Two major functions performed by the retina are to establish the parallel processing of visual information and to adapt visual encoding to the trillion-fold range of light intensities encountered in the environment. Previous work has highlighted many specialized cell types and circuits that instantiate parallel processing and light adaptation. However, fully understanding either process requires identifying how light adaptation and parallel processing interact. One possibility is that light adaptation causes uniform or proportional scaling to the receptive fields (RFs) of different retinal ganglion cell (RGC) types, the output neurons of the retina. Alternatively, light adaptation could cause a reorganization of RF structures across RGC types. A third possibility is that RFs across different RGC types are more similar under some conditions (e.g., low light levels) and more divergent under other conditions. To resolve these possibilities, we examined how the spatiotemporal RF structure of six simultaneously measured RGC types in the rat retina change from rod- to cone-mediated light levels. While light adaptation altered the RF properties of all six RGC types, we found that the relative structure across different RGC types was largely preserved across light levels. However, in both the spatial and temporal domains, one of the six RGC types exhibited adaptation distinct from the other types, resulting in a partial reorganization of RF properties across RGC types. These measurements identify how parallel visual processing interacts with light adaptation and highlights the challenges to stably encode visual scenes across light levels.

Light-field fluorescence microscopy can record large-scale population activity of neurons expressing genetically-encoded fluorescent indicators within volumes of tissue. Conventional light-field microscopy (LFM) suffers from poor lateral resolution when using wide-field illumination. Here, we demonstrate a structured-illumination light-field microscopy (SI-LFM) modality that enhances spatial resolution over the imaging volume. This modality increases resolution by illuminating sample volume with grating patterns that are invariant over the axial direction. The size of the SI-LFM point-spread-function (PSF) was approximately half the size of the conventional LFM PSF when imaging fluorescent beads. SI-LFM also resolved fine spatial features in lens tissue samples and fixed mouse retina samples. Finally, SI-LFM reported neural activity with approximately three times the signal-to-noise ratio of conventional LFM when imaging live zebrafish expressing a genetically encoded calcium sensor.

The retina and primary visual cortex (V1) both exhibit diverse neural populations sensitive to diverse visual features. Yet it remains unclear how neural populations in each area partition stimulus space to span these features. One possibility is that neural populations are organized into discrete groups of neurons, with each group signaling a particular constellation of features. Alternatively, neurons could be continuously distributed across feature-encoding space. To distinguish these possibilities, we presented a battery of visual stimuli to mouse retina and V1 while measuring neural responses with multi-electrode arrays. Using machine learning approaches, we developed a manifold embedding technique that captures how neural populations partition feature space and how visual responses correlate with physiological and anatomical properties of individual neurons. We show that retinal populations discretely encode features, while V1 populations provide a more continuous representation. Applying the same analysis approach to convolutional neural networks that model visual processing, we demonstrate that they partition features much more similarly to the retina, indicating they are more like big retinas than little brains.

Visual processing in the retina depends on the collective activity of large ensembles of neurons organized in different layers. Current techniques for measuring activity of layer-specific neural ensembles rely on expensive pulsed infrared lasers to drive 2-photon activation of calcium-dependent fluorescent reporters. Here, we present a 1-photon light-sheet imaging system that can measure the activity in hundreds of ex vivo retinal neurons over a large field of view while simultaneously presenting visual stimuli. This allowed for a reliable functional classification of different retinal ganglion cell types. We also demonstrate that the system has sufficient resolution to image calcium entry at individual synaptic release sites across the axon terminals of dozens of simultaneously imaged bipolar cells. The simple design, a large field of view, and fast image acquisition, make this a powerful system for high-throughput and high-resolution measurements of retinal processing at a fraction of the cost of alternative approaches.

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