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

Article https://doi.org/10.1038/s41467-026-73999-w Independence and coherence in temporal sequence computation across the frontoparietal network Received: 2 September 2025 1234567890():,; 1234567890():,; Accepted: 21 May 2026 Check for updates Hiroto Imamura, Fumiya Imamura, Reiko Hira Riichiro Hira , Yoshikazu Isomura & 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. Temporal information processing underlies a wide spectrum of cognitive functions1–3. Neural representations of time have been widely observed in higher-order cortical areas and subcortical structures such as the basal ganglia and cerebellum4–22. Elapsed time has traditionally been observed as ramp-up or ramp-down patterns of neural activity, while sequential activity, where neurons that peak at intermediate times fire in succession, is also widespread17,21,23. In non-human primates, both ramping activity and sequential firing patterns have been reported in frontal and parietal cortices during interval timing tasks, suggesting that these coding motifs are conserved across species17,24–26. For time processing on the scale of seconds, which is critical for working memory and action planning and preparation, higher-order cortical areas, including the prefrontal cortex, secondary motor cortex (M2), and posterior parietal cortex (PPC), play particularly important roles. Because time-representing areas are thought to be linked functionally, their temporal representations might be globally synchronized1,5,27. Conversely, when each area must track the elapsed time from different events, their activity could become asynchronous. When multiple brain areas share temporal information, it is thought that they must flexibly switch between a coherence mode, in which all regions operate on a shared clock, and an independence mode, in which each region maintains its own local clock. In considering such inter-areal coordination, the communication subspace hypothesis28–30, in which inter-areal interactions are mediated by selective, low-dimensional population activity patterns, provides a compelling framework. For temporal representations, understanding how switching between these two modes is implemented will thus require analyzing how communication subspaces differ across modes. Department of Physiology and Cell Biology, Graduate School of Medical and Dental Sciences, Institute of Science Tokyo, Tokyo, Japan. e-mail: ; Nature Communications | (2026)17:5174 1 Article https://doi.org/10.1038/s41467-026-73999-w In light of these general considerations, the M2–PPC circuit provides a particularly tractable system. M2 and PPC are reciprocally connected and are known to support various cognitive functions, including working memory and sensory prediction31–36. In workingmemory tasks, recurrent cortico-cortical connections between M2 and PPC are essential for maintaining the memory trace31. Interestingly, the dominant component of neural activity shared by M2 and PPC was unrelated to working memory31. The principal dynamics shared by a pair of cortical areas may also be shared across many regions and have been suggested to relate to motor information21,37,38. Anatomically, M2 and PPC exhibit a distinctive organization characterized by sparse long-range excitatory projections and shared inputs from widespread sources33,34,39–41, features that are theoretically well suited for modulating the switch between independence and coherence. Moreover, the two areas are on the dorsal cortical surface of the mouse brain, making them ideally positioned for simultaneous large-field two-photon imaging and direct comparison between experimental data and computational models. Modelling studies using recurrent neural networks (RNNs) have substantially advanced our understanding of how second-scale temporal processing can be implemented by recurrent neural circuits42–47. However, most existing models focus on a single functional circuit and omit key biological constraints, such as shared low-frequency noise and sparse long-range excitation, thereby limiting their correspondence with experimental observations. A biologically grounded multiRNN framework that incorporates communication subspaces is therefore required to bridge the gap between experimental findings on cortico-cortical communication and theoretical models. Here we combine behaviour, mesoscale imaging, and modelling to uncover how the M2–PPC network balances coherent and independent temporal coding. We therefore hypothesized that this balance arises from the interplay between sparse inter-areal excitation and shared low-frequency global fluctuations. Specifically, we (1) developed a novel alternating-interval task in which 6-s and 12-s a intervals appear alternately, (2) simultaneously recorded thousands of neurons in M2 and PPC using a large field-of-view two-photon microscope21,48, and (3) constructed and analysed a twin RNN model using the acquired data to test this hypothesis under controlled circuit constraints. Our results support this two-factor mechanism, showing that sparse inter-areal excitation promotes coherence, whereas shared 1/f-type global fluctuations favour independence. More broadly, these findings suggest that distributed temporal coding across multiple brain areas reflects a trade-off between robustness and flexibility, enabling stable computation while supporting diverse behavioural demands. Results Development of an alternating-interval task M2 and PPC dynamically maintain a representation of continuous time and update their internal states in response to external inputs (Fig. 1a). This capability persists even when successive trials are separated by a long inter-trial interval49. To probe M2 and PPC effectively, we need a temporally continuous task with no distinction between individ (...truncated)