Neural Computations In Visual Cortex

描述（由申请人提供）：我们的目标是提高我们对初级视觉皮层进行操作的性质的理解，以及这些操作是如何进行的。研究计划围绕两个基本问题展开。A.由V1神经元执行的空间计算是否被定向接受野、简单非线性和增益控制充分描述，或者相反，更复杂的空间操作是否发挥重要作用？B.局部人群的放电率可以被认为是视觉信息的主要载体吗，或者相反，神经活动的精细结构（跨越时间或跨越局部人群）也很重要吗？越来越多的实验证据表明v1神经元执行的操作确实很复杂，这促使了问题A的产生。这里描述的实验策略使用新的刺激，在空间和空间频率上精确平衡-与传统刺激相比，传统刺激在空间上高度局部化，但在空间频率上广泛（例如，点和条），或在空间上扩展，但在空间频率上高度局部化（例如，光栅）。使用新刺激的试点研究展示了标准模型没有预料到的反应模式——包括线性和非线性影响平衡的变化，以及方向调整的变化。刺激集是在其组合空间带宽孔径中逐渐减少限制的图案层次结构。在空间带宽孔径中最受限制的模式是高斯模式和类似边缘和线条的轮廓。随着空间带宽孔径的扩大，本质上二维模式和“非笛卡尔”模式出现，因此表明我们的方法也将在V1之外有用。问题B建立在我们对与视觉刺激可靠相关的神经活动精细结构的统计特征的识别之上。我们现在提议确定这些特征是否确实影响下游神经元的活动。我们的方法依赖于通过四极电极的多神经元记录，以及之前资助期间出现的实验结果和分析工具的组合。总之，这些研究将促进对神经计算的理解，从而从视觉图像中提取特征和物体。

DESCRIPTION (provided by applicant): We aim to advance our understanding of the nature of the operations performed by primary visual cortex, and how these operations are carried out. The research plan is organized around two basic questions. A. Are the spatial computations performed by neurons in V1 adequately described by oriented receptive fields, simple nonlinearities, and gain controls, or, conversely, do more complex spatial operations play an important role? B. Can firing rate of a local population be considered the main carrier of visual information, or, conversely, is the fine structure of neural activity (across time or across a local population) also important? Question A is motivated by growing experimental evidence that the operations performed by V 1 neurons are indeed complex. The experimental strategy described here uses new stimuli, precisely balanced in space and spatial frequency - in contrast to traditional stimuli, that are highly localized in space but broad in spatial frequency (e.g., spots and bars), or extended in space but highly localized in spatial frequency (e.g., gratings). Pilot studies using the new stimuli demonstrate response patterns not anticipated from standard models - including shifts in the balance of linear and nonlinear influences, and shifts in orientation tuning. The stimulus set is a hierarchy of patterns that are progressively less restricted in their combined space bandwidth aperture. The patterns that are the most restricted in space-bandwidth aperture are Gaussians and profiles that resemble edges and lines. As the space-bandwidth aperture is broadened, intrinsically two-dimensional patterns and "non-Cartesian" patterns emerge, thus suggesting that our approach will also be useful beyond V1. Question B builds on our identification of the statistical features of the fine structure of neural activity that are reliably correlated with visual stimuli. We now propose to determine whether these features indeed influence the activity of downstream neurons. Our approach relies on a combination of multiple-neuron recordings via tetrodes, and experimental results and analytic tools that emerged during the previous funding period. Together, these investigations will advance the understanding of neural computations that lead to extraction of features and objects from the visual image.