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.

生物智力的一个标志是能够从非常小的一组经验中学习 在新奇的环境中选择合适的行为。这种能力源于感应偏向，指的是 将先前的观察结果概括为新数据时使用的假设。在机器学习中，归纳偏差是 像动物经常做的那样，从少量的例子中有效地学习是必不可少的。在……里面 神经科学中，感应偏见大多被认为是由进化形成的，以产生一种天生的 感应偏向。尽管代理的归纳偏向应该适应不断变化的任务需求，但如何 大脑产生归纳偏见，用它来指导学习，并使其适应任务统计，仍然很差 明白了。这个项目的总体目标是研究归纳的神经和计算基础。 通过训练非人类灵长类动物完成新的学习任务、表征选择行为来实现偏见适应 和神经元活动，以及开发神经网络学习的计算模型。我们的 神经核学习的理论框架对行为和神经活动做出精确的预测 这一点将在拟议的研究中进行实验测试。我们将在一个新的 设计了“串扰”任务，旨在表征感应偏差，并驱动受试者的 通过明确和可变的任务统计进行适应(目标1)。在这个任务中，受试者通过 对由多种特征组成的刺激进行分类的经验。在一组试验中，某些特征 是该类别的不同信息。接下来，我们将从 任务执行过程中背外侧前额叶皮质和背侧纹状体的神经元(目标2)。在……里面 同时，我们将开发和改进自适应学习的算法和人工神经网络模型 (目标3)为行为和神经活动生成可测试的预测。这些研究的结果将 用于研究学习和概括的计算和神经基础的Impact范式 人类和动物。