Limits to visual processing: non-linearities, noise and integration

在任何感觉系统中，处理过程都有三个基本阶段；初始滤波，非线性转导和积分。神经信号受到内部噪声的干扰，限制了可探测性。以前的方法通常假设视觉系统是线性的，神经噪声是加性的。然而，现在人们认识到人类视觉系统的非线性也会影响这些测量。此外，如果不考虑这些影响，这种非线性的差异可能与内部噪声水平的差异混淆。这些影响需要一种新的综合方法来区分非线性和内部噪声。在这个建议中，我开发了新的方法，将长期适用于理解皮层视觉处理的功能限制。这将在短期内应用于对比处理，并在中期应用于形式和运动的处理。******我将主要使用心理物理方法进行调查。这些都是精心控制的行为实验，当与计算模型相结合时，使我们能够研究视觉系统如何处理其输入以驱动人类行为。我对非线性存在的调查将基于完善的基座掩蔽范式。这种方法测量任何应用于大脑输入信号的非线性变换的导数。我还提出了一个新的范例，它将允许人们测量神经噪声在与不同刺激特征相关的不同大脑区域的整合范围。******短期目标是全面了解正常健康观察者对比度检测的局限性，以及如何跨空间整合来自早期视觉区域的对比度输入，以便对对比度进行全局处理。对于每一个维度的空间频率，时间频率，色度，场偏心率和平均亮度。我将确定非线性转导，内部噪声和时空整合的相对限制。这将作为后续工作的基础，研究这些基本限制如何随着健康观察者的年龄和患者群体的疾病而变化。***我们研究项目的长期目标是研究三个基本因素——非线性、噪声源和时空整合——如何有助于从运动、轮廓、形状、运动形状、光流和立体的对比中检测刺激特征，既适用于通常在纹状皮层进行的局部计算，也适用于纹状皮层外典型的更全面的计算。******这里采用的方法既基本又新颖。它涉及一个原则性的基于模型的框架，在这个框架中可以回答视觉中的一些基本问题

In any sensory system there are three fundamental stages in processing; the initial filtering, non-linear transduction and integration. Neural signals are perturbed by internal noise that limits detectability. Previous approaches have often assumed the visual system is linear and the neural noise is additive. However, it is now recognized that the human visual system has nonlinearities that also affect these measurements. Furthermore, differences in this nonlinearity can be confused with differences in internal noise level if these effects are not accounted for. These effects require a new comprehensive approach that will distinguish nonlinearities from internal noise. In this proposal I develop novel methods that will have long term applicability for understanding the functional limits of visual processing in the cortex. This will be applied in the short term to contrast processing, and to the processing of form and motion in the intermediate term. ******I will principally use psychophysical methods for my investigations. These are carefully-controlled behavioural experiments which, when combined with computational modelling, allow us to investigate how the visual system processes its input in order to drive human behaviour. My investigations into the non-linearities present will be based on the well-established pedestal masking paradigm. This method measures the derivative of any non-linear transformation that is applied to the input signal in the brain. I also propose a novel paradigm that will allow one to measure the range over which neural noise is integrated at the various brain areas relating to different stimulus features.******The short-term objectives are to provide a complete understanding of the limits to contrast detection in normal healthy observers and how contrast inputs from early visual areas are integrated across space to allow for the global processing of contrast. For each of the dimensions of spatial frequency, temporal frequency, chromaticity, field eccentricity and mean luminance. I will determine the relative limitations from non-linear transduction, internal noise, and spatiotemporal integration. This will serve as a foundation for subsequent work on how each of these fundamental limitations changes with age in healthy observers and with disease in patient populations.***The longer term objective of our program of research is to investigate, how three fundamental factors – nonlinearities, noise sources, and spatiotemporal integration – contribute to the detection of stimulus features from contrast to motion, contours, shape, shape-from-motion, optic flow and stereo, both for local computations typically carried out in striate cortex as well as more global computations typical of extra-striate cortex. ******The approach taken here is both fundamental and novel. It involves a principled model-based framework in which to answer some of the basic questions in vision