences were anatomically intermixed over microcircuit scales, suggesting that PPC performs computations through sequence-based dynamics rather than stable states. The PPC was essential for the memory-guided navigation task, as inactivation with muscimol significantly reduced performance. Neuronal activity patterns were imaged using two-photon microscopy, revealing task-modulated cells with transient activity. These cells showed choice-specific activity, with sequences of activation spanning the entire trial. The activity of these cells was not uniform across behavioral periods, indicating heterogeneous dynamics. Sequences were observed in both correct and error trials, with activity patterns varying across trials. The PPC's activity was not solely driven by visual or running patterns, suggesting a more complex role in decision-making and memory. Anatomical analysis revealed that cells with different response preferences were intermixed, indicating a lack of distinct cell classes. The PPC's dynamics were characterized by sequences of activity, with trajectories diverging and converging based on behavioral choices. These findings suggest that PPC uses sequence-based dynamics for decision-making and memory tasks, with anatomically intermingled microcircuits supporting these processes. The results highlight the importance of sequences in PPC function and suggest a framework for understanding decision-making and working memory in the brain.In a virtual navigation task, the posterior parietal cortex (PPC) of mice exhibited choice-specific sequences of neuronal activation. Neurons activated in transient, staggered patterns across the task, forming sequences that varied depending on the behavioral choice. These sequences were distinct and followed divergent trajectories in the state space of neuronal activity. Cells involved in different sequences were anatomically intermixed over microcircuit scales, suggesting that PPC performs computations through sequence-based dynamics rather than stable states. The PPC was essential for the memory-guided navigation task, as inactivation with muscimol significantly reduced performance. Neuronal activity patterns were imaged using two-photon microscopy, revealing task-modulated cells with transient activity. These cells showed choice-specific activity, with sequences of activation spanning the entire trial. The activity of these cells was not uniform across behavioral periods, indicating heterogeneous dynamics. Sequences were observed in both correct and error trials, with activity patterns varying across trials. The PPC's activity was not solely driven by visual or running patterns, suggesting a more complex role in decision-making and memory. Anatomical analysis revealed that cells with different response preferences were intermixed, indicating a lack of distinct cell classes. The PPC's dynamics were characterized by sequences of activity, with trajectories diverging and converging based on behavioral choices. These findings suggest that PPC uses sequence-based dynamics for decision-making and memory tasks, with anatomically intermingled microcircuits supporting these processes. The results highlight the importance of sequences in PPC function and suggest a framework for understanding decision-making and working memory in the brain.