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.

Automated summary

Bayesian Active Learning (BAL) is an efficient framework for learning model parameters by maximizing the mutual information between observations and unknown parameters. However, its applicability to real-time experiments is limited due to high-dimensional integrations and optimizations. To address this issue, we propose an Efficient Sampling-Based Bayesian Active Learning (ESB-BAL) framework that can be used in real-time biological experiments. We demonstrate the effectiveness of our method in improving the precision of model-based inferences for estimating chemical synapse parameters, paving the way for more systematic and efficient experimental designs in physiology.