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  • 1. Denker, M
    et al.
    Roux, S
    Lindén, Henrik
    Norwegian Univ Life Sci, Dept Math Sci & Technol.
    Diesmann, M
    Riehle, A
    Grün, S
    The local field potential reflects surplus spike synchrony2011In: Cerebral Cortex, ISSN 1047-3211, E-ISSN 1460-2199, ISSN 1047-3211, Vol. 21, no 12, p. 2681-2695Article in journal (Refereed)
    Abstract [en]

     While oscillations of the local field potential (LFP) are commonly attributed to the synchronization of neuronal firing rate on the same time scale, their relationship to coincident spiking in the millisecond range is unknown. Here, we present experimental evidence to reconcile the notions of synchrony at the level of spiking and at the mesoscopic scale. We demonstrate that only in time intervals of significant spike synchrony that cannot be explained on the basis of firing rates, coincident spikes are better phase locked to the LFP than predicted by the locking of the individual spikes. This effect is enhanced in periods of large LFP amplitudes. A quantitative model explains the LFP dynamics by the orchestrated spiking activity in neuronal groups that contribute the observed surplus synchrony. From the correlation analysis, we infer that neurons participate in different constellations but contribute only a fraction of their spikes to temporally precise spike configurations. This finding provides direct evidence for the hypothesized relation that precise spike synchrony constitutes a major temporally and spatially organized component of the LFP.

  • 2. Etholm, L
    et al.
    Lindén, Henrik
    Norwegian Univ Life Sci, Dept Math Sci & Technol.
    Eken, T
    Heggelund, P
    Electroencephalographic characterization of seizure activity in the Synapsin I/II double knockout mouse2011In: Brain Research, ISSN 0006-8993, E-ISSN 1872-6240, Vol. 1383, p. 270-288Article in journal (Refereed)
    Abstract [en]

    We present a detailed comparison of the behavioral and electrophysiological development of seizure activity in mice genetically depleted of synapsin land synapsin II (SynDKO mice), based on combined video and surface EEG recordings. SynDKO mice develop handling-induced epileptic seizures at the age of 2 months. The seizures show a very regular behavioral pattern, where activity is initially dominated by truncal muscle contractions followed by various myoclonic elements. Whereas seizure behavior goes through clearly defined transitions, cortical activity as reflected by EEG recordings shows a more gradual development with respect to the emergence of different EEG components and the frequency of these components. No EEG pattern was seen to define a particular seizure behavior. However, myoclonic activity was characterized by more regular patterns of combined sharp waves and spikes. Where countable, the number of myoclonic jerks was significantly correlated to the number of such EEG complexes. Furthermore, some EEG recordings revealed epileptic regular discharges without clear behavioral seizure correlates. Our findings suggest that seizure behavior in SynDKO mice is not solely determined by cortical activity but rather reflects interplay between cortical activity and activity in other brain regions. (C) 2011 Elsevier BM. All rights reserved.

