A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data. (English) Zbl 1223.62162

Summary: Deducing the structure of neural circuits is one of the central problems of modern neuroscience. Recently-introduced calcium fluorescent imaging methods permit experimentalists to observe network activity in large populations of neurons, but these techniques provide only indirect observations of neural spike trains, with limited time resolution and signal quality. We present a Bayesian approach for inferring neural circuitry given this type of imaging data. We model the network activity in terms of a collection of coupled hidden Markov chains, with each chain corresponding to a single neuron in the network and the coupling between the chains reflecting the network’s connectivity matrix. We derive a Monte Carlo Expectation-Maximization algorithm for fitting the model parameters; to obtain the sufficient statistics in a computationally-efficient manner, we introduce a specialized blockwise-Gibbs algorithm for sampling from the joint activity of all observed neurons given the observed fluorescence data. We perform large-scale simulations of randomly connected neuronal networks with biophysically realistic parameters and find that the proposed methods can accurately infer the connectivity in these networks given reasonable experimental and computational constraints. In addition, the estimation accuracy may be improved significantly by incorporating prior knowledge about the sparseness of connectivity in the network, via standard \(L_{1}\) penalization methods.


62P10 Applications of statistics to biology and medical sciences; meta analysis
92C20 Neural biology
62F15 Bayesian inference
92C55 Biomedical imaging and signal processing
65C05 Monte Carlo methods
65C60 Computational problems in statistics (MSC2010)


Full Text: DOI arXiv


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