Gaute Einevoll

• Buccino, Alessio Paolo; Hagen, Espen; Einevoll, Gaute; Häfliger, Philipp & Cauwenberghs, Gert (2018). Independent Component Analysis for Fully Automated Multi-Electrode Array Spike Sorting, In Nigel Lovell (ed.),  Annual Conference of the IEEE Engineering in Medicine and Biology Society.  IEEE conference proceedings.  ISBN 9781538636473.  paper.  s 2627 - 2630
• Buccino, Alessio Paolo; Kordovan, Michael; Ness, Torbjørn V; Merkt, Benjamin; Häfliger, Philipp; Fyhn, Marianne; Cauwenberghs, Gert; Rotter, Stefan & Einevoll, Gaute (2018). Combining biophysical modeling and deep learning for multielectrode array neuron localization and classification. Journal of Neurophysiology.  ISSN 0022-3077.  120(3), s 1212- 1232 . doi: 10.1152/jn.00210.2018 Fulltekst i vitenarkiv.
• Buccino, Alessio Paolo; Ness, Torbjørn V; Einevoll, Gaute; Cauwenberghs, Gert & Häfliger, Philipp (2018). A Deep Learning Approach for the Classification of Neuronal Cell Types, In Nigel Lovell (ed.),  Annual Conference of the IEEE Engineering in Medicine and Biology Society.  IEEE conference proceedings.  ISBN 9781538636473.  paper.  s 999 - 1002
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2018). Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0. Frontiers in Neuroinformatics.  ISSN 1662-5196.  12(92), s 1- 36 . doi: 10.3389/fninf.2018.00092 Fulltekst i vitenarkiv. Vis sammendrag

  Recordings of extracellular electrical, and later also magnetic, brain signals have been the dominant technique for measuring brain activity for decades. The interpretation of such signals is however nontrivial, as the measured signals result from both local and distant neuronal activity. In volume-conductor theory the extracellular potentials can be calculated from a distance-weighted sum of contributions from transmembrane currents of neurons. Given the same transmembrane currents, the contributions to the magnetic field recorded both inside and outside the brain can also be computed. This allows for the development of computational tools implementing forward models grounded in the biophysics underlying electrical and magnetic measurement modalities. LFPy (LFPy.readthedocs.io) incorporated a well-established scheme for predicting extracellular potentials of individual neurons with arbitrary levels of biological detail. It relies on NEURON (neuron.yale.edu) to compute transmembrane currents of multicompartment neurons which is then used in combination with an electrostatic forward model. Its functionality is now extended to allow for modeling of networks of multicompartment neurons with concurrent calculations of extracellular potentials and current dipole moments. The current dipole moments are then, in combination with suitable volume-conductor head models, used to compute non-invasive measures of neuronal activity, like scalp potentials (electroencephalographic recordings; EEG) and magnetic fields outside the head (magnetoencephalographic recordings; MEG). One such built-in head model is the four-sphere head model incorporating the different electric conductivities of brain, cerebrospinal fluid, skull and scalp. We demonstrate the new functionality of the software by constructing a network of biophysically detailed multicompartment neuron models from the Neocortical Microcircuit Collaboration (NMC) Portal (bbp.epfl.ch/nmc-portal) with corresponding statistics of connections and synapses, and compute in vivo-like extracellular potentials (local field potentials, LFP; electrocorticographical signals, ECoG) and corresponding current dipole moments. From the current dipole moments we estimate corresponding EEG and MEG signals using the four-sphere head model. We also show strong scaling performance of LFPy with different numbers of message-passing interface (MPI) processes, and for different network sizes with different density of connections. The open-source software LFPy is equally suitable for execution on laptops and in parallel on high-performance computing (HPC) facilities and is publicly available on GitHub.com.

• Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Einevoll, Gaute & Plesser, Hans Ekkehard (2018). Firing-rate models for neurons with a broad repertoire of spiking behaviors. Journal of Computational Neuroscience.  ISSN 0929-5313.  45(2), s 103- 132 . doi: 10.1007/s10827-018-0693-9 Fulltekst i vitenarkiv.
• Luo, Jinhong; Macias, Silvio; Ness, Torbjørn V; Einevoll, Gaute; Zhang, Kechen & Moss, Cynthia F. (2018). Neural timing of stimulus events with microsecond precision. PLoS biology.  ISSN 1544-9173.  16:e2006422(10), s 1- 22 . doi: 10.1371/journal.pbio.2006422 Fulltekst i vitenarkiv.
• Mäki-Marttunen, Tuomo; Halnes, Geir; Devor, Anna; Metzner, Christoph; Dale, Anders M.; Andreassen, Ole Andreas & Einevoll, Gaute (2018). A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells. Journal of Neuroscience Methods.  ISSN 0165-0270.  293, s 264- 283 . doi: 10.1016/j.jneumeth.2017.10.007 Fulltekst i vitenarkiv.
• Martínez-Cañada, Pablo; Mobarhan, Milad; Halnes, Geir; Fyhn, Marianne; Morillas, Christian; Pelayo, Francisco & Einevoll, Gaute (2018). Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells. PloS Computational Biology.  ISSN 1553-734X.  14(1), s 1- 45 . doi: 10.1371/journal.pcbi.1005930 Fulltekst i vitenarkiv.
• Mobarhan, Milad; Halnes, Geir; Martínez-Cañada, Pablo; Hafting, Torkel; Fyhn, Marianne & Einevoll, Gaute (2018). Firing-rate based network modeling of the dLGN circuit: Effects of cortical feedback on spatiotemporal response properties of relay cells. PloS Computational Biology.  ISSN 1553-734X.  14(5) . doi: 10.1371/journal.pcbi.1006156 Fulltekst i vitenarkiv.
• Ness, Torbjørn V; Remme, Michiel W.H. & Einevoll, Gaute (2018). h-type membrane current shapes the local field potential from populations of pyramidal neurons. Journal of Neuroscience.  ISSN 0270-6474.  38(26), s 6011- 6024 . doi: 10.1523/JNEUROSCI.3278-17.2018
• Pesaran, Bijan; Vinck, Martin; Einevoll, Gaute; Sirota, Anton; Fries, Pascal; Siegel, Markus; Truccolo, Wilson; Schroeder, Charles E. & Srinivasan, Ramesh (2018). Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nature Neuroscience.  ISSN 1097-6256.  21, s 903- 919 . doi: 10.1038/s41593-018-0171-8
• Solbrå, Andreas Våvang; Bergersen, Aslak Wigdahl; van den Brink, Jonas; Malthe-Sørenssen, Anders; Einevoll, Gaute & Halnes, Geir (2018). A Kirchhoff-Nernst-Planck framework for modeling large scale extracellular electrodiffusion surrounding morphologically detailed neurons. PloS Computational Biology.  ISSN 1553-734X.  14(10), s 1- 26 . doi: 10.1371/journal.pcbi.1006510 Fulltekst i vitenarkiv.
• Tennøe, Simen; Halnes, Geir & Einevoll, Gaute (2018). Uncertainpy: A Python toolbox for uncertainty quantification and sensitivity analysis in computational neuroscience. Frontiers in Neuroinformatics.  ISSN 1662-5196.  12:49, s 1- 29 . doi: 10.3389/fninf.2018.00049 Fulltekst i vitenarkiv.
• Aasebø, Ida E. J.; Lepperød, Mikkel Elle; Stavrinou, Maria; Nøkkevangen, Sandra; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2017). Temporal processing in the visual cortex of the awake and anesthetized rat. eNeuro.  ISSN 2373-2822.  4:e0059-17.2017(4), s 1- 26 . doi: 10.1523/ENEURO.0059-17.2017 Fulltekst i vitenarkiv. Vis sammendrag

