Contrast Adaptation in the LGN

描述（由申请人提供）：视觉系统必须在广泛的照明条件下工作。如果没有适应，视觉神经元的动态范围（1-2个数量级）在自然刺激的动态范围（10-11个数量级）内无法支持功能性视觉。因此，视觉神经元不仅适应平均光照水平，而且适应近期视觉刺激的平均对比度。对比适应的生理基础在哺乳动物视觉系统中得到了广泛的研究，但其功能后果尚不清楚。功能结果的直接测试将需要信息理论方法。本研究将检验两种截然不同但并不相互排斥的假设：(1)在不同的对比度条件下，LGN的对比度适应有助于保持敏感性，从而保持编码效率。(2) LGN中的对比度自适应用于计算视觉信息的对比度不变表示。为了验证这些假设，我们将在体内使用LGN的细胞外电极记录对闪烁视觉刺激的神经反应。由这些反应编码的视觉信息将使用几种计算方法进行评估，包括信息论。首先，我们将确定LGN中增益的变化是否符合理论预测，并测试增益变化是否与跨对比度的编码效率维持相关。其次，我们将确定当相同的刺激模式以不同的对比度呈现时，LGN的反应在哪些方面是相同的，并测量LGN中有多少视觉信息是对比度不变的。最后，我们将构建一个模型来检验我们对LGN中对比适应的理解。通过比较有和没有自适应的模型输出，我们将在对比不变性和高效编码方面测试功能结果。这项研究将为我们描述感觉信息是如何通过丘脑传递到皮层的以及为什么传递的提供重要的组成部分。这对于对比适应障碍的诊断或治疗以及用于恢复视力丧失的神经义肢的开发具有重要价值。

DESCRIPTION (provided by applicant): The visual system must function under a broad range of illumination conditions. Without adaptation, the dynamic range of visual neurons (1-2 orders of magnitude) could not support functional vision over the dynamic range of natural stimuli (10-11 orders of magnitude). Therefore visual neurons adapt, not only to the mean light level, but also to the average contrast of recent visual stimuli. The physiological basis of contrast adaptation has been studied extensively in the mammalian visual system, but its functional consequences are not known. A direct test of functional consequences will require an information theoretic approach. The proposed study would test two distinct but not mutually exclusive hypotheses about the function of contrast adaptation in the LGN: (1) Contrast adaptation in the LGN serves to maintain sensitivity and therefore coding efficiency under different contrast conditions. (2) Contrast adaptation in the LGN serves to compute a contrast-invariant representation of visual information. To test these hypotheses, we will record neural responses to flickering visual stimuli using extracellular electrodes in the LGN in vivo. The visual information encoded by these responses will be assessed using several computational approaches, including information theory. First we will determine whether the changes in gain in the LGN fit theoretical predictions and test whether gain changes are correlated with the maintenance of coding efficiency across contrasts. Second, we will determine in what respect(s) the LGN response is the same when the same stimulus patterns are presented at different contrasts, and measure how much of the visual information in the LGN is contrast invariant. Finally we will construct a model to test our understanding of contrast adaptation in the LGN. By comparing the output of the model with and without adaptation, we will test the functional consequences in terms of both contrast invariance and efficient coding. This study will contribute an important component to our description of how sensory information is relayed to cortex through the thalamus and why. This could be valuable for the diagnosis or treatment of disorders of contrast adaptation, and for the development of neural prosthetics to restore vision loss.