ENCODING OF VISUAL INFORMATION IN LGN FIRING PATTERNS

LGN 射击模式中的视觉信息编码

• 批准号: 6012686
• 负责人: PAMELA REINAGEL
• 金额: $ 4万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-07-01 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/6012686
• 关键词: cats; lateral geniculate body; neural information processing; neural transmission; neurons; statistics /biometry; visual perception; visual stimulus

The goal of these experiments is to study the reproducibility of firing patterns in LGN neurons in response to different types of visual stimulation. In my previous theoretical work, based on data collected by experimental collaborators, I showed that a randomly varying visual stimulus could be approximately reconstructed by simply linearly filtering an LGN spike train. Moreover, I showed that the distinctive bursts fired by LGN cells encoded visual information efficiently. In my own experiments during the past two years, I have been studying the reproducibility of individual LGN responses to dynamic visual stimuli. In my final year, I plan to extend this work to the problem of how a population of thalamic neurons collectively encodes natural stimuli. (1) I will study how the statistics of the visual stimulus ensemble affects the reproducibility of individual LGN responses. Response reproducibility will be measured for both the temporal and spatio-temporal natural stimuli. In control experiments, I will manipulate natural stimuli to vary the intensity distribution, the frequency spectrum, or the contrast of the stimuli. (2) I will study whether the noise in an individual LGN response is shared with, or independent from, the noise in other cells responses. To accomplish this I will compare the trial by trial response variations of one cell with those of another simultaneously recorded cell. These studies will shed light on how the responses of individual cells work together to encode natural visual scenes in the LGN.

这些实验的目的是研究LGN神经元响应于不同类型的视觉刺激的放电模式的再现性。 在我以前的理论工作中，基于实验合作者收集的数据，我证明了随机变化的视觉刺激可以通过简单地线性过滤LGN尖峰序列来近似重建。此外，我还证明了LGN细胞发出的独特的爆发有效地编码了视觉信息。 在过去两年的实验中，我一直在研究个体LGN对动态视觉刺激反应的再现性。 在我的最后一年，我计划将这项工作扩展到丘脑神经元群体如何集体编码自然刺激的问题。 (1)我将研究视觉刺激集合的统计学如何影响个体LGN反应的再现性。 将测量时间和时空自然刺激的响应再现性。 在控制实验中，我将操纵自然刺激来改变强度分布、频谱或刺激的对比度。 (2)我将研究单个LGN反应中的噪声是否与其他细胞反应中的噪声共享或独立。 为了做到这一点，我将比较一个细胞与另一个同时记录的细胞的试验响应变化。 这些研究将揭示单个细胞的反应如何协同工作，以编码LGN中的自然视觉场景。