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Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity.

Zaytsev YV, Morrison A, Deger M - J Comput Neurosci (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center, Jülich, Germany, yury@zaytsev.net.

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.

No MeSH data available.


Synfire dynamics of the original and reconstructed networks. The first links in the synfire chains in both original and reconstructed network were stimulated (by injection of a strong input current) at 250 ms and 750 ms. The stimulation was performed using a large current pulse injection into all neurons of the first link. a Original network. b Reconstructed network
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Fig10: Synfire dynamics of the original and reconstructed networks. The first links in the synfire chains in both original and reconstructed network were stimulated (by injection of a strong input current) at 250 ms and 750 ms. The stimulation was performed using a large current pulse injection into all neurons of the first link. a Original network. b Reconstructed network

Mentions: The results of this experiment are displayed in Fig. 10. We used the order of the neuron identifiers in which the cells were originally wired up to permit a clear visualization of the activity. The raster plots show that the dynamics of the spike patterns of ground truth and estimated network are very similar. Generally, an estimate of a GLM based on recorded spike trains is a generative model of the data, in the sense that, if itself simulated, will produce similar data; Fig. 10 demonstrates this using our embedded synfire chain example.Fig. 10


Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity.

Zaytsev YV, Morrison A, Deger M - J Comput Neurosci (2015)

Synfire dynamics of the original and reconstructed networks. The first links in the synfire chains in both original and reconstructed network were stimulated (by injection of a strong input current) at 250 ms and 750 ms. The stimulation was performed using a large current pulse injection into all neurons of the first link. a Original network. b Reconstructed network
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Related In: Results  -  Collection

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Fig10: Synfire dynamics of the original and reconstructed networks. The first links in the synfire chains in both original and reconstructed network were stimulated (by injection of a strong input current) at 250 ms and 750 ms. The stimulation was performed using a large current pulse injection into all neurons of the first link. a Original network. b Reconstructed network
Mentions: The results of this experiment are displayed in Fig. 10. We used the order of the neuron identifiers in which the cells were originally wired up to permit a clear visualization of the activity. The raster plots show that the dynamics of the spike patterns of ground truth and estimated network are very similar. Generally, an estimate of a GLM based on recorded spike trains is a generative model of the data, in the sense that, if itself simulated, will produce similar data; Fig. 10 demonstrates this using our embedded synfire chain example.Fig. 10

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Simulation Laboratory Neuroscience - Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center, Jülich, Germany, yury@zaytsev.net.

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.

No MeSH data available.