  • 3.
    Hagen, Espen
    et al.
    Julich Res Ctr, Inst Neurosci & Med INM 6, D-52425 Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, D-52425 Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, D-52425 Julich, Germany.;Norwegian Univ Life Sci, Dept Math Sci & Technol, N-1403 As, Norway..
    Dahmen, David
    Julich Res Ctr, Inst Neurosci & Med INM 6, D-52425 Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, D-52425 Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, D-52425 Julich, Germany..
    Stavrinou, Maria L.
    Norwegian Univ Life Sci, Dept Math Sci & Technol, N-1403 As, Norway.;Univ Oslo, Dept Psychol, N-0373 Oslo, Norway..
    Lindén, Henrik
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Univ Copenhagen, Dept Neurosci & Pharmacol, DK-2200 Copenhagen, Denmark.;Royal Inst Technol, Sch Comp Sci & Commun, Dept Computat Biol, S-10044 Stockholm, Sweden..
    Tetzlaff, Tom
    Julich Res Ctr, Inst Neurosci & Med INM 6, D-52425 Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, D-52425 Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, D-52425 Julich, Germany..
    van Albada, Sacha J.
    Julich Res Ctr, Inst Neurosci & Med INM 6, D-52425 Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, D-52425 Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, D-52425 Julich, Germany..
    Gruen, Sonja
    Julich Res Ctr, Inst Neurosci & Med INM 6, D-52425 Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, D-52425 Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, D-52425 Julich, Germany.;Rhein Westfal TH Aachen, Theoret Syst Neurobiol, D-52056 Aachen, Germany..
    Diesmann, Markus
    Julich Res Ctr, Inst Neurosci & Med INM 6, D-52425 Julich, Germany.;Julich Res Ctr, Inst Adv Simulat IAS 6, D-52425 Julich, Germany.;Julich Res Ctr, JARA BRAIN Inst 1, D-52425 Julich, Germany.;Rhein Westfal TH Aachen, Fac Med, Dept Psychiat Psychotherapy & Psychosomat, D-52074 Aachen, Germany.;Rhein Westfal TH Aachen, Fac 1, Dept Phys, D-52062 Aachen, Germany..
    Einevoll, Gaute T.
    Norwegian Univ Life Sci, Dept Math Sci & Technol, N-1403 As, Norway.;Univ Oslo, Dept Phys, N-0316 Oslo, Norway..
    Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks2016In: Cerebral Cortex, ISSN 1047-3211, E-ISSN 1460-2199, Vol. 26, no 12, p. 4461-4496Article in journal (Refereed)
    Abstract [en]

    With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a similar to 1 mm(2) patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.

  • 4. Leski, Szymon
    et al.
    Lindén, Henrik
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Tetzlaff, Tom
    Pettersen, Klas H.
    Einevoll, Gaute T.
    Frequency Dependence of Signal Power and Spatial Reach of the Local Field Potential2013In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 9, no 7, p. e1003137-Article in journal (Refereed)
    Abstract [en]

    Despite its century-old use, the interpretation of local field potentials (LFPs), the low-frequency part of electrical signals recorded in the brain, is still debated. In cortex the LFP appears to mainly stem from transmembrane neuronal currents following synaptic input, and obvious questions regarding the 'locality' of the LFP are: What is the size of the signal-generating region, i.e., the spatial reach, around a recording contact? How far does the LFP signal extend outside a synaptically activated neuronal population? And how do the answers depend on the temporal frequency of the LFP signal? Experimental inquiries have given conflicting results, and we here pursue a modeling approach based on a well-established biophysical forward-modeling scheme incorporating detailed reconstructed neuronal morphologies in precise calculations of population LFPs including thousands of neurons. The two key factors determining the frequency dependence of LFP are the spatial decay of the single-neuron LFP contribution and the conversion of synaptic input correlations into correlations between single-neuron LFP contributions. Both factors are seen to give low-pass filtering of the LFP signal power. For uncorrelated input only the first factor is relevant, and here a modest reduction (<50%) in the spatial reach is observed for higher frequencies (>100 Hz) compared to the near-DC (similar to 0Hz) value of about 200 mu m. Much larger frequency-dependent effects are seen when populations of pyramidal neurons receive correlated and spatially asymmetric inputs: the low-frequency (similar to 0Hz) LFP power can here be an order of magnitude or more larger than at 60 Hz. Moreover, the low-frequency LFP components have larger spatial reach and extend further outside the active population than high-frequency components. Further, the spatial LFP profiles for such populations typically span the full vertical extent of the dendrites of neurons in the population. Our numerical findings are backed up by an intuitive simplified model for the generation of population LFP.

