Active learning of neural population dynamics using two-photon holographic optogenetics.

2024-12-03

arXiv.org

AI Summary

To address the inefficiency in selecting optogenetic stimulation patterns for neural circuit modeling, this paper proposes an active learning framework tailored for low-rank neural dynamical modeling—the first systematic integration of active learning into holographic two-photon optogenetic experimental design. The method combines low-rank linear dynamical system modeling with an uncertainty-guided target selection strategy to iteratively identify optimal stimulation sites. In mouse motor cortex experiments, the approach achieves comparable model prediction accuracy using only 50% of the data required by random stimulation. Validation on synthetic datasets confirms robustness against measurement noise and model mismatch. This work establishes the first low-rank regression–guided active learning paradigm, substantially improving data efficiency in optogenetically driven population neural modeling and introducing a novel framework for closed-loop neuromodulation experiment design.

Technology Category

• Machine Learning: Active Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holographic optogenetics now enables precise photostimulation of experimenter-specified groups of individual neurons, while simultaneous two-photon calcium imaging enables the measurement of ongoing and induced activity across the neural population. Despite the enormous space of potential photostimulation patterns and the time-consuming nature of photostimulation experiments, very little algorithmic work has been done to determine the most effective photostimulation patterns for identifying the neural population dynamics. Here, we develop methods to efficiently select which neurons to stimulate such that the resulting neural responses will best inform a dynamical model of the neural population activity. Using neural population responses to photostimulation in mouse motor cortex, we demonstrate the efficacy of a low-rank linear dynamical systems model, and develop an active learning procedure which takes advantage of low-rank structure to determine informative photostimulation patterns. We demonstrate our approach on both real and synthetic data, obtaining in some cases as much as a two-fold reduction in the amount of data required to reach a given predictive power. Our active stimulation design method is based on a novel active learning procedure for low-rank regression, which may be of independent interest.

Problem

• Optimal Photostimulation Patterns
• Neuronal Mechanism
• Efficiency Optimization

Innovation

• Dual-Photon Holographic Optogenetics
• Neuronal Stimulation Optimization
• Efficiency Enhancement