SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity

Luca Ambrogioni, Patrick Ebel, Max Hinne, Umut Güçlü, Marcel Gerven, Eric Maris
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:787-795, 2019.

Abstract

In this paper we introduce a semi-analytic variational framework for approximating the posterior of a Gaussian processes coupled through non-linear emission models. While the semi-analytic method can be applied to a large class of models, the present paper is devoted to the analysis of causal connectivity between biological spiking neurons. Estimating causal connectivity between spiking neurons from measured spike sequences is one of the main challenges of systems neuroscience. This semi-analytic method exploits the tractability of GP regression when the membrane potential is observed. The resulting posterior is then marginalized analytically in order to obtain the posterior of the response functions given the spike sequences alone. We validate our methods on both simulated data and real neuronal recordings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v89-ambrogioni19b, title = {SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity}, author = {Ambrogioni, Luca and Ebel, Patrick and Hinne, Max and G\"{u}\c{c}l\"{u}, Umut and van Gerven, Marcel and Maris, Eric}, booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics}, pages = {787--795}, year = {2019}, editor = {Chaudhuri, Kamalika and Sugiyama, Masashi}, volume = {89}, series = {Proceedings of Machine Learning Research}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v89/ambrogioni19b/ambrogioni19b.pdf}, url = {https://proceedings.mlr.press/v89/ambrogioni19b.html}, abstract = {In this paper we introduce a semi-analytic variational framework for approximating the posterior of a Gaussian processes coupled through non-linear emission models. While the semi-analytic method can be applied to a large class of models, the present paper is devoted to the analysis of causal connectivity between biological spiking neurons. Estimating causal connectivity between spiking neurons from measured spike sequences is one of the main challenges of systems neuroscience. This semi-analytic method exploits the tractability of GP regression when the membrane potential is observed. The resulting posterior is then marginalized analytically in order to obtain the posterior of the response functions given the spike sequences alone. We validate our methods on both simulated data and real neuronal recordings.} }
Endnote
%0 Conference Paper %T SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity %A Luca Ambrogioni %A Patrick Ebel %A Max Hinne %A Umut Güçlü %A Marcel Gerven %A Eric Maris %B Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Masashi Sugiyama %F pmlr-v89-ambrogioni19b %I PMLR %P 787--795 %U https://proceedings.mlr.press/v89/ambrogioni19b.html %V 89 %X In this paper we introduce a semi-analytic variational framework for approximating the posterior of a Gaussian processes coupled through non-linear emission models. While the semi-analytic method can be applied to a large class of models, the present paper is devoted to the analysis of causal connectivity between biological spiking neurons. Estimating causal connectivity between spiking neurons from measured spike sequences is one of the main challenges of systems neuroscience. This semi-analytic method exploits the tractability of GP regression when the membrane potential is observed. The resulting posterior is then marginalized analytically in order to obtain the posterior of the response functions given the spike sequences alone. We validate our methods on both simulated data and real neuronal recordings.
APA
Ambrogioni, L., Ebel, P., Hinne, M., Güçlü, U., Gerven, M. & Maris, E.. (2019). SpikeCaKe: Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity. Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 89:787-795 Available from https://proceedings.mlr.press/v89/ambrogioni19b.html.

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