Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation
This provides a more interpretable method for neuroscientists analyzing sparse neural data, though it is incremental as it builds on existing regularization techniques.
The paper tackles the problem of biased parameter estimation in sparse neural models by introducing Bootstrapped Adaptive Threshold Selection (BoATS), which more accurately recovers sparse parameters compared to L1 and L2 regularizers across various distributions and noise levels, as demonstrated in decoding human speech from ECoG recordings.
A central goal of neuroscience is to understand how activity in the nervous system is related to features of the external world, or to features of the nervous system itself. A common approach is to model neural responses as a weighted combination of external features, or vice versa. The structure of the model weights can provide insight into neural representations. Often, neural input-output relationships are sparse, with only a few inputs contributing to the output. In part to account for such sparsity, structured regularizers are incorporated into model fitting optimization. However, by imposing priors, structured regularizers can make it difficult to interpret learned model parameters. Here, we investigate a simple, minimally structured model estimation method for accurate, unbiased estimation of sparse models based on Bootstrapped Adaptive Threshold Selection followed by ordinary least-squares refitting (BoATS). Through extensive numerical investigations, we show that this method often performs favorably compared to L1 and L2 regularizers. In particular, for a variety of model distributions and noise levels, BoATS more accurately recovers the parameters of sparse models, leading to more parsimonious explanations of outputs. Finally, we apply this method to the task of decoding human speech production from ECoG recordings.