CRCNS: Neural Basis of Inductive Bias

CRCNS：归纳偏差的神经基础

• 批准号: 10619184
• 负责人: DAEYEOL LEE
• 金额: $ 43万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-12 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10619184
• 关键词: Algorithms; Animals; Architecture; Artificial Intelligence; Behavior; Behavioral; Biological; Brain; Categories; Choice Behavior; Code; Computer Models; Corpus striatum structure; Data; Data Set; Dorsal; Evolution; Goals; Human; Impairment; Intelligence; Learning; Light; Link; Machine Learning; Measures; Modeling; Monkeys; Neural Network Simulation; Neurobiology; Neuronal Dysfunction; Neurons; Neurosciences; Population; Prefrontal Cortex; Primates; Process; Property; Psychological reinforcement; Recurrence; Research; Schizophrenia; Stimulus; Task Performances; Testing; Training; adaptive learning; artificial neural network; autism spectrum disorder; base; behavior prediction; caudate nucleus; computational neuroscience; design; experience; experimental study; feedforward neural network; flexibility; insight; kernel methods; learned behavior; neural network; neuropsychiatric disorder; nonhuman primate; novel; recurrent neural network; relating to nervous system; statistics; success; theories; translational applications

A hallmark of biological intelligence is the ability to learn from a remarkably small set of experiences to select appropriate behaviors in novel contexts. This ability arises from inductive bias, referring to the assumptions used in generalizing prior observations to novel data. In machine learning, inductive bias is essential for efficient learning from a small number of examples as animals frequently do. In neuroscience, inductive bias has been mostly considered as shaped by evolution to produce an innate inductive bias. Although an agent’s inductive bias should be adaptive to changing task demands, how brains develop inductive bias, use it to guide learning, and adapt it to task statistics, remain poorly understood. The overall goal of this project is to study the neural and computational bases of inductive bias adaptation by training non-human primates on a novel learning task, characterizing choice behavior and neuronal activity, and developing computational models of learning in neural networks. Our theoretical framework of neural kernel learning makes precise predictions for behavior and neural activity that will be experimentally tested in the proposed studies. We will characterize behavior in a newly designed “crosstalk” task, which is designed to characterize inductive bias and to drive a subject’s adaptation through well-defined and variable task statistics (Aim 1). In this task, the subject learns through experience to categorize stimuli composed of multiple features. Within a block of trials, certain features are differentially informative of the category. Next, we will record simultaneous spiking activity from neurons in dorsolateral prefrontal cortex and the dorsal striatum during task performance (Aim 2). In parallel, we will develop and refine algorithmic and artificial neural network models of adaptive learning (Aim 3) to generate testable predictions for behavior and neural activity. Results from these studies will impact paradigms used to study the computational and neural bases of learning and generalization in humans and animals.

生物智能的标志是能够从一小部分经验中学习的能力 在新颖的环境中选择适当的行为。这种能力来自归纳偏见，指的是 用于将先前观察结果概括为新数据的假设。在机器学习中，归纳偏见是 像动物经常这样做的少数例子中有效学习至关重要。 神经科学，归纳偏见主要被认为是由进化而形成的，以产生先天性 归纳BIA。尽管代理人的归纳偏见应适应不断变化的任务需求，如何 大脑会产生归纳偏见，用它来指导学习并将其适应任务统计数据，保持较差 理解齿。该项目的总体目标是研究归纳的神经和计算基础 通过培训非人类素数的偏见适应新的学习任务，表征选择行为 和神经元活动，并在神经元网络中开发学习的计算模型。我们的 神经元学习的理论框架对行为和神经元活动做出了精确的预测 这将在拟议的研究中进行实验测试。我们将在新的 设计的“ Crosstalk”任务旨在表征归纳偏见并推动受试者的偏见 通过定义明确且可变的任务统计数据进行适应（AIM 1）。在此任务中，主题通过 经验，分类由多个功能组成的刺激。在一个试验中，某些功能 对该类别的信息有所不同。接下来，我们将从 在任务表现过程中，在形成前额叶皮层和背纹状体中的神经元（AIM 2）。 相似，我们将开发和完善自适应学习的算法和人工神经网络模型 （目标3）为行为和神经活动产生可测试的预测。这些研究的结果将 影响学习和概括的计算和神经基础的影响范式 人类和动物。