DOI: 10.5061/dryad.j5r51
title: Data from: Computing the local field potential (LFP) from integrate-and-fire network models

description: 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.
description: Simulated laminar
 recordings for input intensity 0.5 sp/msSimulated recordings
 (10101 ms) of the LFP generated by the 3D network described in the paper,
 computed from LFPy. Each row is a different depth. Input intensity is
 reported in the title.electrode_LFP5.outSimulated laminar recordings for input intensity 1.0 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP10.outSimulated laminar recordings for input intensity 1.5 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP15.outSimulated laminar recordings for input intensity 2.0 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP20.outSimulated laminar recordings for input intensity 2.5 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP25.outSimulated laminar recordings for input intensity 3.0 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP30.outSimulated laminar recordings for input intensity 6.0 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP60.outSimulated laminar recordings for homogeneous synaptic distributionSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper when both inhibitory and excitatory synapses are distributed homogeneously over the whole neuron surface, computed from LFPy. Each row is a different depth. Input intensity is 1.5 sp/ms.homelectrode_LFP15.outSimulated laminar recordings for excitatory synapses only in the upper dendritic bushSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper when excitatory synapses located only in the upper dendritic bush, computed from LFPy. Each row is a different depth. Input intensity is 1.5 sp/ms.excupelectrode_LFP15.outSimulated laminar recordings for excitatory synapses only in the lower dendritic bushSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper when excitatory synapses are located only in the lower dendritic bush, computed from LFPy. Each row is a different depth. Input intensity is 1.5 sp/ms.excdownelectrode_LFP15.outSimulated laminar recordings for cortical excitatory synapses only in the upper dendritic bushSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper when cortical excitatory synapses are located only in the upper bush and thalamic excitatory synapses are distributed homogeneneously over the whole neuron surface, computed from LFPy. Each row is a different depth. Input intensity is 1.5 sp/ms.cortupelectrode_LFP15.outLIF network output variables for input 1.5 sp/msOutputs of Leaky Integrate and Fire Network described in the paper for input intensity 1.5 sp/ms. Simulation lasts for 10100 ms sampled at 20kHz. Columns are 1) Membrane potential 2-5) local AMPA, thalamic AMPA, long range cortical AMPA, local GABA over excitatory neurons, 6-9) same as 2-5) but computed over inhibitory neurons, 10) input firing rate, 11-12) firing rate of inhibitory neurons and excitatory neuronslfp_15.1.outLIF network output variables for input 0.5 sp/msLIF network output variables for input 0.5 sp/ms Outputs of Leaky Integrate and Fire Network described in the paper for input intensity 1.5 sp/ms. Simulation lasts for 10100 ms sampled at 20kHz. Columns are 1) Membrane potential 2-5) local AMPA, thalamic AMPA, long range cortical AMPA, local GABA over excitatory neurons, 6-9) same as 2-5) but computed over inhibitory neurons, 10) input firing rate, 11-12) firing rate of inhibitory neurons and excitatory neuronslfp_5.1.outLIF network output variables for input 1.0 sp/msOutputs of Leaky Integrate and Fire Network described in the paper for input intensity 1.0 sp/ms. Simulation lasts for 10100 ms sampled at 20kHz. Columns are 1) Membrane potential 2-5) local AMPA, thalamic AMPA, long range cortical AMPA, local GABA over excitatory neurons, 6-9) same as 2-5) but computed over inhibitory neurons, 10) input firing rate, 11-12) firing rate of inhibitory neurons and excitatory neuronslfp_10.1.outLIF network output variables for input 2.0 sp/msOutputs of Leaky Integrate and Fire Network described in the paper for input intensity 2.0 sp/ms. Simulation lasts for 10100 ms sampled at 20kHz. Columns are 1) Membrane potential 2-5) local AMPA, thalamic AMPA, long range cortical AMPA, local GABA over excitatory neurons, 6-9) same as 2-5) but computed over inhibitory neurons, 10) input firing rate, 11-12) firing rate of inhibitory neurons and excitatory neuronslfp_20.1.outLIF network output variables for input 2.5 sp/msOutputs of Leaky Integrate and Fire Network described in the paper for input intensity 2.5 sp/ms. Simulation lasts for 10100 ms sampled at 20kHz. Columns are 1) Membrane potential 2-5) local AMPA, thalamic AMPA, long range cortical AMPA, local GABA over excitatory neurons, 6-9) same as 2-5) but computed over inhibitory neurons, 10) input firing rate, 11-12) firing rate of inhibitory neurons and excitatory neuronslfp_25.1.outLIF network output variables for input 3.0 sp/msOutputs of Leaky Integrate and Fire Network described in the paper for input intensity 3.0 sp/ms. Simulation lasts for 10100 ms sampled at 20kHz. Columns are 1) Membrane potential 2-5) local AMPA, thalamic AMPA, long range cortical AMPA, local GABA over excitatory neurons, 6-9) same as 2-5) but computed over inhibitory neurons, 10) input firing rate, 11-12) firing rate of inhibitory neurons and excitatory neuronslfp_30.1.outLIF network output variables for input 6.0 sp/msOutputs of Leaky Integrate and Fire Network described in the paper for input intensity 6.0 sp/ms. Simulation lasts for 10100 ms sampled at 20kHz. Columns are 1) Membrane potential 2-5) local AMPA, thalamic AMPA, long range cortical AMPA, local GABA over excitatory neurons, 6-9) same as 2-5) but computed over inhibitory neurons, 10) input firing rate, 