Sequential estimation of intrinsic activity and synaptic input in single neurons by particle filtering with optimal importance density
This paper deals with the problem of inferring the signals and parameters that cause neural activity to occur. The ultimate challenge being to unveil brain's connectivity, here we focus on a microscopic vision of the problem, where single neurons (potentially connected to a network of peers) ar...
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Main Authors | , |
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Format | Journal Article |
Language | English |
Published |
12.11.2015
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Subjects | |
Online Access | Get full text |
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Summary: | This paper deals with the problem of inferring the signals and parameters
that cause neural activity to occur. The ultimate challenge being to unveil
brain's connectivity, here we focus on a microscopic vision of the problem,
where single neurons (potentially connected to a network of peers) are at the
core of our study. The sole observation available are noisy, sampled voltage
traces obtained from intracellular recordings. We design algorithms and
inference methods using the tools provided by stochastic filtering, that allow
a probabilistic interpretation and treatment of the problem. Using particle
filtering we are able to reconstruct traces of voltages and estimate the time
course of auxiliary variables. By extending the algorithm, through PMCMC
methodology, we are able to estimate hidden physiological parameters as well,
like intrinsic conductances or reversal potentials. Last, but not least, the
method 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. |
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DOI: | 10.48550/arxiv.1511.03895 |