ArticlePLoS computational biology2015
Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers.
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112 citing papers in PubMed.
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- Large-scale neuromorphic modeling of cortical networks on FPGA for investigating anesthetic-induced neural dynamics.Frontiers in systems neuroscience · 2026Article
- Biomimetic model of corticostriatal micro-assemblies discovers a neural code.Nature communications · 2025Article
- Can we infer excitation-inhibition balance from the spectrum of population activity?Communications biology · 2025Article
- Distinct laminar origins of high-gamma and low-frequency ECoG signals revealed by optogenetics.bioRxiv : the preprint server for biology · 2025Article
- Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed.bioRxiv : the preprint server for biology · 2025Article
- Enhancing deep neural networks through complex-valued representations and Kuramoto synchronization dynamics.ArXiv · 2025Article
- A Python toolbox for neural circuit parameter inference.NPJ systems biology and applications · 2025Article
52 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.
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