This title appears in the Scientific Report : 2013 

From spiking point-neuron networks to LFPs: a hybrid approach
Dahmen, David (Corresponding author)
Hagen, Espen / Stavrinou, Maria / Lindén, Henrik / Tetzlaff, Tom / van Albada, Sacha / Grün, Sonja / Diesmann, Markus / Einevoll, Gaute T
Theoretical Neuroscience; IAS-6
Computational and Systems Neuroscience; INM-6
2013
Bernstein Conference 2013, Tübingen (Germany), 2013-09-24 - 2013-09-27
Abstract
Supercomputing and Modelling for the Human Brain
Helmholtz Alliance on Systems Biology
Brain-inspired multiscale computation in neuromorphic hybrid systems
Signalling Pathways and Mechanisms in the Nervous System
The local field potential (LFP), the low-pass filtered extracellular potential, is a common measure of neural activity. Cortical LFPs seem to mainly stem from synaptic inputs, but the net LFP signal from several contributing laminar populations is difficult to interpret, as the individual contributions depend on the locations and morphologies of the postsynaptic neurons, the spatial distribution of active synapses, and the level of correlations in synaptic inputs [1]. While most comprehensive cortical-network simulations are done with single-compartment models [2], multicompartmental neuronal modeling is in general required to calculate LFPs [1]. Here we present a hybrid LFP modeling approach where a network of single-compartment LIF neurons generates the spiking activity (Fig. 1A), while detailed multicompartment neuronal models are used to calculate the accompanying LFP (Fig. 1B-C). Our model describes a 1mm2 patch of cat V1 cortex. It accounts for spatially specific pre- to post-synaptic inter- and intra-layer connectivity constrained by experimental observations [3] using reconstructed neuron morphologies of excitatory and inhibitory neurons in layers L2/3-L6 with passive membrane properties. Model specifications of neuron and synapse numbers within populations are taken from [2], while spatial connectivity profiles are based on [3]. Our hybrid framework allows detailed analysis of how the LFP correlates with the network activity and connectivity, and how spatially specific synapse distributions influence the LFP. Spiking network simulations [2] were implemented in NEST (www.nest-initiative.org), while simulations of LFPs from morphologically realistic neurons used LFPy (http://compneuro.umb.no/LFPy) along with NEURON [4]. This work was also presented at CNS 2013 and INCF 2013.