Journal
JOURNAL OF COMPUTATIONAL NEUROSCIENCE
Volume 39, Issue 1, Pages 77-103Publisher
SPRINGER
DOI: 10.1007/s10827-015-0565-5
Keywords
Spike trains; Network topology; Connectome identification; Inverse problem; Synaptic connectivity; Connectivity inference; Generalized linear model; Maximum likelihood estimation; Penalized likelihood; Sparsity; Point process
Funding
- German Federal Ministry of Education and Research (BMBF) [01GQ0420, 01GQ0830]
- Swiss National Science Foundation [200020_147200]
- Helmholtz Alliance through the Initiative and Networking Fund of the Helmholtz Association
- Helmholtz Portfolio theme Supercomputing and Modeling for the Human Brain
- Swiss National Science Foundation (SNF) [200020_147200] Funding Source: Swiss National Science Foundation (SNF)
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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.
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