ArticleFrontiers in computational neuroscience2018
Introducing a Comprehensive Framework to Measure Spike-LFP Coupling.
Article in Frontiers in computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Synchronous Rhythmic Activity in Area V4 Can Impair Shape Detection and Neuronal Reliability.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Unraveling the roles of spatial working memory sustained and selective neurons in prefrontal cortex.Communications biology · 2025Article
- Spike-phase coupling of subthalamic neurons to posterior perisylvian cortex predicts speech sound accuracy.Nature communications · 2025Article
- Electrophysiological damage to neuronal membrane alters ephaptic entrainment.Scientific reports · 2023Article
- Review
- Uncovering the organization of neural circuits with Generalized Phase Locking Analysis.PLoS computational biology · 2023Article
- Sensory representation of visual stimuli in the coupling of low-frequency phase to spike times.Brain structure & function · 2022Article
- Ephaptic entrainment in hybrid neuronal model.Scientific reports · 2022Article
- Measuring Synchronization between Spikes and Local Field Potential Based on the Kullback-Leibler Divergence.Computational intelligence and neuroscience · 2021Article
- Adaptation Modulates Spike-Phase Coupling Tuning Curve in the Rat Primary Auditory Cortex.Frontiers in systems neuroscience · 2020Article
- Stimulus-Specific Adaptation Decreases the Coupling of Spikes to LFP Phase.Frontiers in neural circuits · 2019Article
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3 authors.
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Abstract
Measuring the coupling of single neuron's spiking activities to the local field potentials (LFPs) is a method to investigate neuronal synchronization. The most important synchronization measures are phase locking value (PLV), spike field coherence (SFC), pairwise phase consistency (PPC), and spike-triggered correlation matrix synchronization (SCMS). Synchronization is generally quantified using the PLV and SFC. PLV and SFC methods are either biased on the spike rates or the number of trials. To resolve these problems the PPC measure has been introduced. However, there are some shortcomings associated with the PPC measure which is unbiased only for very high spike rates. However evaluating spike-LFP phase coupling (SPC) for short trials or low number of spikes is a challenge in many studies. Lastly, SCMS measures the correlation in terms of phase in regions around the spikes inclusive of the non-spiking events which is the major difference between SCMS and SPC. This study proposes a new framework for predicting a more reliable SPC by modeling and introducing appropriate machine learning algorithms namely least squares, Lasso, and neural networks algorithms where through an initial trend of the spike rates, the ideal SPC is predicted for neurons with low spike rates. Furthermore, comparing the performance of these three algorithms shows that the least squares approach provided the best performance with a correlation of 0.99214 and
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