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A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons.

Beim Graben P, Rodrigues S - Front Comput Neurosci (2013)

Bottom Line: We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid.This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells.In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center for Computational Neuroscience Berlin Berlin, Germany ; Department of German Language and Linguistics, Humboldt-Universität zu Berlin Berlin, Germany.

ABSTRACT
We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

No MeSH data available.


Dynamics of the network and LFP comparisons: the three columns represent different runs of the network for three different rates, 1.2, 1.6, and 2.4 spikes/ms. In each column, all panels show the same 250 ms (extracted from 2 s simulations). The first panels (A–C) represent thalamic inputs with the different rates. The second panels (D–F) corresponds to a raster plot of the activity of 200 pyramidal neurons. The third panels (G–I) depict average instantaneous firing rate (computed on a 1 ms bin) of interneurons (blue) and fourth panels (J–L) correspond to average instantaneous firing rate of pyramidal neurons. The fifth panels (M–O) show the Mazzoni et al. LFP L1 from Equation (45). Finally, the last panels (P–R) depict our proposed LFP measure L4, which is the average of dendritic field potential (DFP) (Equation 48).
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Figure 3: Dynamics of the network and LFP comparisons: the three columns represent different runs of the network for three different rates, 1.2, 1.6, and 2.4 spikes/ms. In each column, all panels show the same 250 ms (extracted from 2 s simulations). The first panels (A–C) represent thalamic inputs with the different rates. The second panels (D–F) corresponds to a raster plot of the activity of 200 pyramidal neurons. The third panels (G–I) depict average instantaneous firing rate (computed on a 1 ms bin) of interneurons (blue) and fourth panels (J–L) correspond to average instantaneous firing rate of pyramidal neurons. The fifth panels (M–O) show the Mazzoni et al. LFP L1 from Equation (45). Finally, the last panels (P–R) depict our proposed LFP measure L4, which is the average of dendritic field potential (DFP) (Equation 48).

Mentions: Following Mazzoni et al. (2008), the network simulations are run for 2 s with three different noise levels, specifically, receiving a constant signal with three different rates 1.2, 1.6, and 2.4 spikes/ms as depicted in Figure 3. Note that these input rates do not mean that a single neuron fires at these high rates. Rather, it can be obtained from multiple neurons that jointly fire with slower, yet desynchronized, rates converging at the same postsynaptic cell. The Poisson process ensures that this is well represented.


A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons.

Beim Graben P, Rodrigues S - Front Comput Neurosci (2013)

Dynamics of the network and LFP comparisons: the three columns represent different runs of the network for three different rates, 1.2, 1.6, and 2.4 spikes/ms. In each column, all panels show the same 250 ms (extracted from 2 s simulations). The first panels (A–C) represent thalamic inputs with the different rates. The second panels (D–F) corresponds to a raster plot of the activity of 200 pyramidal neurons. The third panels (G–I) depict average instantaneous firing rate (computed on a 1 ms bin) of interneurons (blue) and fourth panels (J–L) correspond to average instantaneous firing rate of pyramidal neurons. The fifth panels (M–O) show the Mazzoni et al. LFP L1 from Equation (45). Finally, the last panels (P–R) depict our proposed LFP measure L4, which is the average of dendritic field potential (DFP) (Equation 48).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3539751&req=5

Figure 3: Dynamics of the network and LFP comparisons: the three columns represent different runs of the network for three different rates, 1.2, 1.6, and 2.4 spikes/ms. In each column, all panels show the same 250 ms (extracted from 2 s simulations). The first panels (A–C) represent thalamic inputs with the different rates. The second panels (D–F) corresponds to a raster plot of the activity of 200 pyramidal neurons. The third panels (G–I) depict average instantaneous firing rate (computed on a 1 ms bin) of interneurons (blue) and fourth panels (J–L) correspond to average instantaneous firing rate of pyramidal neurons. The fifth panels (M–O) show the Mazzoni et al. LFP L1 from Equation (45). Finally, the last panels (P–R) depict our proposed LFP measure L4, which is the average of dendritic field potential (DFP) (Equation 48).
Mentions: Following Mazzoni et al. (2008), the network simulations are run for 2 s with three different noise levels, specifically, receiving a constant signal with three different rates 1.2, 1.6, and 2.4 spikes/ms as depicted in Figure 3. Note that these input rates do not mean that a single neuron fires at these high rates. Rather, it can be obtained from multiple neurons that jointly fire with slower, yet desynchronized, rates converging at the same postsynaptic cell. The Poisson process ensures that this is well represented.

Bottom Line: We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid.This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells.In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center for Computational Neuroscience Berlin Berlin, Germany ; Department of German Language and Linguistics, Humboldt-Universität zu Berlin Berlin, Germany.

ABSTRACT
We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the DFP around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. (2008), and conclude that our biophysically motivated approach yields substantial improvement.

No MeSH data available.