MSM: Collaborative Research: Cortical Processing Across Multiple Scales

该项目的目标是创建理论和计算工具，以解释哺乳动物初级视觉皮质中发生的“大脑计算”，这是沿着视觉通路的第一个位置，在那里，单个神经细胞(神经元)“识别”视觉场景的基本特征，如模式定向。将开发一个大规模的、具有生物真实性的数值神经网络模型，以模拟初级视觉皮质如何充当使这种特征识别成为可能的“计算机”，并将用于模拟初级视觉皮质的一块大小约5毫米乘5毫米、包含近100万个神经元的斑块。还将开发初级视觉皮质的粗粒度表示，将其视为一个连续体而不是一组单独的神经元，并在描述中纳入多个实验运行的统计数据，而不是仅一个，从而消除了大量模拟的需要。最后，将开发混合表示，其中选择的神经元组用大规模神经元模型来描述，而其他的用大量的粗粒度表示来描述。这种表示在模拟大脑更大部分对刺激的真实神经元处理方面似乎特别有希望。模型和软件将在初级视觉皮质观察到的两个显著的处理例子上得到验证和展示：自发皮质活动的模式和运动错觉。前者被认为包含了数千个神经元在毫米尺度上的集体行为，并似乎在神经元倾向于相同方向的区域被激活。在一种流行的运动错觉中，显示一个紧跟着一个长条的小正方形，使该正方形看起来像是在“生长”成为条形。这种错觉的一种生理机制被实验观察到是实际的皮质活动，对应于感知到的正方形进入棒状物的“生长”，类似于真正的运动引起的。该软件和模型的一个预期任务是解释控制初级视觉皮质动态行为的这种机制，以及这些和其他皮质现象的基础。视觉皮质中的信息是如何处理的，是神经科学中最具挑战性的问题之一。提出的大尺度时空活动研究，使用了一个非常大的初级视觉皮质的计算模型和粗粒度的理论方法，解决了现代神经科学中放大的迫切需要，这是对最近发展的多模式、大规模实验方法的理论补充。在这个项目中开发的模型和软件旨在从定性和定量上对初级视觉皮质以及可能还有其他皮质区域的神经元计算所依据的生物学机制做出解释。该软件将方便神经科学领域的一大批研究人员使用，目的是帮助科学在理论上理解感觉感知机制以及可能的其他大脑功能方面取得重大进展。

The project's goal is to create theoretical and computational tools to explain "brain computations" taking place in the mammalian primary visual cortex, the first location along the visual pathway in which individual nerve cells (neurons) "recognize" elementary features of the visual scene, such as pattern orientation. A large-scale, biologically realistic numerical neuronal network model will be developed for simulating how the primary visual cortex acts as a "computer" to make this feature recognition possible, and used to simulate a patch of the primary visual cortex of about 5 millimeters by 5 millimeters in size and containing close to a million neurons. Coarse-grained representations of the primary visual cortex will also be developed, which treat it as a continuum rather than a set of individual neurons and incorporate the statistics of a multitude of experimental runs in the description instead of just one, thus eliminating the need for large numbers of simulations. Finally, hybrid representations will be developed, in which select groups of neurons are described by a large-scale neuronal model, while others are described by a bulk coarse-grained representation. Such representations appear particularly promising for simulating realistic neuronal processing of stimuli in yet larger portions of the brain.The models and software will be validated and showcased on two striking examples of observed processing in the primary visual cortex: patterns of spontaneous cortical activity and motion illusions. The former were seen to encompass collective behavior of thousands of neurons on millimeter scales, and appear to get activated in areas in which neurons prefer the same orientation. In a popular motion illusion, showing a small square immediately followed by a long bar makes the square appear as if it is "growing" to become the bar. A physiological mechanism for this illusion was observed experimentally to be actual cortical activity corresponding to the perceived "growth" of the square into the bar, similar to that caused by real motion. One intended task for the software and models is to explain such mechanisms governing the dynamical behavior of the primary visual cortex and underlying these and other cortical phenomena.How information is processed in the visual cortex is one of the most challenging questions in neuroscience. The proposed study of spatiotemporal activity over large scales, using a very large computational model of the primary visual cortex as well as coarse-grained theoretical methods, addresses an urgent need for scale-up in modern neuroscience, which is the theoretical complement to the recent development of multi-mode, large-scale experimental methods. The models and software developed in this project are aimed at obtaining qualitatively and quantitatively realistic explanations of the biological mechanisms that underlie neuronal computations in the primary visual cortex, and possibly other cortical areas. The software will be made readily accessible to a large group of researchers across the neurosciences, with the aim that its results will help science make significant inroads into theoretical understanding of the mechanisms of sensory perception and possibly other brain functions.