Spatiotemporal neuronal system dynamics underlying hierarchical visual representations of objects and faces for primate perception and discrimination

人类和(非人类灵长类)大脑是最复杂的生物系统之一。神经科学中的一个主要挑战是理解大脑是如何作为一个动态的复杂系统运行的，以及正常感知和认知的神经机制是什么。对象和人脸的视觉表示通过高度并行化和分层组织的处理阶段跨多个相互连接的区域同时编码。我们的目标是通过发现在颞叶的高级视觉区域和与选择相关的前额叶区域内和之间运行的时空过程来促进对系统水平神经元相互作用的科学理解，这些时空过程共同奠定了物体/人脸感知和辨别的基础。我们只能通过将多电极、多区域记录与干预相结合来了解大脑区域如何在神经元水平上因果相互作用。要做到这一点，我们必须记录大脑中的基本功能单位神经元(而不是包含数十万神经元的神经成像‘体素’)，我们必须使用动物模型，因为这种记录是侵入性的。最近的技术进步促进了多区域多电极记录和研究许多区域和皮质层内和之间的神经元动力学。此外，我们只能通过将神经元记录与干预相结合来了解大脑区域如何在神经元水平上因果互动。自从Hebb的突触可塑性原理以来，相互连接的神经元活动的时空动力学一直被认为是学习的关键原理。时空放电模式存在于不同行为任务的多个区域。在视觉的情况下，分布式视觉表征的绑定可以利用这些原理，但是在多个时间尺度和时间滞后上提取复杂的神经元放电(棘波)序列在计算上是复杂的。现在，已经开发了新的高效算法来识别在不同时间尺度上具有一致尖峰延迟的较大神经元集合，而不需要假设基本的编码方法。我们将应用新的计算和统计工具来识别细胞组合，并提取区域内和区域之间一致的多神经元尖峰序列。我们的经验记录将通过运行针对尖峰神经网络模型的GPU优化模拟来补充，以评估我们的发现并生成新的预测。来自更多神经元的记录同时提高了“维度诅咒”和网络科学，为系统神经科学提供了先进的分析和可扩展的工具，补充了降维方法，并允许对网络结构的量化分析和对整个种群活动的概率描述。这些方法使我们能够更好地理解计算，量化和跟踪关键概念的动力学，如‘细胞集合’，并跟踪由行为或大脑干预引起的变化。我们的目标分为三个子目标：前两个是理解神经元在多个更高的视觉区域内和多个高级视觉区域之间的相互作用的动态时空表示和机制，这分别是对物体和面部的正常感知的基础。在人脸的情况下，我们将研究不同的颞叶和额叶人脸斑块的这种动力学和相互作用。我们的第三个子目标是了解这些机制和相互作用在记忆、分类和关于物体和面孔的选择行为的背景下如何不同，特别强调额叶-颞叶的相互作用。

The human and (non-human primate) brain is one of the most complex biological systems. A major challenge in neuroscience is to understand how the brain operates as a dynamic complex system and what neuronal mechanisms underlie normal perception and cognition. Visual representations of objects and faces are simultaneously encoded across multiple reciprocally connected regions with highly parallelized and hierarchically organized processing stages. Our objective is to advance scientific understanding of systems level neuronal interactions by discovering the spatio-temporal processes operating within and between higher visual areas in the temporal lobe and choice-related prefrontal regions, that together underlie object/face perception and discrimination. We can only learn how brain areas causally interact at the neuronal level by combining multi-electrode, multi-area recordings with interventions. To do this we must record the fundamental functional units in the brain, neurons, (not neuroimaging 'voxels' containing hundreds of thousands of neurons) and we must use animal models because such recording is invasive. Recent technological advances facilitate multi-area multi-electrode recordings and investigation of neuronal dynamics both within and between many areas and cortical layers. Moreover, we can only learn how brain areas causally interact at the neuronal level by combining neuronal recordings with interventions. Ever since Hebb's principles of synaptic plasticity, spatiotemporal dynamics of interconnected neuronal activities have been implicated as key principle underlying learning. Spatiotemporal firing patterns exist in many areas across different behavioral tasks. In the case of vision, the binding of distributed visual representations may exploit such principles but the extraction of complex neuronal firing ('spike') sequences, at multiple temporal scales and time-lags, is computationally complex. Now, new eEfficient algorithms have been developed for identifying larger assemblies of neurons with consistent spike delays at varying temporal scales without making assumptions of the underling encoding method. We will apply new computational and statistical tools to identify cell assemblies and extract consistent multi-neuron spiking sequences within and across areas. Our empirical recordings will be complemented by running GPU optimised simulations of spiking neural networks models to assess our findings and generate novel predictions. Recording from more neurons simultaneously raises the 'curse of dimensionality' and Network science, offers advanced analytical and scalable tools for systems neuroscience, complementing dimension-reduction approaches, and allowing for quantitative analyses of network structure and probabilistic descriptions of population-wide activity. These approaches allow us to better understand computations, quantify and track dynamics of key concepts such as 'cell assemblies', and track changes elicited by behaviour or brain interventions.Our objective is divided into 3 sub-goals: The first two are to understand the dynamic spatiotemporal representations and mechanisms of neuronal interaction operating within and between multiple higher visual areas, and cortical layers, that underlie normal perception of objects and faces respectively. In the case of faces we will study such dynamics and interactions across different temporal and frontal lobe face patches. Our third sub-goal is to understand how these mechanisms and interactions differ in the context of memory, categorization, and choice behaviour with respect to objects and faces with a special emphasis on frontal lobe - temporal lobe interactions.