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

人类的情绪是对大脑网络互动中出现的挑战和机遇的动态，多维的反应。这些动力学的破坏是许多精神障碍（包括焦虑和抑郁）中情绪失调的基础。为了实证研究人类情绪推理的神经基础，实验者经常让观察者观看情感内容不同的自然图像，同时使用脑电图（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，但仍有许多关于自然场景中情感识别的计算原理有待了解。经验神经科学家面临的一个主要障碍是无法精确操纵人类神经系统并测试成像数据的后果。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无成本的计算机模拟中测试这些假设的有前途的工具。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，通过协同整合神经元，电路和系统级机制，在现实世界中进行情感推理。认识到最先进的DNN以自下而上和仅前馈处理为中心，这与生理学中观察到的强烈的目标导向的自上而下调制复发不一致，该项目旨在丰富DNN，并通过整合目标导向的自上而下调制和相互作用DNN来实现更紧密的AI-神经科学交互，并测试模型假设和神经成像数据预测。为了实现这些目标，该项目旨在开发一个大脑启发的目标导向和双向深度学习模型，用于情感推理。尽管今天的深度学习作为生物视觉建模的框架显示出了巨大的前景，但它们的架构仅限于模拟用于面部/物体/场景识别的视觉皮层，很少超越颞下皮层（IT），这是建模高级认知过程所必需的。在这个项目中，我们建议通过将计算机模拟的杏仁核模块集成到DNN的视觉皮层架构（VCA模型）中来构建一个生物学上合理的深度学习架构。研究人员希望通过目标导向的注意启动和多通路预测编码建立神经元、回路和系统水平的调制，以1）阐明人工神经元对自然主义情绪的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绪反应，以及3）通过将在同步fMRI-EEG数据中观察到的注意启动和时间泛化与使用我们的大脑启发的VCA模型的计算观察进行比较，改进对人类神经行为数据的神经科学理解。该项目将两项关键创新引入DNN架构，并在应用于复杂的现实任务时展示其上级性能。该项目的成功实施将推动新一代人工智能模型的开发，这些模型将受到神经科学的启发，进而为神经科学研究提供动力。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.