Deconvolution improves the detection and quantification of spike transmission gain from spike trains.

ABSTRACT: Accurate detection and quantification of spike transmission between neurons is essential for determining neural network mechanisms that govern cognitive functions. Using point process and conductance-based simulations, we found that existing methods for determining neuronal connectivity from spike times are highly affected by burst spiking activity, resulting in over- or underestimation of spike transmission. To improve performance, we developed a mathematical framework for decomposing the cross-correlation between two spike trains. We then devised a deconvolution-based algorithm for removing effects of second-order spike train statistics. Deconvolution removed the effect of burst spiking, improving the estimation of neuronal connectivity yielded by state-of-the-art methods. Application of

SUBMITTER: Spivak L 

PROVIDER: S-EPMC9156687 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature