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III: Small: Modular structures in the brain and artificial learningsystems: emergence and function

III：小：大脑和人工学习系统的模块化结构：出现和功能

基本信息

批准号：
2151077
负责人：
Ila Fiete
金额：
$ 46.43万
依托单位：
Massachusetts Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151077&HistoricalAwards=false
关键词：
III Small Modular structures brain

项目摘要

Deep learning has made great strides, with artificial neural networks outperforming humans after training exhaustively on specialized tasks. However, rapid learning and flexible intelligence, hallmarks of biological brains, remain out of reach. In this proposal, we seek to understand an important feature of biological brains that will be critical for better and more interpretable artificial intelligence: the existence of modular architectures that are combined in rich ways to solve multiple problems with some shared sub-structure. We will study three aspects of modular architecture: 1) How observed modular structures in the brain might arise with minimal training, through simple and local constraints on how far a neuron can extend and how many synapses it can make; 2) What is the utility of a modular organization in neural circuits in solving tasks, in terms of robustness and the speed of learning, in the context of known neural circuits and function; 3) How the modular architectures and principles that lead to rapid modularization we learn about in 1)-2) can be imported into artificial neural networks to improve the learning speed and flexibility of machine intelligence. Thus, we seek to better understand brains, build a stronger dialogue between neuroscience and artificial intelligence, and use the resulting insights to improve machine intelligence. The grant will provide training opportunities to students and postdoctoral fellows in cutting-edge areas of strong interest for industry, government, and science, and we will focus on training a highly skilled and diverse workforce in these areas.Compositionality, i.e., the ability to learn and perform complex cognitive function by re-combining simpler sub-functions, is fundamental to the capabilities of the mind and is the basis for general intelligence. Underlying this ability is the presence of modular structures in the brain. Modular structures confer inherent advantages, such as increased stability to perturbations and faster learning. We will investigate mechanisms for the emergence of modularity in natural and artificial systems. Existing models for modularity are based primarily on top-down supervised learning, which requires large amounts of learning time and data. Our central hypothesis is that local constraints on connectivity in the brain provide strong prior biases towards modularization, and these lead to the rapid emergence of modular structure and improved function. First, we will use theoretical and computational tools to model low-level constraints to probe how they may drive modularization in neural circuits observed in the brain, including grid cells in the entorhinal cortex. Second, we will analyze the advantages conferred by modular organization in biological systems by studying the properties of high-capacity memory architectures directly inspired by the entorhinal-hippocampal circuit, with mEC-like modular subnetworks. Third, we will combine our developed modularization mechanisms and understanding of biological architectural circuitry to import similar advantages into artificial learning systems, creating artificial neural networks that achieve robust modular solutions to complex real-world tasks. Thus, our work will help to characterize how biological and artificial networks may spontaneously modularize to support robust and efficient inference and learning.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.

深度学习已经取得了巨大的进步，人工神经网络在经过专门任务的详尽训练后，表现优于人类。然而，快速学习和灵活的智力——生物大脑的特征——仍然遥不可及。在本提案中，我们试图理解生物大脑的一个重要特征，这将对更好、更可解释的人工智能至关重要：模块化架构的存在，这些架构以丰富的方式组合在一起，以一些共享的子结构来解决多个问题。我们将研究模块化结构的三个方面：1)通过简单和局部的限制，神经元可以延伸多远，可以制造多少突触，如何在最小的训练下产生大脑中观察到的模块化结构；2)在已知神经回路和功能的情况下，在鲁棒性和学习速度方面，神经回路中模块化组织在解决任务方面的效用是什么？3)如何将我们在1)-2)中了解到的导致快速模块化的模块化架构和原理引入人工神经网络，以提高机器智能的学习速度和灵活性。因此，我们寻求更好地了解大脑，在神经科学和人工智能之间建立更强有力的对话，并利用由此产生的见解来改进机器智能。这笔拨款将为学生和博士后提供培训机会，在工业、政府和科学领域对前沿领域有浓厚兴趣，我们将重点培养这些领域的高技能和多样化的劳动力。组合性，即通过重新组合更简单的子功能来学习和执行复杂认知功能的能力，是心智能力的基础，也是一般智力的基础。这种能力的基础是大脑中存在的模块化结构。模块化结构赋予了固有的优势，例如增强了对扰动的稳定性和更快的学习。我们将研究自然和人工系统中出现模块化的机制。现有的模块化模型主要基于自上而下的监督学习，这需要大量的学习时间和数据。我们的中心假设是，大脑中连接的局部约束对模块化提供了强烈的先验偏见，这导致了模块化结构的快速出现和功能的改进。首先，我们将使用理论和计算工具来模拟低级约束，以探索它们如何驱动在大脑中观察到的神经回路中的模块化，包括内嗅皮层中的网格细胞。其次，我们将分析模块化组织在生物系统中的优势，通过研究直接受内嗅-海马体回路启发的高容量存储架构的特性，具有类似于mec的模块化子网络。第三，我们将结合我们开发的模块化机制和对生物建筑电路的理解，将类似的优势引入人工学习系统，创建人工神经网络，为复杂的现实世界任务实现强大的模块化解决方案。因此，我们的工作将有助于描述生物和人工网络如何自发模块化，以支持鲁棒和有效的推理和学习。该奖项反映了美国国家科学基金会的法定使命，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。