Synaptic inputs to displaced intrinsically-photosensitive ganglion cells in macaque retina

Retinal Connectomics: A Review

The retina is an ideal model for understanding the fundamental rules for how neural networks are constructed. The compact neural networks of the retina perform all of the initial processing of visual information before transmission to higher visual centers in the brain. The field of retinal connectomics uses high-resolution electron microscopy datasets to map the intricate organization of these networks and further our understanding of how these computations are performed by revealing the fundamental topologies and allowable networks behind retinal computations. In this article, we review some of the notable advances that retinal connectomics has provided in our understanding of the specific cells and the organization of their connectivities within the retina, as well as how these are shaped in development and break down in disease. Using these anatomical maps to inform modeling has been, and will continue to be, instrumental in understanding how the retina processes visual signals.

Synaptic Origins of the Complex Receptive Field Structure in Primate Smooth Monostratified Retinal Ganglion Cells

Considerable progress has been made in studying the receptive fields of the most common primate retinal ganglion cell (RGC) types, such as parasol RGCs. Much less is known about the rarer primate RGC types and the circuitry that gives rise to noncanonical receptive field structures. The goal of this study was to analyze synaptic inputs to smooth monostratified RGCs to determine the origins of their complex spatial receptive fields, which contain isolated regions of high sensitivity called "hotspots." Interestingly, smooth monostratified RGCs co-stratify with the well-studied parasol RGCs and are thus constrained to receiving input from bipolar and amacrine cells with processes sharing the same layer, raising the question of how their functional differences originate. Through 3D reconstructions of circuitry and synapses onto ON smooth monostratified and ON parasol RGCs from central macaque retina, we identified four distinct sampling strategies employed by smooth and parasol RGCs to extract diverse response properties from co-stratifying bipolar and amacrine cells. The two RGC types differed in the proportion of amacrine cell input, relative contributions of co-stratifying bipolar cell types, amount of synaptic input per bipolar cell, and spatial distribution of bipolar cell synapses. Our results indicate that the smooth RGC's complex receptive field structure arises through spatial asymmetries in excitatory bipolar cell input which formed several discrete clusters comparable with physiologically measured hotspots. Taken together, our results demonstrate how the striking differences between ON parasol and ON smooth monostratified RGCs arise from distinct strategies for sampling a common set of synaptic inputs.

New insights into retinal circuits through EM connectomics: what we have learnt and what remains to be learned

The retinal neural circuit is intricately wired for efficient processing of visual signals. This is well-supported by the specialized connections between retinal neurons at both the functional and ultrastructural levels. Through 3D electron microscopic (EM) reconstructions of retinal neurons and circuits we have learnt much about the specificities of connections within the retinal layers including new insights into how retinal neurons establish connections and perform sophisticated visual computations. This mini-review will summarize the retinal circuitry and provide details about the novel insights EM connectomics has brought into our understanding of the retinal circuitry. We will also discuss unresolved questions about the retinal circuitry that can be addressed by EM connectomics in the future.