The neuronal basis of direction selectivity in lobula plate tangential cells

A neuronally based model of contrast gain adaptation in fly motion vision

Motion-sensitive neurons in the visual systems of many species, including humans, exhibit a depression of motion responses immediately after being exposed to rapidly moving images. This motion adaptation has been extensively studied in flies, but a neuronal mechanism that explains the most prominent component of adaptation, which occurs regardless of the direction of motion of the visual stimulus, has yet to be proposed. We identify a neuronal mechanism, namely frequency-dependent synaptic depression, which explains a number of the features of adaptation in mammalian motion-sensitive neurons and use it to model fly motion adaptation. While synaptic depression has been studied mainly in spiking cells, we use the same principles to develop a simple model for depression in a graded synapse. By incorporating this synaptic model into a neuronally based model for elementary motion detection, along with the implementation of a center-surround spatial band-pass filtering stage that mimics the interactions among a subset of visual neurons, we show that we can predict with remarkable success most of the qualitative features of adaptation observed in electrophysiological experiments. Our results support the idea that diverse species share common computational principles for processing visual motion and suggest that such principles could be neuronally implemented in very similar ways.

Fast-scale adaptive changes of directional tuning in fly tangential cells are explained by a static nonlinearity

The response of vertebrate motion-sensitive neurons to a directional stimulus is affected by the direction of the stimulus that immediately preceded it. These nonlinear effects are also observed for orientation tuning and are typically interpreted as fast-scale adaptive changes. We verified that similar effects are observed for spiking tangential cells in the fly lobula plate. We also investigated the spatial selectivity of these effects by presenting multiple patches at different positions within the receptive field,and found that the effects are strictly local.

We modelled the data using elementary operators (linear filters and threshold nonlinearities). A satisfactory account of the results is obtained when an early static nonlinearity acts on the outputs of multiple front-end filters that are subsequently pooled in a spatially restricted manner by the tangential cell. In line with recent studies, these findings emphasize the importance of testing simple nonlinear models before attempting more elaborate interpretations of fast-scale adaptive phenomena in single neurons. We discuss a potential neural implementation of the model based on medullar projections to the lobula plate.