Browsing by Author "Field, Gregory D"

One of the central problems in neuroscience is that there is a lack of understanding of the diversity and functions of cell types in the brain. Even in brain areas that have been studied extensively, such as the retina, much remains to be learned about the diversity and functions of cell types. Morphological, functional and genetic studies have yet to converge on a consistent picture of cell type diversity in the retina, because the field lacks a standardized approach to classify cell types. A systematic classification approach is essential to provide an unambiguous appreciation of cell type diversity, and a better understanding of the organization and function of the retina. In the first portion of this dissertation, we present a novel approach that classifies retinal ganglion cells (RGCs) in a quantitative, verifiable and reproducible manner. We utilize diverse visual stimuli and a multi-electrode array, to record simultaneously from multiple RGCs, and show that there are at least 13 RGC types with distinct functional properties. In the second portion of the dissertation, we present a quantitative determination and comparison of the spatiotemporal receptive field (RF) structures and neural coding properties across these RGC types. Determining the RF structure of RGC types is important, because it constrains the computations performed by retinal circuits and identifies the signals available to retinal recipient areas. We find that RGC types exhibit functional asymmetries in terms of their RF size, temporal integration, and response nonlinearities. We also show that no RGC types exhibited RFs that were strictly independent in space and time. These results provide several new insights into the computations performed in the rodent retina, and highlight the importance of understanding cell type diversity to further our understanding of how the retina works and the role it plays in visual processing.

Direction selective ganglion cells (DSGCs) signal the presence and direction of motion from the retina to multiple brain areas. Reliably signaling motion is critical through dynamic environments, including contrasts and light levels. This dissertation examines how direction-selective responses are reliably generated across stimulus contrasts, and how populations of DSGCs adapt to changes in light level, spanning moonlight to daylight. In Chapter 2, I describe the development of a functional classification method to identify and classify DSGCs. In Chapter 3, I show how NMDA-dependent synapses improve direction coding in DSGCs at threshold contrasts. In Chapter 4, I describe changes in DSGC responses across light levels, and how these adaptive changes depend on cell types. Chapter 5 focuses on two mechanisms that contribute to this cell type-dependent adaptation: connexin36-mediated electrical coupling and differences in effective GABAergic inhibition. In Chapter 6, I show with a simulation of DSGC activity based on data that this adaptation strategy is beneficial for balancing motion detection and direction estimation at the lower signal-to-noise encountered at night.

The retina signals visual information to the brain with the parallel channels of different retinal ganglion cell (RGC) types, whose signals ultimately lead to visual perception. Between cloudy nights and sunny days, the retina must combat the trillion-fold change in mean light intensity to successfully convey visual information. Critically, the nature of both signal and noise in RGC populations is altered across this broad range of light levels, creating a rich problem of how visual messages are encoded by the retina and transmitted to the brain. This thesis addresses these topics using large-scale multielectrode array recordings of RGC populations in different light conditions. In Chapter 2, I characterize how retinal signaling is altered over a wide range of light intensities. Chapter 3 investigates how adaptation impacts visual encoding of different RGC types. My results suggest that although retinal computations change substantially over light conditions, there are some elements of visual encoding that are invariant to light adaptation. Finally, Chapter 4 examines adaptation-induced changes in the structure of correlated activity and the subsequent impact on processing retinal output. The findings of this chapter clarify the nature of RGC responses crucial for downstream readout across light levels. Overall, this work identifies aspects of RGC activity that are important for encoding visual information and decoding retinal output from starlight to sunlight.

Efficient coding theory provides a powerful framework for understanding the organization of the early visual system. Prior research has demonstrated that efficient coding theory can help account for a range of retinal ganglion cell (RGC) organizational features, including the center-surround spatial receptive fields and ON and OFF parallel pathways. Here, we use a machine learning-based computational framework for efficient coding and show that more functional architecture of visual processing can be explained on the basis of this principle. First, how should receptive fields (RFs) be arranged to best encode natural images? When the spatial RFs and contrast response functions are optimized in order to maximally encode natural stimuli given noise and firing rate constraints, the RFs form a pair of mosaics, one with ON RFs and one with OFF RFs, similar to those of mammalian retina, as an existing finding from previous research. Interestingly, the relative arrangement of the two mosaics transitions between alignment under high signal-to-noise conditions and anti-alignment under low signal-to-noise conditions. The next question we tackled is: how are the ON and OFF RF mosaics arranged in the mammalian retina? We examined the retina of rats and primates and confirmed that the ON and OFF mosaic pairs encoding the same visual feature are anti-aligned, indicating that the retina is optimized to handle dim or low-contrast stimuli. Finally, we dove into the question: how many cell types can be predicted by efficient coding theory? We examined encoding of natural videos, and found that, as the available channel capacity – the number of simulated RGCs available for encoding – increases, new cell types emerge that focus on higher temporal frequencies and larger spatial areas. Together, these studies advance our understanding of the relationships between efficient coding, retinal organization, and diversity of retinal cell types.