The structure and significance of correlated activity among retinal ganglion cells

视网膜神经节细胞之间相关活动的结构和意义

• 批准号: 9611172
• 负责人: Kiersten Ruda
• 金额: $ 3.61万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9611172
• 关键词: Area; Biological Models; Brain; Cells; Code; Complex; Computational Technique; Computer Simulation; Conflict (Psychology); Data; Disease; Equilibrium; Exhibits; Human; Individual; Information Theory; Knowledge; Light; Light Adaptations; Macular degeneration; Measures; Modeling; Neurons; Noise; Output; Paint; Pathway interactions; Perception; Performance; Population; Process; Research; Retina; Retinal; Retinal Ganglion Cells; Retinitis Pigmentosa; Rodent; Role; Sampling; Sensory; Shapes; Signal Transduction; Source; Stimulus; Structure; Testing; Time; Vision; Visual; Visual system structure; Work; base; cell type; improved; individual response; light intensity; multi-electrode arrays; neural circuit; neural model; novel; receptive field; relating to nervous system; response; synergism; visual coding; visual information; visual processing; visual stimulus

ABSTRACT Correlated activity is an integral part of how populations of neurons process information. Signal and noise correlations are important to consider because they have the potential to improve or degrade encoding and decoding, depending on their specific structure. Here I propose to utilize the retina as a model system to elucidate the impact of correlated activity on visual processing. In particular, I will test the role of correlations across different computations and adaptation states in the retina. Retinal ganglion cells (RGCs), the sole source of visual information for the brain, exhibit signal and noise correlations in their responses. RGCs are organized into ~30 different cell types, each of which performs a distinct visual function. Here I consider the structure and significance of correlations across diverse RGC types. Adaptation state is another crucial factor because the retina must convey visual signals over a broad range of light intensities, and different light levels alter the strength of correlations between RGCs. My overarching hypothesis is that correlated activity among RGCs encodes novel visual signals and can substantially improve decoding by circuits downstream of the retina. I will test this hypothesis across cell types and light levels in three aims. My first aim will measure and model the structure of correlations among RGCs to quantify response components that underlie correlated activity. In my second aim, I will determine how correlated activity impacts retinal encoding of visual scenes. Specifically, I will find the stimulus features and the amount of information that are encoded by correlated activity across cell types and light levels. The third aim will determine the how correlations may impact downstream processing of retinal output. In this aim, I will implement a decoder that estimates visual stimuli from RGC responses. I will determine if correlated activity improves the decoder’s performance such that a single decoder can successfully readout RGC activity across light levels. Overall, this work utilizes and combines the powerful capabilities of large-scale multi-electrode arrays with advanced computational techniques. These approaches will enable me to determine the role of correlated activity for encoding and decoding visual stimuli in the retina. This work will advance our knowledge of early visual processing, as well as how correlations impact neural computations and circuit function in general.

摘要