RI: Exploring neurobiological strategies of visual scene analysis using oscillations in recurrent neural circuitry

反复连接和内在节律性神经活动在生物视觉通路中普遍存在，它们最早出现在视网膜。尽管视网膜回路的这些特性的功能作用还没有完全解决，但最近的研究表明，它们可能有助于传达关于视觉背景的信息，甚至是场景的主旨。此外，新的实验工作表明，视网膜网络产生的活动的振荡模式是从丘脑传递到皮质的。然而，到目前为止，视网膜振荡在计算机视觉系统中通常被忽略，即使是基于神经原理的系统也是如此。如果人工系统在视觉感知上与人类的表现相匹配，那么这种对生物回路的缺乏可能就不值得注意了。但事实并非如此。在分析杂乱的场景、在嘈杂的背景或不同的照明条件下识别物体等日常任务中，人类做得比计算机好得多。因此，该项目旨在开发受生物启发的模型，其中包括振荡网络，以改进人工视觉中的场景分析。新模型将在视网膜电路中纳入局部和分布式连接，并利用高效稀疏编码方案，这是理解感觉处理的一个强大的新概念。通过同时考虑局部和空间上广泛的电路，人们期望该模型能够编码关于刺激的两种互补类型的信息。过去的工作表明，尖峰率相对于刺激的变化编码关于局部特征的信息。这种类型的速率(或刺激锁定)编码可以由小规模电路建模。新模型将包括持续的振荡活动，这种活动由大型循环网络在内部产生，但也受到感觉输入的调节。此外，视觉诱发的这些固有振荡的时间结构的变化发生在比视觉诱发的速率变化更精细的时间尺度上。因此，原则上，视觉信息可以通过相对于内在节律的尖峰计时来编码。此外，由于视网膜振荡是由分布式网络产生的，它们很可能提供关于刺激的全局特征的信息。因此，如果成功，新模型将能够同时捕捉有关局部细节和场景要点的信息。大量应用程序将受益于对信息在早期视觉系统中是如何编码的更深入的理解。例如，本文提出的研究结果将对视觉假体的发展以及涉及图像处理的技术应用具有价值，从适应感官感知的图像压缩的新方法到自动对象分割、场景分析和识别问题。

Recurrent connections and intrinsic rhythmic neural activity are ubiquitous in biological visual pathways, where they first appear in the retina. Although the functional role of these properties of the retinal circuit is not fully resolved, recent work suggests that they might help convey information about visual context or even the gist of a scene. Further, new experimental work shows that the oscillatory patterns of activity generated by retinal networks are relayed from the thalamus to the cortex. As yet, however, retinal oscillations are usually ignored in systems of computer vision, even those based on neural principles. If artificial systems matched human performance in visual perception, this lack of attention to the biological circuitry might not be worthy of note. But this is not the case. Humans do a far better job than computers in routine tasks like analyzing cluttered scenes or recognizing objects in noisy backgrounds or under different lighting conditions. Thus this project aims to develop biologically inspired models that include oscillating networks to improve scene analysis in artificial vision.The new models will incorporate local and distributed connections in the retinal circuit and also take advantage of the scheme of efficient sparse coding, a powerful new concept for understanding sensory processing. By taking both local and spatially extensive circuits into account, the expectation is that the model will be able to encode two complementary types of information about the stimulus. Past work has shown that changes in spike rate with respect to the stimulus encode information about local features. This type of rate (or stimulus-locked) coding can be modeled by small scale circuits. The new models will include ongoing oscillatory activity that is generated internally by large recurrent networks but is also modulated by sensory input. Further, visually evoked changes in the temporal structure of these intrinsic oscillations occur at finer time scales than visually evoked changes in rate. Thus, in principle, visual information could be encoded by spike timing with respect to intrinsic rhythms. Moreover, since the retinal oscillations are generated by distributed networks, it is likely that they provide information about global feature of the stimulus. Thus, if successful, the new models will be able to capture, at once, information about local detail and the gist of scene.Numerous applications would benefit from a deeper understanding of how information is encoded in the early visual system. For example, the models that will result from the research proposed here have value for the development of visual prosthetics as well as for technical applications that involve image-processing from new methods for image compression adapted to sensory perception to the problem of automated object segmentation, scene analysis and recognition.