Abstract
Weakly electric fish localize and identify objects by sensing distortions in a self-generated electric field. Fish can determine the resistance and capacitance of an object, for example, even though the field distortions being sensed are small and highly dependent on object distance and size. The neural computations underlying these remarkable behavioral capacities are poorly understood. We measured responses of electroreceptor afferents and constructed a filter-based model that accurately accounts for them. We also built models of the electric fields generated by the fish and of the distortions in these fields due to objects of different resistances and capacitances at different locations. Combining these models provides an accurate and efficient method for generating large artificial datasets simulating fish interacting with a wide variety of objects. Using these sets, we trained an artificial neural network (ANN), representing brain areas downstream of the electroreceptors, to extract the 3D location, size, and electrical properties of objects. Model performance is comparable to that of real fish in experimentally tested behavioral tasks. Performance is maximized if the ANN operates in two stages: first estimating object distance and size and then using this information to extract electrical properties. These results highlight the potential of end-to-end modeling for studies of electrosensation and suggest a specific form of modularity in electrosensory processing that can be tested experimentally.