Sunday, May 11, 2025
5/11/2025
CRCNS: Neural Basis of Inductive Bias - 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.
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