CAREER: Developing Neural Network Theory for Uncovering How the Brain Learns

职业：发展神经网络理论以揭示大脑如何学习

• 批准号: 2239780
• 负责人: Cengiz Pehlevan
• 金额: $ 60.25万
• 依托单位: Harvard University
• 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: 2023-10-01 至 2028-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239780&HistoricalAwards=false
• 关键词: CAREER; Developing; Neural; Network; Theory

Despite many recent advances enabling the collection of large-scale data on the brain's activity and connectivity, our ability to extract principles from such data of how the brain learns is still limited. This shortfall arises from the absence of a thoroughly developed and predictive theory that elucidates and models learning in the brain at the neural level. To address this gap, this project will develop new theoretical frameworks and mathematical models to help formulate experimentally testable hypotheses about how the brain's neural networks learn. These frameworks will address how data are represented in the brain and how these representations are learned through synaptic plasticity. They will further probe why existing neural network models of the brain lag behind the artificial neural networks that empower AI systems in certain tasks. Results of this project will enhance our understanding of brain function and will be integrated into in education and outreach efforts at the high school, college, graduate and post-graduate levels, including in programs aimed at groups historically under-represented in STEM fields.The project will follow three research thrusts. The first thrust will develop novel theory to elucidate signatures of learning rules and inductive biases in neuronal representations. Experimental techniques allow recording activities of tens or even hundreds of thousands of neurons in the brain. This thrust will help interpret these datasets from a functional point of view. The second thrust will develop a normative theory of biologically plausible learning rules. The investigator's previous work showed that Hebbian learning, despite being local, can implement exact gradient learning on a class of similarity matching cost functions. The project will exploit this finding to design new cost functions for object recognition as manifold disentangling, build corresponding Hebbian neural networks, and compare their learned representations to publicly available neural data from the visual cortex. The last thrust will address learning temporal sequences in recurrent neural networks. It will quantify the temporal sequence learning capabilities of spike-time dependent plasticity. It will study the robustness of neural network trajectories to noise, a key feature of sequential neuronal dynamics in the brain. Finally, the investigator will look for ways of improving sequence learning capacity through nonlinear synaptic interactions.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.

尽管最近取得了许多进展，能够收集有关大脑活动和连接的大规模数据，但我们从这些数据中提取大脑学习方式的能力仍然有限。这种缺陷是由于缺乏一种彻底发展和预测的理论来阐明和建模大脑在神经水平的学习。为了解决这一差距，该项目将开发新的理论框架和数学模型，以帮助制定关于大脑神经网络如何学习的实验可检验的假设。这些框架将解决数据如何在大脑中表示以及如何通过突触可塑性学习这些表示。他们将进一步探索为什么现有的大脑神经网络模型落后于人工神经网络，后者使AI系统能够完成某些任务。该项目的成果将增强我们对大脑功能的理解，并将融入高中、大学、研究生和研究生层面的教育和推广工作，包括针对STEM领域历史上代表性不足的群体的计划。该项目将遵循三个研究方向。第一个重点将发展新的理论，以阐明神经元表征中的学习规则和归纳偏差的签名。实验技术允许记录大脑中数万甚至数十万个神经元的活动。这将有助于从功能的角度解释这些数据集。第二个重点是发展一个生物学上合理的学习规则的规范理论。研究人员以前的工作表明，Hebbian学习尽管是局部的，但可以在一类相似性匹配成本函数上实现精确的梯度学习。该项目将利用这一发现为物体识别设计新的成本函数，作为流形解纠缠，构建相应的Hebbian神经网络，并将其学习的表示与来自视觉皮层的公开神经数据进行比较。最后一个推力将解决递归神经网络中的时间序列学习问题。它将量化尖峰时间依赖可塑性的时间序列学习能力。它将研究神经网络轨迹对噪声的鲁棒性，这是大脑中顺序神经元动力学的一个关键特征。最后，研究人员将寻找通过非线性突触相互作用提高序列学习能力的方法。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。