Efficient Sampling-Based Bayesian Active Learning for synaptic characterization

TL;DR

This paper introduces ESB-BAL, a sampling-based Bayesian active learning framework that enables real-time estimation of synaptic parameters, improving experimental efficiency and inference precision in neurophysiology.

Contribution

The paper presents a novel sampling-based Bayesian active learning method tailored for real-time biological experiments, overcoming computational limitations of existing BAL approaches.

Findings

Improves parameter estimation precision in synaptic models.

Demonstrates effectiveness on synthetic and real electrophysiological data.

Enables real-time experimental design in neurophysiology.

Abstract

Bayesian Active Learning (BAL) is an efficient framework for learning the parameters of a model, in which input stimuli are selected to maximize the mutual information between the observations and the unknown parameters. However, the applicability of BAL to experiments is limited as it requires performing high-dimensional integrations and optimizations in real time: current methods are either too time consuming, or only applicable to specific models. Here, we propose an Efficient Sampling-Based Bayesian Active Learning (ESB-BAL) framework, which is efficient enough to be used in real-time biological experiments. We apply our method to the problem of estimating the parameters of a chemical synapse from the postsynaptic responses to evoked presynaptic action potentials. Using synthetic data and synaptic whole-cell patch-clamp recordings, we show that our method can improve the precision of model-based inferences, thereby paving the way towards more systematic and efficient experimental designs in physiology.