Neuronal population codes and the perception of object distance in weakly electric fish.
Weakly electric fish use an electric sense to navigate and capture prey in the dark. Objects in the surroundings of the fish produce distortions in their self-generated electric field; these distortions form a two-dimensional Gaussian-like electric image on the skin surface. To determine the distance of an object, the peak amplitude and width of its electric image must be estimated. These sensory features are encoded by a neuronal population in the early stages of the electrosensory pathway, but are not represented with classic bell-shaped neuronal tuning curves. In contrast, bell-shaped tuning curves do characterize the neuronal responses to the location of the electric image on the body surface, such that parallel two-dimensional maps of this feature are formed. In the case of such two-dimensional maps, theoretical results suggest that the width of neural tuning should have no effect on the accuracy of a population code. Here we show that although the spatial scale of the electrosensory maps does not affect the accuracy of encoding the body surface location of the electric image, maps with narrower tuning are better for estimating image width and those with wider tuning are better for estimating image amplitude. We quantitatively evaluate a two-step algorithm for distance perception involving the sequential estimation of peak amplitude and width of the electric image. This algorithm is best implemented by two neural maps with different tuning widths. These results suggest that multiple maps of sensory features may be specialized with different tuning widths, for encoding additional sensory features that are not explicitly mapped.
Lewis
J E
JE
Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Ontario, Canada K1H 8M5. jlewis@uottawa.ca
Maler
L
L
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004215:Distance Perception / Q000502:physiology*; D004555:Electric Fish / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D016011:Normal Distribution; D015203:Reproducibility of Results; D012679:Sense Organs / Q000502:physiology; D011984:Sensory Receptor Cells / Q000502:physiology; D012867:Skin / Q000294:innervation