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A central goal of neuroscience is to understand how activity in the nervoussystem is related to features of the external world, or to features of thenervous system itself. A common approach is to model neural responses as aweighted combination of external features, or vice versa. The structure of themodel weights can provide insight into neural representations. Often, neuralinput-output relationships are sparse, with only a few inputs contributing tothe output. In part to account for such sparsity, structured regularizers areincorporated into model fitting optimization. However, by imposing priors,structured regularizers can make it difficult to interpret learned modelparameters. Here, we investigate a simple, minimally structured modelestimation method for accurate, unbiased estimation of sparse models based onBootstrapped Adaptive Threshold Selection followed by ordinary least-squaresrefitting (BoATS). Through extensive numerical investigations, we show thatthis method often performs favorably compared to L1 and L2 regularizers. Inparticular, for a variety of model distributions and noise levels, BoATS moreaccurately recovers the parameters of sparse models, leading to moreparsimonious explanations of outputs. Finally, we apply this method to the taskof decoding human speech production from ECoG recordings.

Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation