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

尽管最近取得了许多进展，使得收集关于大脑活动和连接的大规模数据成为可能，但我们从这些数据中提取大脑如何学习的原则的能力仍然有限。这一不足是由于缺乏一种彻底发展的预测性理论来在神经水平上解释大脑中的学习并对其进行建模。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可实验验证的假设。这些框架将解决数据在大脑中是如何表示的，以及这些表示是如何通过突触可塑性学习的。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，人工神经网络使人工智能系统能够在某些任务中发挥作用。该项目的成果将增强我们对大脑功能的理解，并将被纳入高中、大学、研究生和研究生层面的教育和推广工作中，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推动力。第一个推力将发展新的理论来解释学习规则和神经元表征中的归纳偏见的签名。实验技术可以记录大脑中数万甚至数十万个神经元的活动。这一推力将有助于从功能的角度解释这些数据集。第二个推动力将发展一种生物学上可信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，但可以在一类相似匹配代价函数上实现精确的梯度学习。该项目将利用这一发现来设计新的代价函数，用于流形分离时的对象识别，构建相应的Hebbian神经网络，并将其学习的表示与来自视觉皮质的公开可用的神经数据进行比较。最后一个重点将解决在递归神经网络中学习时间序列的问题。它将量化尖峰时间相关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稳健性，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。这一奖项反映了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。

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.