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要旨

We present a theory for the construction of an optimal matched filter for self-motion induced optic flow fields. The matched filter extracts local flow
components along a set of pre-defined directions and weights them according to
an optimization principle which minimizes the difference between estimated and
real egomotion parameters. In contrast to previous approaches, prior knowledge
about distance and translation statistics is incorporated in the form of a
"world model". Simulations indicate that the matched filter model yields
reliable self-motion estimates. A comparison of the weight distribution used
in the model with the local motion sensitivities of individual and small
groups of interneurons in the fly visual system shows a close
correspondence. This suggests that these so-called tangential neurons are
tuned to optic flow fields induced by rotation or translation along a
particular axis. They seem to weight the local optic flow according to the
contribution of input noise and the expected variability of the translatory
flow component. Their local preferred directions and motion sensitivities can
be interpreted as an adaptation to the processing requirements of estimating
self-motion from the optic flow.