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

TL;DR

This work identifies 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 showcases its remarkable efficacy in data preparation and pattern interpretation.

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.

Figures (7)

• Figure 1: Overview of the proposed pipeline. Our pipeline streamlines time-consuming and expertise-intensive steps, easing the workload for scientists, in contrast to conventional workflow.
• Figure 2: Proposed AR-ICL method for behavioral labeling at the $(t)$-th frame.
• Figure 3: Performance of VLM-based calcium activity. F1 score (left axis) and accuracy (right axis).
• Figure 4: AR-ICL prompt used for temporally consistent behavioral labeling. In addition to fixed ICL examples, the model receives the previous second with its predicted label and the next second as unlabeled temporal context.
• Figure 5: Discovery ICL prompt used for AI-driven interpretation of latent neural components. The model receives retrieved neuroscience literature (RAG component), followed by several example latent factors paired with expert interpretations and discovery scores (ICL), and then a new latent component for analysis.