11-12) firing rate of inhibitory neurons and excitatory neuronslfp_60.1.outSimulated laminar recordings 125 microns from network centerSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Virtual electrode is located 125 microns away from network center. Each row is a different depth. Input intensity is 1.5 sp/mselectrode_LFP15.outSimulated laminar recordings 250 microns from network centerSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Virtual electrode is located 250 microns away from network center. Each row is a different depth. Input intensity is 1.5 sp/ms.electrode_LFP15.outSimulated laminar recordings 375 microns from network centerSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Virtual electrode is located 375 microns away from network center. Each row is a different depth. Input intensity is 1.5 sp/ms.electrode_LFP15.outSimulated laminar recordings 500 microns from network centerSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Virtual electrode is located 500 microns away from network center. Each row is a different depth. Input intensity is 1.5 sp/ms.electrode_LFP15.outSimulated laminar recordings 625 microns from network centerSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Virtual electrode is located 625 microns away from network center. Each row is a different depth. Input intensity is 1.5 sp/ms.electrode_LFP15.outSimulated laminar recordings 875 microns from network centerSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Virtual electrode is located 875 microns away from network center. Each row is a different depth. Input intensity is 1.5 sp/mselectrode_LFP15.outSimulated laminar recordings 1000 microns from network centerSimulated recordings (10101 ms) of the LFP generated by the 3D network described in the paper, computed from LFPy. Virtual electrode is located 1000 microns away from network center. Each row is a different depth. Input intensity is 1.5 sp/ms.electrode_LFP15.outSimulated laminar recordings for input intensity 0.5 sp/ms when synaptic model is conductance-basedSimulated recordings (10101 ms) of the LFP generated by the 3D network with conductance-based synapses described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP5.outSimulated laminar recordings for input intensity 1.0 sp/ms when synaptic model is conductance basedSimulated recordings (10101 ms) of the LFP generated by the 3D network with conductance-based synapses described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP10.outSimulated laminar recordings for input intensity 1.5 sp/ms when synaptic model is conductance basedSimulated recordings (10101 ms) of the LFP generated by the 3D network with conductance-based synapses described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP15.outSimulated laminar recordings for input intensity 2.0 sp/ms when synaptic model is conductance basedSimulated recordings (10101 ms) of the LFP generated by the 3D network with conductance-based synapses described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP20.outSimulated laminar recordings for input intensity 2.5 sp/ms when synaptic model is conductance basedSimulated recordings (10101 ms) of the LFP generated by the 3D network with conductance-based synapses described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the titleelectrode_LFP25.outSimulated laminar recordings for input intensity 3.0 sp/ms when synaptic model is conductance basedSimulated recordings (10101 ms) of the LFP generated by the 3D network with conductance-based synapses described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP30.outSimulated laminar recordings for input intensity 6.0 sp/ms when synaptic model is conductance basedSimulated recordings (10101 ms) of the LFP generated by the 3D network with conductance-based synapses described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP60.outSimulated laminar recordings from reconstructed morphologies for input intensity 0.5 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network with reconstructed (rather than artificial) morphologies described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP5.outSimulated laminar recordings from reconstructed morphologies for input intensity 1.0 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network with reconstructed (rather than artificial) morphologies described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP10.outSimulated laminar recordings from reconstructed morphologies for input intensity 1.5 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network with reconstructed (rather than artificial) morphologies described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP15.outSimulated laminar recordings from reconstructed morphologies for input intensity 2.0 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network with reconstructed (rather than artificial) morphologies described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP20.outSimulated laminar recordings from reconstructed morphologies for input intensity 2.5 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network with reconstructed (rather than artificial) morphologies described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP25.outSimulated laminar recordings from reconstructed morphologies for input intensity 3.0 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network with reconstructed (rather than artificial) morphologies described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP30.outSimulated laminar recordings from reconstructed morphologies for input intensity 6.0 sp/msSimulated recordings (10101 ms) of the LFP generated by the 3D network with reconstructed (rather than artificial) morphologies described in the paper, computed from LFPy. Each row is a different depth. Input intensity is reported in the title.electrode_LFP60.out