Circuit mechanisms underlying the encoding of ethologically relevant visual stimuli in the retina
Turner, Maxwell Holte
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In the human retina, the axons of roughly 10^6 retinal ganglion cells (RGCs) carry all of the information underlying visual perception and visually guided behavior. RGC computation is the last processing step before the imposition of this sensory bottleneck. Because of the importance of RGC computation for visual function and because of the accessibility of the retina to physiological investigation, RGCs are among the best studied class of early sensory neurons. Many decades of investigation using artificial visual stimuli (e.g. spots, gratings, white noise etc.) has revealed a great deal about the physiology of RGCs, the computations they can perform, and the circuitry underlying these computations. What is lacking, however, is an understanding of RGC computation and encoding during natural visual stimulation. A complete understanding of RGC function requires extending what we have learned using artificial, mostly static visual stimuli to the dynamic and spatially structured conditions that characterize natural vision. A major focus of the thesis presented here is to begin to bridge this gap. In Chapter 2 I will review some of what is known about how neural circuits in the early visual system encode naturalistic visual inputs. In Chapters 3 & 4 I will present work that connects a ubiquitous concept in early visual processing - the receptive field - to the encoding of natural visual stimuli in the retina. Classical models of the retinal ganglion cell (RGC) receptive field assume linear integration across visual space, and this assumption guides most modern day models that aim to predict RGC responses to visual stimulation. I show that spatial nonlinearities can be important for encoding spatial contrast within a natural scene. Furthermore, we can use what we know about nonlinear receptive field structure to improve models of RGCs. Finally, I show that spatial contrast encoding by RGCs can be modulated by visual context. Regardless of the stimulus being encoded, a fundamental limit to the fidelity with which sensory information can be encoded is the variability of neural responses. Repeated presentations of the same stimulus will elicit variable responses from a single neuron. Noise is a feature of neural population responses as well, and in practice it is often found that this noise is correlated within a population. In Chapter 5, I will present a strategy used by retinal circuits to minimize the effect of noise on the encoding of a behaviorally-relevant feature of the visual world - namely an object's direction of motion.
- Neuroscience