Graph Neural Networks for Connectivity Inference in Spatially Patterned Neural Responses

A continuous attractor network is one of the most common theoretical framework for studying a wide range of neural computations in the brain. Many previous approaches have attempted to identify continuous attractor systems by investigating the state-space structure of population neural activity. However, establishing the patterns of connectivity for relating the structure of attractor networks to their function is still an open problem. In this work, we propose the use of graph neural networks combined with the structure learning for inferring the recurrent connectivity of a ring attractor network and demonstrate that the developed model greatly improves the quality of circuit inference as well as the prediction of neural responses compared to baseline inference algorithms.