Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models
By Alberto Mazzoni, Henrik Lindén, Hermann Cuntz, Anders Lansner, Stefano Panzeri, Gaute T. Einevoll. PLoS Comput. Biol., 2015
348 citations recorded by the source corpus.
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.
Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/fa25e5f76d592b15071e508e04b1dbb3d16455bf
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Citations in the corpus
- A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings (2023, unknown): “We defined - source activity as the proxy of the local field potential computed by summing the absolute values of the excitatory and inhibitory currents entering the E population 35 .”
- Rhythms of human attention and memory: An embedded process perspective (2022, background): “…and sustain activity in neural networks based on inhibitory interneuron (ING) or inhibitory as well as excitatory pyramidal-interneuron (PING) GABAergic activity (e.g., Tiesinga and Sejnowski, 2009; Buzsáki and Wang, 2012; for computational models, see, Mazzoni et al., 2015; Keeley et al., 2017).”