CAREER: Extracting principles of neural computation from large scale neural recordings through neural network theory and high dimensional statistics

最近的技术进步现在可以记录复杂行为期间的数千个神经元。 这种实验能力可能会揭示大脑如何编码感觉，形成记忆，学习任务，做出决定和产生运动动作。 然而，要想科学地理解心智的心理能力是如何从大脑的生物湿件中产生的，还存在着重大障碍。首先，数据分析方法不足以理解目前从大脑收集的大量数据集。其次，理论方法不足以优化设计大规模的神经记录，以及将许多神经元的集体生物物理学与感觉、思想和行动背后的心理过程联系起来。 该项目将开发新的数据分析和理论方法，以提取对大脑如何产生认知的概念性理解。这些方法将在许多研究感知、记忆、学习、决策和运动控制的实验室的大规模录音中进行测试。它们也将被应用于开发更好的学习协议和神经假体设备。它将建立在高维统计学的进步，以发展一种理论，即神经元子集何时以及如何反映它们嵌入其中的更大的未观察到的电路的集体动力学。这一理论将为未来大规模记录实验的有效设计提供定量指导。其次，它将基于深度学习的进步来开发算法方法，以提取对复杂神经网络如何解决任务的概念性理解。这些算法方法将阐明网络连通性和动力学的哪些方面对于理解神经回路如何执行计算至关重要，从而为未来的神经科学实验提供指导。最后，它将推进神经网络学习的理论，以更好地理解先验经验的结构如何决定学习的神经连接，以及如何优化这个学习过程。这些一般性的理论进展将在具体的、密切的实验合作中得到完善和测试，包括：识别运动皮层中的反馈控制律，在海马记忆回路中发现吸引子动力学的特征，理解视网膜中感知和前额叶皮层中决策的神经算法，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响评审标准。

Recent technological advances now enable recordings of thousands of neurons during complex behaviors. Such experimental capabilities could potentially reveal how the brain encodes sensations, forms memories, learns tasks, makes decisions, and generates motor actions. However, there exist major obstacles to attaining a scientific understanding of how the psychological capabilities of the mind emerge from the biological wetware of the brain. First, data analytic methods are not adequate to make sense of the massive datasets currently being gathered from the brain. Second, theoretical methods are not adequate for both optimally designing large-scale neural recordings, and bridging scales from the collective biophysics of many neurons to psychological processes underlying sensations, thoughts and actions. This project will develop novel data analytic and theoretical methods to extract a conceptual understanding of how the brain gives rise to cognition. These methods will be tested in large-scale recordings from many experimental labs studying perception, memory, learning, decision making and motor control. They will also be applied to developing better learning protocols and neural prosthetic devices.This project will pursue three overarching aims. It will build on advances in high dimensional statistics to develop a theory of when and how subsets of neurons reflect the collective dynamics of the much larger unobserved circuit in which they are embedded. This theory will provide quantitative guidance for the efficient design of future large-scale recording experiments. Second, it will build on advances in deep learning to develop algorithmic methods for extracting a conceptual understanding of how complex neural networks solve tasks. These algorithmic methods will elucidate which aspects of network connectivity and dynamics are essential to understanding how neural circuits perform their computations, thereby providing guidance for what to measure in future neuroscience experiments. Finally, it will advance theories of neural network learning to better understand how the structure of prior experience determines learned neural connectivity, and how this learning process can be optimized. These general theoretical advances will be refined and tested in specific, close experimental collaborations, involving: identifying feedback control laws in motor cortex, finding signatures of attractor dynamics in the hippocampal memory circuits, understanding the neural algorithms for perception in the retina and decision making in prefrontal cortex, and developing frameworks for understanding rapid rodent learning built upon prior experiences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.