CRII: RI: Neuromodulated Deep Vision: Continual Learning and Goal-Oriented Adaptation

深度神经网络正在彻底改变许多领域和行业。这些系统可以学习解决从解析自然语言到发现新药的各种问题。然而，目前的网络只能解决个别的静态问题;它们不能随着时间的推移逐渐学习或真实的调整它们的计算。该项目旨在通过模拟大脑中的化学过程来弥合人类和机器智能之间的差距，帮助人类根据环境学习和调整行为。具体来说，化学物质乙酰胆碱（ACh）已被证明可以通过选择性地提高或降低单个神经元的活动水平来调节对视觉刺激的反应。这种化学物质对婴儿的视觉学习也至关重要。大脑中乙酰胆碱浓度不足会导致视觉注意力不佳，也与记忆缺陷和阿尔茨海默病有关。的疾病。因此，向深度神经网络中添加类似于ACh的机制可能会使它们随着时间的推移而学习，并使其行为适应快速变化的条件-从森林保护到网络安全等领域的需求日益增长。更详细地说，深度卷积神经网络（CNN）是各种图像处理任务的最先进方法，例如对象识别和语义分割。该项目将研究如何通过模拟这种化学过程的空间和时间动态来将ACh样调节添加到CNN中。具体来说，CNN将连接到一组空间和时间异构的调制源。CNN中的每个神经元都有不同的受体，决定特定神经元如何对不同的ACh水平做出反应。该系统将使用两个控制器：一个用于确定跨网络的不同部分的ACh的当前水平，另一个用于设置正确的上下文（即，以模拟来自更高皮层区域的自上而下的反馈）。第一个控制器将允许系统快速调整其行为，而第二个控制器将允许长期学习。该项目是开发智能系统的第一步，这些系统像生物有机体一样，可以自主科普不断变化的动态环境。除了技术贡献外，该项目还将使美国最多元化的机构之一格鲁吉亚州立大学的学生能够促进人工智能、神经科学和相关STEM学科之间的跨学科联系。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Deep neural networks are revolutionizing many fields and industries. These systems can learn to solve problems ranging from parsing natural language to discovering new drugs. However, current networks can only solve individual, static problems; they cannot learn gradually over time or adapt their computations in real time. This project seeks to bridge the gap between human and machine intelligence by modeling a chemical process in the brain that help humans learn and adjust their behavior based on context. Specifically, the chemical acetylcholine (ACh) has been shown to modulate responses to visual stimuli by selectively raising or lowering the activity level of individual neurons. This chemical is also crucial for visual learning in infants. An insufficient concentration of ACh in the brain leads to poor visual attention and has also been linked to memory deficits and Alzheimer?s disease. Thus, adding ACh-like mechanisms to deep neural networks can potentially allow them to learn over time and adapt their behavior to rapidly changing conditions--a growing need in domains ranging from forest conservation to cybersecurity.In more detail, deep convolutional neural networks (CNNs) are the state-of-the-art approach for a wide variety of image processing tasks, such as object recognition and semantic segmentation. This project will investigate how to add ACh-like regulation to CNNs by modeling the spatial and temporal dynamics of this chemical process. Specifically, the CNN will be connected to a set of spatially and temporally heterogeneous sources of modulation. Each neuron in the CNN will have different receptors that determine how that particular neuron responds to different ambient levels of ACh. The system will use two controllers: one to determine the current level of ACh across different parts of the network and another to set the correct context (i.e., to model top-down feedback from higher cortical areas). The first controller will allow the system to quickly adjust its behavior, while the second will enable long-term learning. This project constitutes an initial step in developing intelligent systems that, like biological organisms, can autonomously cope with changing, dynamic environments. In addition to its technical contributions, this project will allow students at Georgia State University, one of the most diverse institutions in the country, to foster interdisciplinary connections between artificial intelligence, neuroscience, and related STEM disciplines.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.