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10.1371/journal.pcbi.1004584 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1004584 C4682791!4682791!26657024     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+Comput+Biol 2015 ; 11 (12): ä Nephropedia Template TP {{tp|p=26657024|t=2015. Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models |pdf=|usr=}}{{26657024}} gab.com Text Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=26657024 #FOAvip 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. Twit Text FOAVip Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=26657024 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26657024 #FOAvip English Wikipedia <ref>{{Cite pmid|26657024}}</ref> Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models #MMPMID26657024 Mazzoni A; Lindén H; Cuntz H; Lansner A; Panzeri S; Einevoll GT PLoS Comput Biol 2015[Dec]; 11 (12): ä PMID26657024show ga 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. äComputing the Local Field Potential (LFP) from Integrate-and-Fire Netw(epmc).pdf DeepDyve Pubget Overpricing