This title appears in the Scientific Report :
2015
Please use the identifier:
http://dx.doi.org/10.1007/s10827-015-0565-5 in citations.
Please use the identifier: http://hdl.handle.net/2128/9349 in citations.
Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity
Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity
Dynamics and function of neuronal networks are determined by their synaptic connectivity. Current experimental methods to analyze synaptic network structure on the cellular level, however, cover only small fractions of functional neuronal circuits, typically without a simultaneous record of neuronal...
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Personal Name(s): | Zaytsev, Yury (Corresponding author) |
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Morrison, Abigail / Deger, Moritz | |
Contributing Institute: |
Jülich Supercomputing Center; JSC Computational and Systems Neuroscience; INM-6 JARA - HPC; JARA-HPC Computational and Systems Neuroscience; IAS-6 |
Published in: | Journal of computational neuroscience, 39 (2015) 1, S. 77 - 103 |
Imprint: |
Dordrecht [u.a.]
Springer Science + Business Media B.V
2015
|
DOI: |
10.1007/s10827-015-0565-5 |
PubMed ID: |
26041729 |
Document Type: |
Journal Article |
Research Program: |
SimLab Neuroscience W2/W3 Professorinnen Programm der Helmholtzgemeinschaft Supercomputing and Modelling for the Human Brain Computational Science and Mathematical Methods |
Link: |
OpenAccess OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/9349 in citations.
Dynamics and function of neuronal networks are determined by their synaptic connectivity. Current experimental methods to analyze synaptic network structure on the cellular level, however, cover only small fractions of functional neuronal circuits, typically without a simultaneous record of neuronal spiking activity. Here we present a method for the reconstruction of large recurrent neuronal networks from thousands of parallel spike train recordings. We employ maximum likelihood estimation of a generalized linear model of the spiking activity in continuous time. For this model the point process likelihood is concave, such that a global optimum of the parameters can be obtained by gradient ascent. Previous methods, including those of the same class, did not allow recurrent networks of that order of magnitude to be reconstructed due to prohibitive computational cost and numerical instabilities. We describe a minimal model that is optimized for large networks and an efficient scheme for its parallelized numerical optimization on generic computing clusters. For a simulated balanced random network of 1000 neurons, synaptic connectivity is recovered with a misclassification error rate of less than 1 % under ideal conditions. We show that the error rate remains low in a series of example cases under progressively less ideal conditions. Finally, we successfully reconstruct the connectivity of a hidden synfire chain that is embedded in a random network, which requires clustering of the network connectivity to reveal the synfire groups. Our results demonstrate how synaptic connectivity could potentially be inferred from large-scale parallel spike train recordings. |