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This paper deals with the problem of inferring the signals and parametersthat cause neural activity to occur. The ultimate challenge being to unveilbrain's connectivity, here we focus on a microscopic vision of the problem,where single neurons (potentially connected to a network of peers) are at thecore of our study. The sole observation available are noisy, sampled voltagetraces obtained from intracellular recordings. We design algorithms andinference methods using the tools provided by stochastic filtering, that allowa probabilistic interpretation and treatment of the problem. Using particlefiltering we are able to reconstruct traces of voltages and estimate the timecourse of auxiliary variables. By extending the algorithm, through PMCMCmethodology, we are able to estimate hidden physiological parameters as well,like intrinsic conductances or reversal potentials. Last, but not least, themethod is applied to estimate synaptic conductances arriving at a target cell,thus reconstructing the synaptic excitatory/inhibitory input traces. Notably,these estimations have a bound-achieving performance even in spiking regimes.

Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density