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 神经元响应不同类型视觉刺激的放电模式的再现性。 在我之前的理论工作中，基于实验合作者收集的数据，我表明可以通过简单地线性过滤 LGN 尖峰序列来近似重建随机变化的视觉刺激。此外，我还证明 LGN 细胞发出的独特爆发可以有效地编码视觉信息。 在我自己过去两年的实验中，我一直在研究个体 LGN 对动态视觉刺激的反应的再现性。 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共同编码自然刺激的问题。 (1) 我将研究视觉刺激集合的统计数据如何影响个体 LGN 反应的再现性。 将测量时间和时空自然刺激的反应再现性。 在控制实验中，我将操纵自然刺激来改变刺激的强度分布、频谱或对比度。 (2) 我将研究单个 LGN 响应中的噪声是否与其他细胞响应中的噪声共享或独立。 为了实现这一目标，我将通过试验将一个细胞的反应变化与另一个同时记录的细胞的反应变化进行比较。 这些研究将揭示单个细胞的反应如何协同工作来编码 LGN 中的自然视觉场景。