Understanding the behavioral and neural dynamics of social interactions is a goalof contemporary neuroscience. Many machine learning methods have emergedin recent years to make sense of complex video and neurophysiological data thatresult from these experiments. Less focus has been placed on understanding howanimals process acoustic information, including social vocalizations. A criticalstep to bridge this gap is determining the senders and receivers of acoustic infor-mation in social interactions. While sound source localization (SSL) is a classicproblem in signal processing, existing approaches are limited in their ability tolocalize animal-generated sounds in standard laboratory environments. Advancesin deep learning methods for SSL are likely to help address these limitations,however there are currently no publicly available models, datasets, or benchmarksto systematically evaluate SSL algorithms in the domain of bioacoustics. Here,we present the VCL Benchmark: the first large-scale dataset for benchmarkingSSL algorithms in rodents. We acquired synchronized video and multi-channelaudio recordings of 767,295 sounds with annotated ground truth sources across 9conditions. The dataset provides benchmarks which evaluate SSL performance onreal data, simulated acoustic data, and a mixture of real and simulated data. Weintend for this benchmark to facilitate knowledge transfer between the neuroscienceand acoustic machine learning communities, which have had limited overlap.

Sequential optimal design methods hold great promise for improving the efficiency of neurophysiology experiments. However, previous methods for optimal experimental design have incorporated only weak prior information about the underlying neural system (e.g., the sparseness or smoothness of the receptive field). Here we describe how to use stronger prior information, in the form of parametric models of the receptive field, in order to construct optimal stimuli and further improve the efficiency of our experiments. For example, if we believe that the receptive field is well-approximated by a Gabor function, then our method constructs stimuli that optimally constrain the Gabor parameters (orientation, spatial frequency, etc.) using as few experimental trials as possible. More generally, we may believe a priori that the receptive field lies near a known sub-manifold of the full parameter space; in this case, our method chooses stimuli in order to reduce the uncertainty along the tangent space of this sub-manifold as rapidly as possible. Applications to simulated and real data indicate that these methods may in many cases improve the experimental efficiency.