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.

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