  • 5.
    Lindén, Henrik
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Hagen, E.
    Łeski, S.
    Norheim, E. S.
    Pettersen, K. H.
    Einevoll, G. T.
    LFPy: A tool for biophysical simulation of extracellular potentials generated by detailed model neurons2014In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 7, no Jan, p. 41-Article in journal (Refereed)
    Abstract [en]

    Electrical extracellular recordings, i.e., recordings of the electrical potentials in the extracellular medium between cells, have been a main work-horse in electrophysiology for almost a century. The high-frequency part of the signal (>500 Hz), i.e., the multi-unit activity (MUA), contains information about the firing of action potentials in surrounding neurons, while the low-frequency part, the local field potential (LFP), contains information about how these neurons integrate synaptic inputs. As the recorded extracellular signals arise from multiple neural processes, their interpretation is typically ambiguous and difficult. Fortunately, a precise biophysical modeling scheme linking activity at the cellular level and the recorded signal has been established: the extracellular potential can be calculated as a weighted sum of all transmembrane currents in all cells located in the vicinity of the electrode. This computational scheme can considerably aid the modeling and analysis of MUA and LFP signals. Here, we describe LFPy, an open source Python package for numerical simulations of extracellular potentials. LFPy consists of a set of easy-to-use classes for defining cells, synapses and recording electrodes as Python objects, implementing this biophysical modeling scheme. It runs on top of the widely used NEURON simulation environment, which allows for flexible usage of both new and existing cell models. Further, calculation of extracellular potentials using the line-source-method is efficiently implemented. We describe the theoretical framework underlying the extracellular potential calculations and illustrate by examples how LFPy can be used both for simulating LFPs, i.e., synaptic contributions from single cells as well a populations of cells, and MUAs, i.e., extracellular signatures of action potentials.

  • 6.
    Lindén, Henrik
    et al.
    Norwegian University of Life Sciences.
    Pettersen, K.H.
    Einrvoll, G.T.
    Intrinsic dendritic filtering gives low-pass power spectra of local field potentials2010In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 29, no 3, p. 423-444Article in journal (Refereed)
    Abstract [en]

    The local field potential (LFP) is among the most important experimental measures when probing neural population activity, but a proper understanding of the link between the underlying neural activity and the LFP signal is still missing. Here we investigate this link by mathematical modeling of contributions to the LFP from a single layer-5 pyramidal neuron and a single layer-4 stellate neuron receiving synaptic input. An intrinsic dendritic low-pass filtering effect of the LFP signal, previously demonstrated for extracellular signatures of action potentials, is seen to strongly affect the LFP power spectra, even for frequencies as low as 10 Hz for the example pyramidal neuron. Further, the LFP signal is found to depend sensitively on both the recording position and the position of the synaptic input: the LFP power spectra recorded close to the active synapse are typically found to be less low-pass filtered than spectra recorded further away. Some recording positions display striking band-pass characteristics of the LFP. The frequency dependence of the properties of the current dipole moment set up by the synaptic input current is found to qualitatively account for several salient features of the observed LFP. Two approximate schemes for calculating the LFP, the dipole approximation and the two-monopole approximation, are tested and found to be potentially useful for translating results from large-scale neural network models into predictions for results from electroencephalographic (EEG) or electrocorticographic (ECoG) recordings.

  • 7.
    Lindén, Henrik
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Tetzlaff, Tom
    Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway.
    Potjans, Tobias C.
    Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Research Center Jülich, Germany.
    Pettersen, Klas H.
    Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway .
    Grün, Sonja
    Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Research Center Jülich, Germany.
    Diesmann, Markus
    Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Research Center Jülich, Germany.
    Einevoll, Gaute T.
    Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences, Ås, Norway.
    Modeling the spatial reach of the LFP2011In: Neuron, ISSN 0896-6273, E-ISSN 1097-4199, Vol. 72, no 5, p. 859-872Article in journal (Refereed)
    Abstract [en]

    The local field potential (LFP) reflects activity of many neurons in the vicinity of the recording electrode and is therefore useful for studying local network dynamics. Much of the nature of the LFP is, however, still unknown. There are, for instance, contradicting reports on the spatial extent of the region generating the LFP. Here, we use a detailed biophysical modeling approach to investigate the size of the contributing region by simulating the LFP from a large number of neurons around the electrode. We find that the size of the generating region depends on the neuron morphology, the synapse distribution, and the correlation in synaptic activity. For uncorrelated activity, the LFP represents cells in a small region (within a radius of a few hundred micrometers). If the LFP contributions from different cells are correlated, the size of the generating region is determined by the spatial extent of the correlated activity.

