Liu JK, Gollisch T
A central goal in sensory neuroscience is to understand the neuronal signal processing involved in the encoding of natural stimuli. A critical step towards this goal is the development of successful computational models of this encoding. For ganglion cells in the vertebrate retina, the development of satisfactory models for responses to natural visual scenes is an ongoing challenge. Standard models typically apply linear integration of visual stimuli over space, yet many ganglion cells are known to show nonlinear spatial integration in natural stimulus contexts. We here study the encoding of natural images by retinal ganglion cells, using multielectrode-array recordings from isolated salamander retinas. We assess how responses to natural and blurred images depend on first- and second-order statistics of spatial patterns inside the receptive field. This leads us to a simple extension of current standard ganglion cell models, which are based on linear spatial integration. We show that taking not only the weighted average of light intensity inside the receptive field into account but also its variance over space yields substantially improved response predictions of responses to novel images. Finally, we demonstrate how this model framework can be used to assess the spatial scale of nonlinear spatial integration. Our results underscore the importance of nonlinear spatial stimulus integration in the retina in responses to natural images. Furthermore, the introduced model framework provides a simple, yet powerful extension of standard models and may serve as a benchmark for the development of more detailed models of the nonlinear structure of receptive fields.