Statistical machine learning tools for understanding neural ensemble representations and dynamics

用于理解神经集成表示和动态的统计机器学习工具

• 批准号: 10510107
• 负责人: Uri Tzvi Eden
• 金额: $ 186.15万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10510107
• 关键词: Address; Algorithmic Software; Algorithms; Animals; Area; Brain; Cells; Cognition; Cognitive; Communities; Complex; Computer software; Data; Data Set; Development; Devices; Dimensions; Ensure; Esthesia; Future; Goals; Hippocampus (Brain); Joints; Location; Measurable; Measurement; Measures; Memory; Methods; Modeling; Modernization; Motor; Motor output; Nature; Neurons; Neurosciences; Pattern; Perception; Play; Population; Positioning Attribute; Problem Solving; Property; Psyche structure; Research; Research Personnel; Role; Sensory; Signal Transduction; Software Tools; Sorting - Cell Movement; Stimulus; Structure; System; Testing; Time; Work; cognitive process; computing resources; deep learning; deep neural network; experience; experimental study; high dimensionality; implementation tool; insight; large datasets; neuronal circuitry; novel; novel strategies; parallel computer; receptive field; relating to nervous system; response; sensory input; statistical and machine learning; theories; tool; user-friendly

The brain is a massively interconnected network of specialized circuits. Understanding how these circuits support sensation, perception, cognition, and action requires measuring activity patterns within and across regions, but the measurements themselves do not produce insight into the structure or function of the underlying neuronal system. Insight requires the applications of quantitative methods that relate neuronal activity patterns to experimentally measurable variables, including things like present and past sensory inputs, current location, and current or future motor outputs. The result is an “encoding” model relating measured variables to spiking activity. Through a simple application of Bayes rule, this encoding model can be used to create a “decoding” model. In decoding, the goal is to take a pattern of spiking activity, along with a previously developed encoding model, and assess the sensory, cognitive or motor representation corresponding to the spiking. Encoding and decoding algorithms are a fundamental part of modern systems neuroscience and play a critical role in helping us understand the nature and dynamics of neuronal representations. These approaches provide a powerful way to gain insight about neuronal populations, but several limitations of current algorithms blunt their efficacy. First, while modern deep neural networks can be powerful for decoding, they have multiple shortcomings in the context of scientific discovery. Second, advanced decoding algorithms tend to be too complex and computationally intensive for most researchers to implement in the analyses of large-scale neural datasets. Moreover, robust, easy to use software that would allow less sophisticated users to take advantage of these algorithms does not exist. Third, the results of decoding are typically very sensitive to the total number of neurons recorded. Fourth, while decoding a single variable (e.g. animal position, target value, etc.) is tractable, decoding multiple variables simultaneously is beyond the capacities of current approaches. Fifth, neural response properties and the quality of neural recording often changes through the course of an experiment. Existing decoding algorithms are either static or require repeated re-estimation of the encoding model to maintain estimation accuracy. Finally, decoding has traditionally focused on observable signals, such as the animal’s position, but recent work has focused on unobserved cognitive processes, such as mental exploration. New methods are needed to determine when decoding of cognitive processes is reliable. Solving these problems requires new approaches and new parallelized software that make these approaches easy to use and efficient for the community. We have developed clusterless decoding algorithms that make very efficient use of the available data, and here we will further develop those algorithms and the software that implements them to meet all of the challenges described above. The result will be a powerful set of tools that have the potential to drive new discoveries.

大脑是一个由专门电路组成的大规模互联网络。了解这些电路如何支持 感觉、知觉、认知和行动需要测量区域内和区域间的活动模式， 测量本身并不能深入了解神经元的结构或功能 系统洞察力需要应用定量方法，将神经元活动模式与 实验可测量的变量，包括现在和过去的感觉输入，当前位置， 当前或未来的电机输出。结果是一个“编码”模型，将测量的变量与尖峰活动联系起来。 通过简单的应用贝叶斯规则，这种编码模型可以用来创建一个“解码”模型。在 解码，目标是采取尖峰活动的模式，沿着先前开发的编码模型，以及 评估与尖峰对应的感觉、认知或运动表征。编码和解码 算法是现代系统神经科学的基础部分，在帮助我们 了解神经元表征的性质和动力学。这些方法提供了一种强有力的方法， 获得有关神经元群体的洞察力，但当前算法的几个局限性削弱了它们的功效。第一、 虽然现代深度神经网络在解码方面很强大，但它们在上下文中存在多个缺点。 科学发现。其次，高级解码算法往往过于复杂和计算量大 对于大多数研究人员来说，在分析大规模神经数据集时是密集的。此外，鲁棒， 一个易于使用的软件，将允许不太复杂的用户利用这些算法， 存在.第三，解码的结果通常对记录的神经元总数非常敏感。四、 当解码单个变量（例如动物位置、目标值等）时，是易于处理的，解码多个变量 这超出了当前方法的能力。第五，神经反应的性质和质量 在实验过程中，神经记录经常会发生变化。现有的解码算法是 静态的或需要编码模型的重复重新估计以保持估计精度。最后，解码 传统上专注于可观察到的信号，如动物的位置，但最近的工作集中在 未观察到的认知过程，如心理探索。需要新的方法来确定何时 认知过程的解码是可靠的。解决这些问题需要新的方法和 并行化的软件，使这些方法易于使用和有效的社区。我们有 开发了无簇解码算法，可以非常有效地利用可用数据，这里我们将 进一步开发这些算法和实现它们的软件，以应对上述所有挑战 以上其结果将是一套强大的工具，有可能推动新的发现。