Firing Rate Estimation Using Infinite Mixture Models and Its Application to Neural Decoding

Neural decoding is a framework for reconstructing external stimuli from spike trains recorded in brains. Kloosterman et al. (2014) proposed a new decoding method using marked point processes. This method does not require spike sorting and thereby improves decoding accuracy dramatically. In this method, they used kernel density estimation to estimate intensity functions of marked point processes. However, using kernel density estimation causes problems. To overcome these problems, we propose a new decoding method using infinite mixture models to estimate intensity. The proposed method improves decoding performance in terms of accuracy and computation speed. We apply the proposed method to simulation and experimental data to verify its performance.

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