​​​Talk from Joao Barbosa – Laboratoire de Neurosciences cognitives, Université PSL

Short abstract:

​Previous work investigating the neural dynamics underlying context-dependent decision making typically analyses a single brain region or recurrent neural network (RNN). However, evidence suggests that the information required to solve tasks is distributed across multiple regions. Here, we investigate the neural dynamics across seven brain regions of the non-human primate brain where such distributed information has been observed. 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. 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.​​​​