Recent advances in multi-electrode recording and imaging techniques have made it possible to observe the activity of large populations of neurons. However, to take full advantage of these techniques, new methods for the analysis of population responses must be developed. In this paper, we present an algorithm for optimizing population decoding with distance metrics. To demonstrate the utility of this algorithm under experimental conditions, we evaluate its performance in decoding both population spike trains and calcium signals with different correlation structures. Our results demonstrate that the optimized decoder outperforms other simple population decoders and suggest that optimization could serve as a tool for quantifying the potential contribution of individual cells to the population code.
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