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CRCNS: Neural Basis of Inductive Bias

CRCNS：归纳偏差的神经基础

基本信息

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
项目类别：
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

项目摘要

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.

生物智能的一个标志是能够从非常少的经验中学习