Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models

PLoS Comput Biol. 2015 Dec 14;11(12):e1004584. doi: 10.1371/journal.pcbi.1004584. eCollection 2015 Dec.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Action Potentials / physiology*
  • Brain / physiology*
  • Brain Mapping / methods*
  • Computer Simulation
  • Electromagnetic Fields
  • Humans
  • Membrane Potentials / physiology
  • Models, Neurological*
  • Nerve Net / physiology*
  • Neurons / physiology*
  • Synaptic Transmission / physiology

Grant support

AM was supported by the NEBIAS European project (EUFP7-ICT-611687), by the PRIN/HandBot Italian project (CUP: B81J12002680008; prot.: 20102YF2RY), by the SI-CODE Project of the FP7 of the European Commission (FET-Open, Grant FP7-284553) and by the Italian Ministry of Foreign Affairs and International Cooperation, Directorate General for Country Promotion (Economy, Culture and Science)—Unit for Scientific and Technological Cooperation, via the Italy-Sweden bilateral research project on "Brain network mechanisms for integration of natural tactile input patterns". HL was supported by The Danish Council for Independent Research and FP7 Marie Curie Actions – COFUND (grant id: DFF – 1330-00226), EU grant 269921 (BrainScaleS) and The Dynamical Systems Interdisciplinary Network, University of Copenhagen. HC was supported by BMBF grant 01GQ1406 (Bernstein Award 2013). SP was supported by the SI-CODE Project of the FP7 of the European Commission (FET-Open, Grant FP7-284553), and the Autonomous Province of Trento (“Grandi Progetti 2012,” the “Characterizing and Improving Brain Mechanisms of Attention–ATTEND” project). GTE was supported by the Research Council of Norway (ISP-Physics), EU grants 269921 (BrainScaleS) and 604102 (Human Brain Project). Computer time on supercomputer Stallo was provided by Research Council of Norway (Notur). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.