  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.
