Independence and coherence in temporal sequence computation across the fronto-parietal network

Time processing requires distributed and coordinated cortical dynamics, yet how multiple brain areas flexibly switch between coherent and independent temporal representations remains unclear. Using mesoscale two-photon calcium imaging, we simultaneously recorded neuronal populations in the secondary motor cortex and posterior parietal cortex of mice performing a novel alternating-interval timing task. Both areas encoded elapsed time through similar high-dimensional sequential activity, and decoding analyses revealed both coherent temporal errors shared across areas and independent errors confined to one area. Communication-subspace analysis showed that temporal information was distributed across multiple low-variance shared dimensions, whereas the dominant shared dimension preferentially encoded behaviour. A twin recurrent neural network model with sparse inter-network coupling and shared high-variance noise reproduced these experimental findings. Perturbation and local Lyapunov exponent analyses further showed that different shared subspaces selectively promote coherent or independent modes. These results reveal how sparse coupling and shared global fluctuations enable robust yet flexible fronto-parietal temporal computation.