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From calcium imaging to spikes, using sequential Monte Carlo methods

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Great technological and experimental advances have recently facilitated the imaging neural activity both in live animals. We describe a sequential Monte Carlo (SMC) expectation maximization algorithm that both infers the posterior distributions of the hidden states, and finds the maximum likelihood estimates of the parameters. Using such an approach enables us to (i) incorporate errorbars on the estimate of the hidden states, (ii) allow for nonlinearities in the observation and transition distributions, and (iii) consider Markov priors governing neural activity. This strategy works in real time for each observable neuron. We show how this method can condition the inferred spike trains on external stimuli, and achieve superresolution, i.e., infer not just whether a spike occurred within a stimulus frame, but when within that frame. Furthermore, our model has a relatively small number of parameters, and each of the parameters may be estimated using standard gradient ascent techniques, without needing “ground truth”. We demonstrate the advantage of this approach over the Wiener and a nonnegative deconvolution filter using data sets containing ground truth.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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