Boelts, J.

Harth, P.

Hege, H.

Macke, J.

Oberlaender, M.

Yanez, F.

English

MPG.PuRe

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/348550447Bayesian inference for synaptic connectivity rules in anatomically realisticcortical connectomesPoster · January 2019DOI: 10.12751/nncn.bc2019.0079CITATIONS0READS36 authors, including:Some of the authors of this publication are also working on these related projects:Feature extraction View projectThe origin of pathological phase-amplitude coupling in the subthalamic nucleus View projectJan BöltsTechnische Universität München11 PUBLICATIONS   5 CITATIONS   SEE PROFILEPhilipp HarthZuse-Institut Berlin3 PUBLICATIONS   0 CITATIONS   SEE PROFILEHans-Christian HegeZuse-Institut Berlin339 PUBLICATIONS   7,619 CITATIONS   SEE PROFILEMarcel OberlaenderCenter of Advanced European Studies and Research59 PUBLICATIONS   1,300 CITATIONS   SEE PROFILEAll content following this page was uploaded by Philipp Harth on 16 January 2021.The user has requested enhancement of the downloaded file.1/16/2021 Bernstein Conference 2019https://abstracts.g-node.org/abstracts/41e82db4-e68d-441c-a26e-fd079624552f 1/2Bayesian inference for synaptic connectivityrules in anatomically realistic corticalconnectomesJan Boelts , Philipp Harth , Felipe Yanez , Hans-Christian Hege ,Marcel Oberlaender , Jakob H. Macke 1. Department of Electrical and Computer Engineering, TU Munich, Munich, Germany2. Department of Visual Data Analysis, Zuse Institute Berlin, Berlin, Germany3. Max Planck Group: In Silico Brain Sciences, Center of Advanced European Studies andResearch, Bonn, GermanyA key goal in connectomics research is to discover local synaptic connectivity rules which canexplain the global organization of cortical circuits. Identifying and selecting such rules remains achallenging problem, as even advanced experimental techniques can only provide partialmeasurements of neural circuits.We approach identi cation and selection of synaptic connectivity rules as a Bayesian inferenceproblem: Given partial measurements of structural features at di erent scales, we use Bayesianinference to identify which parameters of synaptic connectivity models are quantitativelyconsistent with experimental observations.One challenge of applying these approaches is the fact that the underlying models might not betractable with conventional Bayesian approaches: In particular, when models are de ned onlyindirectly through a complex simulator, or are constrained by aggregate data, it is oftenimpossible to compute the likelihood  of the data  given model parameters . In contrastto classical likelihood-free approaches, recent advances based on conditional density estimation[1, 2, 3] can scale to high-dimensional observations. We use automatic posterior transformation(APT) [3] to estimate the posterior distribution of the parameters of hypothesized synapticconnectivity rules, given observed connectivity measurements.We illustrate the approach on a dense structural model of the rat barrel cortex. This model isbased on anatomical measurements of geometry, soma distributions, and morphologies [4]. Weperform inference on a synaptic connectivity rule that predicts the presence of synapses basedon pre- and postsynaptic target densities. In the tractable case of a small sub-volume with fullyobserved synapse counts the posterior is estimated accurately. When using only empiricallymeasured connection probabilities the resulting posterior successfully reveals the underlyingrule parameters and how they are constrained by the observed data.By scaling this inference to larger sub-volumes and by enabling the comparison of hypothesizedsynaptic connectivity rules, we will ultimately provide tools to relate structural features ofneuronal networks to the underlying synaptic organization.AcknowledgementsThis work was supported by the German Research Foundation (DFG) through SPP 2041Computational Connectomics and the German Federal Ministry of Education and Research(BMBF, project ‘ADMIMEM’, FKZ 01IS18052 A-D).1 2 1, 3 23 1p(x|θ) x θ1/16/2021 Bernstein Conference 2019https://abstracts.g-node.org/abstracts/41e82db4-e68d-441c-a26e-fd079624552f 2/2References1. Papamakarios, G., & Murray, I. (2016). Fast ε-free inference of simulation models withBayesian conditional density estimation. In Advances in Neural Information ProcessingSystems (pp. 1028-1036). (http://papers.nips.cc/paper/6084-fast-free-inference-of-simulation-models-with-bayesian-conditional-density-estimation),10.1080/03610918.2017.1401082 (http://dx.doi.org/10.1080/03610918.2017.1401082)2. Lueckmann, J. M., Goncalves, P. J., Bassetto, G., Öcal, K., Nonnenmacher, M., & Macke, J. H.(2017). Flexible statistical inference for mechanistic models of neural dynamics. InAdvances in Neural Information Processing Systems (pp. 1289-1299).(http://papers.nips.cc/paper/6728- exible-statistical-inference-for-mechanistic-models-of-neural-dynamics), 10.3389/fnins.2014.00010 (http://dx.doi.org/10.3389/fnins.2014.00010)3. Greenberg, D. S., Nonnenmacher, M., & Macke, J. H. (2019). Automatic PosteriorTransformation for Likelihood-Free Inference. arXiv preprint arXiv:1905.07488.(https://arxiv.org/abs/1905.07488), 10.1109/MCSE.2011.37(http://dx.doi.org/10.1109/MCSE.2011.37)4. Egger, R., Dercksen, V. J., Udvary, D., Hege, H. C., & Oberlaender, M. (2014). Generation ofdense statistical connectomes from sparse morphological data. Frontiers inneuroanatomy, 8, 129. (https://www.frontiersin.org/article/10.3389/fnana.2014.00129),10.3389/fnana.2014.00129 (http://dx.doi.org/10.3389/fnana.2014.00129)Copyright: © (2019) Boelts J, Harth P, Yanez F, Hege H, Oberlaender M, Macke JHCitation: Boelts J, Harth P, Yanez F, Hege H, Oberlaender M, Macke JH (2019) Bayesian inference forsynaptic connectivity rules in anatomically realistic cortical connectomes. Bernstein Conference2019. doi: 10.12751/nncn.bc2019.0079 (http://doi.org/10.12751/nncn.bc2019.0079)View publication stats

Bayesian inference for synaptic connectivity rules in anatomically realistic cortical connectomes

http://hdl.handle.net/21.11116/0000-0007-CDF9-A

Bayesian inference for synaptic connectivity rules in anatomically realistic cortical connectomes

Abstract

Similar works

Full text

Available Versions

MPG.PuRe