ArticleJournal of computational neuroscience2015
Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity.
Article in Journal of computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Comparing effective and functional connectivity.Scientific reports · 2026Article
- Nondifferentiable activity in the brain.PNAS nexus · 2024Article
- Inference of network connectivity from temporally binned spike trains.Journal of neuroscience methods · 2024Article
- Bringing Anatomical Information into Neuronal Network Models.Advances in experimental medicine and biology · 2022Article
- A convolutional neural network for estimating synaptic connectivity from spike trains.Scientific reports · 2021Article
- Inferring thalamocortical monosynaptic connectivity in vivo.Journal of neurophysiology · 2021Article
- Estimating Transfer Entropy in Continuous Time Between Neural Spike Trains or Other Event-Based Data.PLoS computational biology · 2021Article
- Inference of synaptic connectivity and external variability in neural microcircuits.Journal of computational neuroscience · 2020Article
- Inferring and validating mechanistic models of neural microcircuits based on spike-train data.Nature communications · 2019Article
- Reconstructing neuronal circuitry from parallel spike trains.Nature communications · 2019Article
- Article
- Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings.PLoS computational biology · 2018Article
- Learning neural connectivity from firing activity: efficient algorithms with provable guarantees on topology.Journal of computational neuroscience · 2018Article
- Computational Neuroscience: Mathematical and Statistical Perspectives.Annual review of statistics and its application · 2018Article
- Functional network stability and average minimal distance - A framework to rapidly assess dynamics of functional network representations.Journal of neuroscience methods · 2018Article
- Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation.Frontiers in neuroinformatics · 2018Article
- Ensemble stacking mitigates biases in inference of synaptic connectivity.Network neuroscience (Cambridge, Mass.) · 2018Article
- On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs.PLoS computational biology · 2017Article
- From point process observations to collective neural dynamics: Nonlinear Hawkes process GLMs, low-dimensional dynamics and coarse graining.Journal of physiology, Paris · 2016Review
- Estimation of Directed Effective Connectivity from fMRI Functional Connectivity Hints at Asymmetries of Cortical Connectome.PLoS computational biology · 2016Article
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3 authors.
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Abstract
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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