Summary The distributions of neurons in sensory circuits display ordered spatial patterns arranged to enhance or encode specific regions or features of the external environment. Indeed, visual space is not sampled uniformly across the vertebrate retina. Retinal ganglion cell (RGC) density increases and dendritic arbor size decreases towards retinal locations with higher sampling frequency, such as the fovea in primates and area centralis in carnivores [1]. In these locations, higher acuity at the level of individual cells is obtained because the receptive field center of a RGC corresponds approximately to the spatial extent of its dendritic arbor [2, 3]. For most species, structurally and functionally distinct RGC types appear to have similar topographies; collectively scaling their cell densities and arbor sizes towards the same retina location [4]. Thus, visual space is represented across the retina in parallel by multiple distinct circuits [5]. In contrast, we find a population of mouse RGCs, known as alpha or alpha-like [6], that displays a nasal-to-temporal gradient in cell density, size and receptive fields, which facilitates enhanced visual sampling in frontal visual fields. The distribution of alpha-like RGCs contrasts with other known mouse RGC types, and suggests that unlike most mammals, RGC topographies in mice are arranged to sample space differentially.

Summary Neural responses are noisy, and circuit structure can correlate this noise across neurons. Theoretical studies show that noise correlations can have diverse effects on population coding, but these studies rarely explore stimulus dependence of noise correlations. Here, we show that noise correlations in responses of ON-OFF direction-selective retinal ganglion cells are strongly stimulus dependent and we uncover the circuit mechanisms producing this stimulus dependence. A population model based on these mechanistic studies shows that stimulus-dependent noise correlations improve the encoding of motion direction two-fold compared to independent noise. This work demonstrates a mechanism by which a neural circuit effectively shapes its signal and noise in concert, minimizing corruption of signal by noise. Finally, we generalize our findings beyond direction coding in the retina and show that stimulus-dependent correlations will generally enhance information coding in populations of diversely tuned neurons.

Summary A central goal in the study of any sensory system is to predict neural responses to complex inputs, especially those encountered during natural stimulation. Nowhere is the transformation from stimulus to response better understood than the vertebrate retina. Nevertheless, descriptions of retinal computation are largely based on stimulation using artificial visual stimuli, and it is unclear how these descriptions map onto the encoding of natural stimuli. We demonstrate that nonlinear spatial integration, a common feature of retinal ganglion cell (RGC) processing, shapes neural responses to natural visual stimuli in primate Off parasol RGCs, whereas On parasol RGCs exhibit surprisingly linear spatial integration. Despite this asymmetry, both cell types show strong nonlinear integration when presented with artificial stimuli. We show that nonlinear integration of natural stimuli is a consequence of rectified excitatory synaptic input, and that accounting for nonlinear spatial integration substantially improves models that predict RGC responses to natural images.

Throughout the CNS, gap junction–mediated electrical signals synchronize neural activity on millisecond timescales via cooperative interactions with chemical synapses. However, gap junction–mediated synchrony has rarely been studied in the context of varying spatiotemporal patterns of electrical and chemical synaptic activity. Thus, the mechanism underlying fine-scale synchrony and its relationship to neural coding remain unclear. We examined spike synchrony in pairs of genetically identified, electrically coupled ganglion cells in mouse retina. We found that coincident electrical and chemical synaptic inputs, but not electrical inputs alone, elicited synchronized dendritic spikes in subregions of coupled dendritic trees. The resulting nonlinear integration produced fine-scale synchrony in the cells’ spike output, specifically for light stimuli driving input to the regions of dendritic overlap. In addition, the strength of synchrony varied inversely with spike rate. Together, these features may allow synchronized activity to encode information about the spatial distribution of light that is ambiguous on the basis of spike rate alone.

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