ArticleJournal of computational neuroscience2013
Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model.
Article in Journal of computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 35 citations in OpenAlex.
- Spontaneous Dynamics Predict the Effects of Targeted Intervention in Hippocampal Neuronal Cultures.bioRxiv : the preprint server for biology · 2025Article
- 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
- Reconstructing neuronal circuitry from parallel spike trains.Nature communications · 2019Article
- Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings.PLoS computational biology · 2018Article
- Ensemble stacking mitigates biases in inference of synaptic connectivity.Network neuroscience (Cambridge, Mass.) · 2018Article
- Bayesian Estimation of Phase Dynamics Based on Partially Sampled Spikes Generated by Realistic Model Neurons.Frontiers in computational neuroscience · 2017Article
- Effects of local network topology on the functional reconstruction of spiking neural network models.Applied network science · 2017Article
- Estimating Fast Neural Input Using Anatomical and Functional Connectivity.Frontiers in neural circuits · 2016Review
- Multineuronal activity patterns identify selective synaptic connections under realistic experimental constraints.Journal of neurophysiology · 2015Article
- Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity.Journal of computational neuroscience · 2015Article
- A statistical method of identifying interactions in neuron-glia systems based on functional multicell Ca2+ imaging.PLoS computational biology · 2014Article
- Article
- Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging.PloS one · 2014Article
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2 authors at 1 institution in 1 country.
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Abstract
Many mechanisms of neural processing rely critically upon the synaptic connectivity between neurons. As our ability to simultaneously record from large populations of neurons expands, the ability to infer network connectivity from this data has become a major goal of computational neuroscience. To address this issue, we employed several different methods to infer synaptic connections from simulated spike data from a realistic local cortical network model. This approach allowed us to directly compare the accuracy of different methods in predicting synaptic connectivity. We compared the performance of model-free (coherence measure and transfer entropy) and model-based (coupled escape rate model) methods of connectivity inference, applying those methods to the simulated spike data from the model networks with different network topologies. Our results indicate that the accuracy of the inferred connectivity was higher for highly clustered, near regular, or small-world networks, while accuracy was lower for random networks, irrespective of which analysis method was employed. Among the employed methods, the model-based method performed best. This model performed with higher accuracy, was less sensitive to threshold changes, and required less data to make an accurate assessment of connectivity. Given that cortical connectivity tends to be highly clustered, our results outline a powerful analytical tool for inferring local synaptic connectivity from observations of spontaneous activity.
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