Transforming Behavioral Neuroscience Discovery with In-Context Learning and AI-Enhanced Tensor Methods

2026-02-18

AI Summary

Traditional behavioral neuroscience research pipelines are often complex, rigid, and time-consuming, hindering efficient investigation of critical questions such as fear generalization. This work proposes an AI-augmented research pipeline that introduces in-context learning (ICL) as a neuroscientist-friendly interactive interface and integrates a novel AI-enhanced tensor decomposition method to automate preprocessing and pattern discovery in heterogeneous multidimensional neural data—without requiring users to possess AI training expertise. Experimental results demonstrate that the proposed approach significantly outperforms established domain practices and non-ICL machine learning baselines. The identified neural patterns have been validated by domain experts as biologically meaningful, underscoring the method’s potential to accelerate discovery in systems neuroscience.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Scientific discovery pipelines typically involve complex, rigid, and time-consuming processes, from data preparation to analyzing and interpreting findings. Recent advances in AI have the potential to transform such pipelines in a way that domain experts can focus on interpreting and understanding findings, rather than debugging rigid pipelines or manually annotating data. As part of an active collaboration between data science/AI researchers and behavioral neuroscientists, we showcase an example AI-enhanced pipeline, specifically designed to transform and accelerate the way that the domain experts in the team are able to gain insights out of experimental data. The application at hand is in the domain of behavioral neuroscience, studying fear generalization in mice, an important problem whose progress can advance our understanding of clinically significant and often debilitating conditions such as PTSD (Post-Traumatic Stress Disorder). We identify the emerging paradigm of "In-Context Learning" (ICL) as a suitable interface for domain experts to automate parts of their pipeline without the need for or familiarity with AI model training and fine-tuning, and showcase its remarkable efficacy in data preparation and pattern interpretation. Also, we introduce novel AI-enhancements to tensor decomposition model, which allows for more seamless pattern discovery from the heterogeneous data in our application. We thoroughly evaluate our proposed pipeline experimentally, showcasing its superior performance compared to what is standard practice in the domain, as well as against reasonable ML baselines that do not fall under the ICL paradigm, to ensure that we are not compromising performance in our quest for a seamless and easy-to-use interface for domain experts. Finally, we demonstrate effective discovery, with results validated by the domain experts in the team.

Problem

• Behavioral Neuroscience
• Fear Generalization
• Scientific Discovery Pipeline
• PTSD
• Data Interpretation

Innovation

• In-Context Learning
• AI-enhanced tensor decomposition
• behavioral neuroscience
• fear generalization
• scientific discovery pipeline