A nonlinear interaction between signals from at least two spatially displaced receptors is a fundamental requirement for a direction-selective motion detector. This paper characterizes the nonlinear mechanism present in the motion detector pathway that provides the input to wide-field directional neurons in the nucleus of the optic tract of the wallaby, Macropus eugenii. An apparent motion stimulus is used to reveal the interactions that occur between adjacent regions of the receptive fields of the neurons. The interaction between neighboring areas of the field is a nonlinear facilitation that is accurately predicted by the outputs of an array of correlation-based motion detectors (Reichardt detectors). Based on the similarity between the output properties of the detector array and the real neurons, it is proposed that the interaction between neighboring regions of the receptive field is a second-order nonlinearity such as a multiplication. The results presented here for wallaby neurons are compared to data collected from directional systems in other species.

This study is concerned with how information about the direction of visual motion is encoded by motion-sensitive neurons. Motion-sensitive neurons are usually studied using stimuli unchanging in speed and direction over several seconds. Recently, it has been suggested that neuronal responses to more naturalistic stimuli cannot be understood on the basis of experiments with constant-motion stimuli (de Ruyter van Steveninck et al., 1997). We measured the variability and information content of spike trains recorded from directional neurons in the nucleus of the optic tract (NOT) of the wallaby, Macropus eugenii, in response to constant and time-varying motion. While the NOT forms part of the mammalian optokinetic system, we have shown previously that the responses of its directional neurons resemble those of insect H1 in many respects (Ibbotson et al., 1994). We find that directional neurons in the wallaby NOT respond with lower variability and higher rates of information transmission to time-varying stimuli than to constant motion. The difference in response variability is predicted by an inhomogeneous Poisson model of neuronal spiking incorporating an absolute refractory period of 2 ms during which no subsequent spike can be fired. Refractoriness imposes structure on the spike train, reducing variability (de Ruyter van Steveninck & Bialek, 1988; Berry & Meister, 1998). A given refractory period has a greater impact when firing rates are high, as for the responses of NOT neurons to time-varying stimuli. It is in just these cases that variability in experimentally observed spike trains is lowest. Thus, differences in response variability do not necessarily imply that different models are required to predict neuronal responses to constant- and time-varying motion stimuli.