CAREER: Approximate inference at the intersection of neuroscience and machine learning

职业：神经科学和机器学习交叉点的近似推理

• 批准号: 2143440
• 负责人: Ralf Haefner
• 金额: $ 60万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143440&HistoricalAwards=false
• 关键词: CAREER; Approximate; inference; intersection; neuroscience

Finding patterns in complex data is of great importance to modern society. It lies at the heart of forecasting in business and economics, analyzing data from large-scale experiments, and powering the ongoing revolution in artificial intelligence and machine learning. The brain faces exactly the same challenge: how to extract behaviorally relevant patterns in the large amounts of data it receives from its senses, including millions of photoreceptors in the eyes. This project will investigate two important aspects of the computations underlying this process: how to decide whether to combine two pieces of information, for instance, from two photoreceptors, or even two different senses, and the consequences of doing so not instantaneously and exactly, but over time and approximately. By investigating these questions in the brain, this project aims to extract insights that are relevant for both neuroscience and machine learning. The main contributions of this project to neuroscience and cognitive science are a deeper understanding of the central computational motif underlying sensory processing and new computational theories of confirmation bias and attention. The central contributions to machine learning will consist in suggesting architectural changes to current deep learning architectures and in evaluating their performance benefits. In its educational part, this project will develop and evaluate a curriculum for an interdisciplinary, research project-based college-level course for educating the next generation of computational neuroscience and machine learning researchers.This project focuses on two key questions: (1) What is the architecture of the deep probabilistic model that the brain has learned, and (2) How does the brain perform approximate and sequential, as opposed to exact and instantaneous, inference in this model? The researchers will address the first question by proposing a deep hierarchical causal inference-based model for motion perception. They will quantitatively test this model using human psychophysical experiments and collaborate to test it using neurophysiology experiments. The relationship of the probabilistic model's central computational motif to other computations such as divisive normalization and predictive coding will be explored. In collaboration with machine learning researchers the benefits of incorporating the computational insights into deep learning systems will be quantified. The second research aim investigates the consequences of approximate inference computations in two contexts. First, the researchers will develop a rigorous computational theory relating limited biological and computational "resources" during approximate inference. Second, they will compare the biases due to approximate inference with those found in humans using psychophysical evidence integration tasks. In preliminary data the emergence of a confirmation bias as the result of approximate inference was found -- both in the context of passive interpretation of visual evidence, and in the context of active inference using eye-movements. In addition to a better understanding of inference in the brain, the goal of this work is to yield insights into strategies for how to counter biases and design efficient artificial intelligence systems.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.

在复杂数据中发现模式对于现代社会非常重要。它是商业和经济预测的核心，分析来自大规模实验的数据，并推动人工智能和机器学习的持续革命。大脑面临着完全相同的挑战：如何从从感官接收的大量数据中提取与行为相关的模式，包括眼睛中的数百万个光感受器。这个项目将研究这一过程背后的计算的两个重要方面：如何决定是否将两条信息联合收割机，例如，从两个光感受器，甚至两个不同的感官，以及这样做的后果不是即时和精确的，而是随着时间的推移和近似的。通过研究大脑中的这些问题，该项目旨在提取与神经科学和机器学习相关的见解。该项目对神经科学和认知科学的主要贡献是更深入地理解了感觉处理的核心计算基序以及确认偏差和注意力的新计算理论。对机器学习的核心贡献将包括对当前深度学习架构提出架构更改，并评估其性能优势。在其教育部分，该项目将开发和评估一个跨学科的，基于研究项目的大学水平课程，以教育下一代计算神经科学和机器学习研究人员的课程。该项目侧重于两个关键问题：（1）大脑已经学习的深度概率模型的架构是什么，以及（2）大脑如何执行近似和顺序，而不是精确和即时的推理研究人员将通过提出一个基于深层因果推理的运动感知模型来解决第一个问题。他们将使用人类心理物理学实验定量测试这个模型，并合作使用神经生理学实验来测试它。将探讨概率模型的中央计算主题与其他计算的关系，如分裂归一化和预测编码。与机器学习研究人员合作，将计算洞察力融入深度学习系统的好处将被量化。第二个研究目标调查的后果近似推理计算在两种情况下。首先，研究人员将开发一个严格的计算理论，在近似推理过程中将有限的生物和计算“资源”联系起来。其次，他们将比较由于近似推理的偏差与使用心理物理证据整合任务在人类中发现的偏差。在初步的数据中，我们发现，在被动解释视觉证据的情况下，以及在使用眼球运动进行主动推理的情况下，由于近似推理而出现了确认偏差。除了更好地理解大脑中的推理外，这项工作的目标是深入了解如何对抗偏见和设计有效的阿尔蒂官方智能系统的策略。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智力价值和更广泛的影响审查标准进行评估来支持。