This title appears in the Scientific Report : 2015 

Hybrid scheme for modeling local field potentials from point-neuron networks
Hagen, Espen (Corresponding author)
Dahmen, David / Stavrinou, Maria L. / Lindén, Henrik / Tetzlaff, Tom / van Albada, Sacha / Diesmann, Markus / Grün, Sonja / Einevoll, Gaute T.
Computational and Systems Neuroscience; INM-6
Theoretical Neuroscience; IAS-6
The 11th Göttingen meeting of the German Neuroscience Society, Göttingen (Germany), 2015-03-18 - 2015-03-21
The Human Brain Project
Supercomputing and Modelling for the Human Brain
Theory, modelling and simulation
Please use the identifier: in citations.
While recordings of extracellular potentials in neural tissue are commonly used for monitoring neural activity, interpretation of the low frequency part, the local field potential (LFP), remains ambiguous in terms of the underlying network activity. Studies have shown that the LFP depends on electrode position, extracellular volume conductor model, neuronal morphology, synapse distributions and synaptic input correlations [1,2,3]. In order to relate spiking dynamics in point-neuron neuron network models to extracellular signals, e.g., the LFP, we have developed a hybrid scheme that uses the spiking activity (Fig. panel A) generated by a network of single-compartment leaky integrate-and-fire model neurons (implemented in NEST [4]). The network provides synaptic input to populations of detailed multi- compartmental neuron models (Fig. panel B) which are used to compute the spatiotemporal LFP pattern (Fig. panel C) based on biophysical principles behind extracellular electric signals using LFPy ( [5] and NEURON [6]. The hybrid scheme is incorporated in a new, publicly available Python package named hybridLFPy ( here demonstrate an application of the method with the network model of [7] describing the localmicrocircuitry under 1 mm2 surface of cat primary visual cortex. The point-neuron network includes ~77000 cells in total distributed across four layers, each composed of one excitatory and one inhibitory population representing layers 2/3 through 6, activated by external input (cortico-cortical, thalamo- cortical). For the LFP model, the same amount of neurons, subdivided into 16 cell types with passive membrane properties are used with cell-type and layer-specific connectivity derived from the point- neuron network description and additional anatomical data [8]. Our results show that both spontaneous and stimulus-evoked LFPs depend critically on the level of synchrony in the underlying network state. Besides, we show that full-scale simulations, i.e., simulations including all cells in the network, are required to address the effect of network correlations on the LFP. Furthermore, the hybrid scheme can be used to develop and verify simplified models for LFP generation from point-neuron network models. Given the widespread use of point-neuron network models and the previous lack of tractable methods to associate their activity to easy-to-measure signals (e.g., LFPs), the present method is a step toward gaining important insight into the link between experimental measurements and the underlying network activity. References:1. Lindén et al. (2011). Neuron. 72:859-872.2. Einevoll et al. (2013). Nat Rev Neurosci. 14:770-785.3. Tomsett et al. (2014). Brain Struct Funct. :1-214. Gewaltig and Diesmann (2007). Scholarpedia. 2(4):1430. 5. Lindén et al. (2014). Front Neuroinformatics. 7:41.6. Hines et al. (2009). Front Neuroinformatics. 3:1-12.7. Potjans and Diesmann (2012). Cereb Cortex. 24:785-806. 8. Binzegger et al. (2004). J Neurosci. 24:8441-8453.