NCS-FO: Brain-Informed Goal-Oriented and Bidirectional Deep Emotion Inference

NCS-FO：大脑知情的目标导向双向深度情感推理

• 批准号: 2318984
• 负责人: Ruogu Fang
• 金额: $ 92万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318984&HistoricalAwards=false
• 关键词: NCS; FO; Brain; Informed; Goal

Human emotions are dynamic, multidimensional responses to challenges and opportunities that emerge from network interactions in the brain. Disruptions of these dynamics underlie emotional dysregulation in many mental disorders including anxiety and depression. To empirically study the neural basis of human emotion inference, experimenters often have observers view natural images varying in affective content, while at the same time recording their brain activity using electroencephalogram (EEG) and/or Functional magnetic resonance imaging (fMRI). Despite extensive research over the last few decades, much remains to be learned about the computational principles subserving the recognition of emotions in natural scenes. A major roadblock faced by empirical neuroscientists is the inability to carry out precisely manipulate human neural systems and test the consequences in imaging data. Deep Neural Networks (DNN), owing to their high relevance to human neural systems and extraordinary prediction capability, have become a promising tool for testing these sorts of hypotheses in swift and nearly costless computer simulations. The overarching goal of this project is to develop a neuroscience-inspired, DNN-based deep learning framework for emotion inference in real-world scenarios by synergistically integrating neuron-, circuit-, and system-level mechanisms. Recognizing that the state-of-the-art DNNs are centered on bottom-up and feedforward-only processing, which disagrees with the strong goal-oriented top-down modulation recurrence observed in the physiology, this project aims to enrich DNNs and enable closer AI-neuroscience interaction by incorporating goal-oriented top-down modulation and reciprocal interactions DNNs and test the model assumptions and predictions on neuroimaging data.To meet these goals, the project aims to develop a brain-inspired goal-oriented and bidirectional deep learning model for emotion inference. Despite the great promise shown by today’s deep learning as a framework for modeling biological vision, their architecture is limited to emulating the visual cortex for face/object/scene recognition and rarely goes beyond the inferotemporal cortex (IT), which is necessary for modeling high-level cognitive processes. In this project, we propose to build a biologically plausible deep learning architecture by integrating an in-silico amygdala module into the visual cortex architecture in DNN (the VCA model). The researchers hope to build neuron-, circuit-, and system-level modulation via goal-oriented attention priming, and multi-pathway predictive coding to 1) elucidate the mechanism of selectivity underlying preference and response to naturalistic emotions by artificial neurons; 2) differentiate fine-grained emotional responses via multi-path predictive coding, and 3) refine the neuroscientific understanding of human neuro-behavioral data by comparing attention priming and temporal generalization observed in simultaneous fMRI-EEG data to the computational observations using our brain-inspired VCA model. This project introduces two key innovations, both patterned after how brain operates, into DNN architecture and demonstrate their superior performance when applied to complex real-world tasks. Successful execution of the project can lead to the development of a new generation of AI-models that are inspired by neuroscience and that may in turn power neuroscience research.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.

人类情绪是对大脑中网络互动中出现的挑战和机会做出的动态、多维的反应。这些动力的破坏是许多精神障碍的情绪失调的基础，包括焦虑和抑郁。为了经验性地研究人类情绪推理的神经基础，实验者通常让观察者观看情感内容不同的自然图像，同时使用脑电(EEG)和/或功能磁共振成像(FMRI)记录他们的大脑活动。尽管在过去的几十年里进行了广泛的研究，但关于自然场景中情感识别的计算原理仍有许多需要了解。经验神经学家面临的一个主要障碍是无法进行精确的人类神经系统操作，并无法在成像数据中测试结果。深度神经网络(DNN)由于其与人类神经系统的高度相关性和非凡的预测能力，已成为在快速且几乎不花费成本的计算机模拟中检验这类假设的一种很有前途的工具。该项目的总体目标是开发一个受神经科学启发的、基于DNN的深度学习框架，通过协同集成神经元、电路和系统级别的机制来进行真实世界场景中的情感推理。考虑到最新的DNN集中于自下而上和仅前馈的处理，这与生理学中观察到的强烈的目标导向的自上而下的调制重复不一致，本项目旨在通过结合目标导向的自上而下调制和交互DNN来丰富DNN，并使更紧密的AI-神经科学交互成为可能，并在神经成像数据上测试模型假设和预测。为了实现这些目标，本项目旨在开发一个受大脑启发的目标导向的双向深度学习模型，用于情绪推理。尽管当今的深度学习作为生物视觉建模的框架显示出巨大的前景，但它们的体系结构仅限于模拟人脸/对象/场景识别的视觉皮质，很少超越建模高级认知过程所必需的颞下皮质(IT)。在这个项目中，我们建议通过将硅内杏仁核模块集成到DNN(VCA模型)的视觉皮质结构中来构建一个生物学上可行的深度学习结构。研究人员希望通过目标导向的注意启动和多路径预测编码建立神经元、电路和系统级别的调制，以1)阐明人工神经元对自然情感偏好和反应的选择性机制；2)通过多路径预测编码区分细粒度的情感反应；3)通过比较同时在fMRI-EEG数据中观察到的注意启动和时间概括与我们的大脑启发的VCA模型的计算观察，来完善对人类神经行为数据的神经科学理解。该项目在DNN架构中引入了两个关键创新，都是模仿Brain的运作方式，并展示了它们在应用于复杂的现实世界任务时的卓越性能。该项目的成功实施可以导致新一代人工智能模型的开发，这些模型受到神经科学的启发，并可能反过来推动神经科学研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。