A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data

Details A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data

2011-07-21

arxiv.org/abs/1107.4228v1

Annals of Applied Statistics 2011, Vol. 5, No. 2B, 1229-1261

Statistics

Applications

Published in at http://dx.doi.org/10.1214/09-AOAS303 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/09-AOAS303

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. In this work 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.

Affiliated with

Also associated with

No associations

Rating

A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-687737