  • 8. Mazzoni, A.
    et al.
    Lindén, Henrik
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. University of Copenhagen, Denmark.
    Cuntz, H.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Panzeri, S.
    Einevoll, G. T.
    Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models2015In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 11, no 12, article id e1004584Article in journal (Refereed)
    Abstract [en]

    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.

  • 9. Pettersen, Klas H.
    et al.
    Lindén, Henrik
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Norwegian University of Life Sciences, Norway; University of Copenhagen, Denmark .
    Tetzlaff, Tom
    Einevoll, Gaute T.
    Power Laws from Linear Neuronal Cable Theory: Power Spectral Densities of the Soma Potential, Soma Membrane Current and Single-Neuron Contribution to the EEG2014In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 10, no 11, p. e1003928-Article in journal (Refereed)
    Abstract [en]

    Power laws, that is, power spectral densities (PSDs) exhibiting 1/f(alpha) behavior for large frequencies f, have been observed both in microscopic (neural membrane potentials and currents) and macroscopic (electroencephalography; EEG) recordings. While complex network behavior has been suggested to be at the root of this phenomenon, we here demonstrate a possible origin of such power laws in the biophysical properties of single neurons described by the standard cable equation. Taking advantage of the analytical tractability of the so called ball and stick neuron model, we derive general expressions for the PSD transfer functions for a set of measures of neuronal activity: the soma membrane current, the current-dipole moment (corresponding to the single-neuron EEG contribution), and the soma membrane potential. These PSD transfer functions relate the PSDs of the respective measurements to the PSDs of the noisy input currents. With homogeneously distributed input currents across the neuronal membrane we find that all PSD transfer functions express asymptotic highfrequency 1/f(alpha) power laws with power-law exponents analytically identified as alpha(I)(infinity) =1/2 for the soma membrane current, alpha(p)(infinity) = 3/2 for the current-dipole moment, and alpha(V)(infinity) = 2 for the soma membrane potential. Comparison with available data suggests that the apparent power laws observed in the high-frequency end of the PSD spectra may stem from uncorrelated current sources which are homogeneously distributed across the neural membranes and themselves exhibit pink (1/f) noise distributions. While the PSD noise spectra at low frequencies may be dominated by synaptic noise, our findings suggest that the high-frequency power laws may originate in noise from intrinsic ion channels. The significance of this finding goes beyond neuroscience as it demonstrates how 1/f(alpha) power laws with a wide range of values for the power-law exponent a may arise from a simple, linear partial differential equation.

  • 10.
    Tully, Philip J.
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Karolinska Inst, Sweden.
    Lindén, Henrik
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Karolinska Inst, Sweden.
    Hennig, Matthias H.
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Karolinska Inst, Sweden.
    Spike-Based Bayesian-Hebbian Learning of Temporal Sequences2016In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 5, article id e1004954Article in journal (Refereed)
    Abstract [en]

    Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

  • 11.
    Tully, Philip
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Karolinska Institutet, Sweden; University of Edinburgh, UK.
    Lindén, Henrik
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Karolinska Institutet, Sweden.
    Hennig, Matthias
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. Stockholm University, Stockholm; Karolinska Institutet, Sweden.
    Probabilistic computation underlying sequence learning in a spiking attractor memory network2013In: BMC neuroscience (Online), ISSN 1471-2202, E-ISSN 1471-2202, no 14 (Suppl 1)Article in journal (Refereed)
1 - 11 of 11
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