"Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively" puts forward a new take on Bayesian integration of multimodal cues. Instead of assuming a special area in the brain, where evidence from various sensory cues is combined (as in Ma and all, 2006), the authors consider a scenario, whereby each area receiving direct afferent input from a single modality (i.e. In the example analysed by the authors, and under a number of suitable assumptions, the cue integration they observe in their networks is close to Bayes-optimal. Building up on work of Fung and all (2010), the authors derive theoretical predictions for the integration of information in reciprocally coupled ring attractors (CANNs), which they also confirm by simulations. The reader is led through the general steps of the analysis, while details are provided in the supplementary material.

Psychophysical experiments have demonstrated that the brain integrates information from multiple sensory cues in a near Bayesian optimal manner. The present study proposes a novel mechanism to achieve this. We consider two reciprocally connected networks, mimicking the integration of heading direction information between the dorsal medial superior temporal (MSTd) and the ventral intraparietal (VIP) areas. Each network serves as a local estimator and receives an independent cue, either the visual or the vestibular, as direct input for the external stimulus. We find that positive reciprocal interactions can improve the decoding accuracy of each individual network as if it implements Bayesian inference from two cues. Our model successfully explains the experimental finding that both MSTd and VIP achieve Bayesian multisensory integration, though each of them only receives a single cue as direct external input. Our result suggests that the brain may implement optimal information integration distributively at each local estimator through the reciprocal connections between cortical regions.