Efficient sampling-based Bayesian Active Learning for synaptic characterization

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.Author summary: Optimizing the design of an experiment is a critical problem in biology. However, most experiments still rely on suboptimal designs, which may not yield sufficient information about the studied system. Consequently, such experiments often require more observations to reach a certain result. An efficient theoretical framework to alleviate this issue is called Optimal Experiment Design (OED), in which experimental protocols are selected to reduce the uncertainty of inferred parameters. However, the applicability of OED methods to actual experiments is limited: they often require computations which are too long for sequential experiments, and do not generalize to different models. Here, we developed a method called Efficient Sampling-Based Bayesian Active Learning (ESB-BAL), and apply it to the problem of estimating the parameters of a chemical synapse from its evoked postsynaptic currents. After each new observation, the optimal next stimulation time can be computed using ESB-BAL. Using recordings in cerebellar brain slices, we show that our method is fast enough to be used in real-time biological experiments and can significantly reduce the uncertainty of inferred parameters. Our method can be readily used by experimentalists via a simple interface. Moreover, our proposed solution is general enough to be applicable to different experimental settings.