Data from: Computing the local field potential (LFP) from integrate-and-fire network models
Mazzoni, Alberto, Sant'Anna School of Advanced Studies
Lindén, Henrik Anders, University of Copenhagen
Cuntz, Hermann, Ernst Strüngmann Institute for Neuroscience, Goethe University Frankfurt, Frankfurt Institute for Advanced Studies
Lansner, Anders, Royal Institute of Technology
Panzeri, Stefano, Italian Institute of Technology
Einevoll, Gaute Tomas, Norwegian University of Life Sciences
Lindén, Henrik, University of Copenhagen, Royal Institute of Technology
Published Oct 27, 2016 on Dryad.
https://doi.org/10.5061/dryad.j5r51
Cite this dataset
Mazzoni, Alberto et al. (2016). Data from: Computing the local field potential (LFP) from integrate-and-fire network models [Dataset]. Dryad. https://doi.org/10.5061/dryad.j5r51
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.
Usage notes
Simulated laminar recordings for input intensity 0.5 sp/ms
Simulated 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.out
Simulated laminar recordings for input intensity 1.0 sp/ms
Simulated 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.out
Simulated laminar recordings for input intensity 1.5 sp/ms
Simulated 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.out
Simulated laminar recordings for input intensity 2.0 sp/ms
Simulated 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.out
Simulated laminar recordings for input intensity 2.5 sp/ms
Simulated 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.out
Simulated laminar recordings for input intensity 3.0 sp/ms
Simulated 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.out
Simulated laminar recordings for input intensity 6.0 sp/ms
Simulated 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.out
Simulated laminar recordings for homogeneous synaptic distribution
Simulated 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.out
Simulated laminar recordings for excitatory synapses only in the upper dendritic bush
Simulated 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.out
Simulated laminar recordings for excitatory synapses only in the lower dendritic bush
Simulated 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.out
Simulated laminar recordings for cortical excitatory synapses only in the upper dendritic bush
Simulated 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.out
LIF network output variables for input 1.5 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 neurons
lfp_15.1.out
LIF network output variables for input 0.5 sp/ms
LIF 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 neurons
lfp_5.1.out
LIF network output variables for input 1.0 sp/ms
Outputs 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 neurons
lfp_10.1.out
LIF network output variables for input 2.0 sp/ms
Outputs 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 neurons
lfp_20.1.out
LIF network output variables for input 2.5 sp/ms
Outputs 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 neurons
lfp_25.1.out
LIF network output variables for input 3.0 sp/ms
Outputs 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 neurons
lfp_30.1.out
LIF network output variables for input 6.0 sp/ms
Outputs 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 neurons
lfp_60.1.out
Simulated laminar recordings 125 microns from network center
Simulated 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/ms
electrode_LFP15.out
Simulated laminar recordings 250 microns from network center
Simulated 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.out
Simulated laminar recordings 375 microns from network center
Simulated 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.out
Simulated laminar recordings 500 microns from network center
Simulated 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.out
Simulated laminar recordings 625 microns from network center
Simulated 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.out
Simulated laminar recordings 875 microns from network center
Simulated 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/ms
electrode_LFP15.out
Simulated laminar recordings 1000 microns from network center
Simulated 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.out
Simulated laminar recordings for input intensity 0.5 sp/ms when synaptic model is conductance-based
Simulated 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.out
Simulated laminar recordings for input intensity 1.0 sp/ms when synaptic model is conductance based
Simulated 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.out
Simulated laminar recordings for input intensity 1.5 sp/ms when synaptic model is conductance based
Simulated 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.out
Simulated laminar recordings for input intensity 2.0 sp/ms when synaptic model is conductance based
Simulated 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.out
Simulated laminar recordings for input intensity 2.5 sp/ms when synaptic model is conductance based
Simulated 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_LFP25.out
Simulated laminar recordings for input intensity 3.0 sp/ms when synaptic model is conductance based
Simulated 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.out
Simulated laminar recordings for input intensity 6.0 sp/ms when synaptic model is conductance based
Simulated 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.out
Simulated laminar recordings from reconstructed morphologies for input intensity 0.5 sp/ms
Simulated 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.out
Simulated laminar recordings from reconstructed morphologies for input intensity 1.0 sp/ms
Simulated 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.out
Simulated laminar recordings from reconstructed morphologies for input intensity 1.5 sp/ms
Simulated 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.out
Simulated laminar recordings from reconstructed morphologies for input intensity 2.0 sp/ms
Simulated 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.out
Simulated laminar recordings from reconstructed morphologies for input intensity 2.5 sp/ms
Simulated 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.out
Simulated laminar recordings from reconstructed morphologies for input intensity 3.0 sp/ms
Simulated 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.out
Simulated laminar recordings from reconstructed morphologies for input intensity 6.0 sp/ms
Simulated 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