Estimating flexible across-area communication with neurally-constrained RNNs

Previous work investigating the neural dynamics underlying context-dependent decision making typically analyses a single brain region or recurrent neural network (RNN) [1,2]. However, evidence suggests that the information required to solve tasks is distributed across multiple regions [3]. Here, we investigate the neural dynamics across seven brain regions of the non-human primate brain where such distributed information has been observed[3]. By examining within-region geometry and dynamics, we identified significant differences not captured by classical decoding analyses. Using multi-regional RNNs trained on condition-averaged data, we explored how inter-area interactions shaped neural representations. Our findings reveal that even when task-inputs were withheld from frontal regions during testing, these regions still encoded stimulus information and generated response codes, similar to brain data. Moreover, networks in which across-region interactions were blocked could represent stimuli but failed to solve the task and lacked attractor states for current contexts. Gradually disconnecting regions led to an abrupt breakdown of task-solving capabilities, analogous to spatial bifurcation phenomena [4]. Perturbation experiments highlighted the differential contributions of various regions, offering predictive insights for future experimental validation. These results underscore the critical role of inter-regional communication in task performance and provide a framework for understanding distributed neural processing.