ArticlePloS one2011
Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.
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102 citing papers in PubMed.
- Whole-brain millisecond-scale effective connectivity of auditory and visual naming.NeuroImage · 2026Article
- Toward reliable machine learning models for neural circuit inference: A diagnostic study of CNNs on spike trains.PLoS computational biology · 2026Article
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- Quantifying Correlogram Shape to Analyze Neuronal Firing Dynamics Recorded in TBI-on-a-Chip.Neuroinformatics · 2026Article
- Habit learning is associated with efficiently controlled network dynamics in naive macaque monkeys.Npj complexity · 2026Article
- Jacobian Granger causality for count and binary data with applications to causal network inference.Scientific reports · 2025Article
- Inferring effective networks of spiking neurons using a continuous-time estimator of transfer entropy.PLoS computational biology · 2025Article
- Advances in large-scale electrophysiology with high-density microelectrode arrays.Lab on a chip · 2025Review
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- Spontaneous Dynamics Predict the Effects of Targeted Intervention in Hippocampal Neuronal Cultures.bioRxiv : the preprint server for biology · 2025Article
- Visualization of functional and effective connectivity underlying auditory descriptive naming.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025Article
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- How advocacy groups on Twitter and media coverage can drive US firearm acquisition: A causal study.PNAS nexus · 2025Article
- MINT: A toolbox for the analysis of multivariate neural information coding and transmission.PLoS computational biology · 2025Article
- Application of Transfer Entropy Measure to Characterize Environmental Sounds in Urban and Wild Parks.Sensors (Basel, Switzerland) · 2025Article
- Normative high-frequency oscillation phase-amplitude coupling and effective connectivity under sevoflurane.Brain communications · 2025Article
- Maximum likelihood estimation of spatially dependent interactions in large populations of cortical neurons.Frontiers in computational neuroscience · 2025Article
- Inferring structure of cortical neuronal networks from activity data: A statistical physics approach.PNAS nexus · 2025Article
- Mapping the computational similarity of individual neurons within large-scale ensemble recordings using the SIMNETS analysis framework.Frontiers in neuroscience · 2025Article
- Nanomagnetic Guidance Shapes the Structure-Function Relationship of Developing Cortical Networks.Nano letters · 2024Article
42 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.
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