  The activity pattern and temporal dynamics within and between neuron ensembles are essential features of information processing and believed to be profoundly affected by anesthesia. Much of our general understanding of sensory information processing, including computational models aimed at mathematically simulating sensory information processing, rely on parameters derived from recordings conducted on animals under anesthesia. Due to the high variety of neuronal subtypes in the brain, population-based estimates of the impact of anesthesia may conceal unit- or ensemble-specific effects of the transition between states. Using chronically implanted tetrodes into primary visual cortex (V1) of rats, we conducted extracellular recordings of single units and followed the same cell ensembles in the awake and anesthetized states. We found that the transition from wakefulness to anesthesia involves unpredictable changes in temporal response characteristics. The latency of single-unit responses to visual stimulation was delayed in anesthesia, with large individual variations between units. Pair-wise correlations between units increased under anesthesia, indicating more synchronized activity. Further, the units within an ensemble show reproducible temporal activity patterns in response to visual stimuli that is changed between states, suggesting state-dependent sequences of activity. The current dataset, with recordings from the same neural ensembles across states, is well suited for validating and testing computational network models. This can lead to testable predictions, bring a deeper understanding of the experimental findings and improve models of neural information processing. Here, we exemplify such a workflow using a Brunel network model.

• Buccino, Alessio Paolo; Ness, Torbjørn Vefferstad; Einevoll, Gaute; Cauwenberghs, Gert & Häfliger, Philipp (2017). Localizing neuronal somata from Multi-Electrode Array in-vivo recordings using deep learning. IEEE Engineering in Medicine and Biology Society. Conference Proceedings.  ISSN 1557-170X.  s 974- 977 . doi: 10.1109/EMBC.2017.8036988 Vis sammendrag

  With the latest development in the design and fabrication of high-density Multi-Electrode Arrays (MEA) for in-vivo neural recordings, the spatiotemporal information in the recorded signals allows for refined estimation of a neuron’s location around the probe. In parallel, advances in computational models for neural activity enables simulation of recordings from neurons with detailed morphology. Our approach uses deep learning algorithms on a large set of such simulation data to extract the 3D position of the neuronal somata. Multi-compartment models from 13 different neural morphologies in layer 5 (L5) of the rat’s neocortex are placed at random locations and with different alignments with respect to the MEA. The sodium trough and repolarisation peak images on the MEA serve as input features for a Convolutional Neural Network (CNN), which predicts the neural location robustly and with low error rates. The forward modeling/machine learning approach yields very accurate results for the different morphologies and is able to cope with different neuron alignments.

• Devor, Anna; Andreassen, Ole Andreas; Wang, Yunpeng; Mäki-Marttunen, Tuomo; Smeland, Olav Bjerkehagen; Fan, Chun Chieh; Schork, Andrew J.; Holland, Dominic; Thompson, Wesley Kurt; Witoelar, Aree; Chen, Chi-Hua; Desikan, Rahul S.; McEvoy, Linda K.; Djurovic, Srdjan; Greengard, Paul; Svenningsson, Per; Einevoll, Gaute & Dale, Anders (2017). Genetic evidence for role of integration of fast and slow neurotransmission in schizophrenia. Molecular Psychiatry.  ISSN 1359-4184.  22(6), s 792- 801 . doi: 10.1038/mp.2017.33 Fulltekst i vitenarkiv.
• Dragly, Svenn-Arne; Mobarhan, Milad; Solbrå, Andreas Våvang; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute (2017). Neuronify: An educational simulator for neural circuits. eNeuro.  ISSN 2373-2822.  4(2:e0022-17.2017), s 1- 13 . doi: 10.1523/ENEURO.0022-17.2017 Fulltekst i vitenarkiv.
• Gratiy, Sergey L.; Halnes, Geir; Denman, Daniel; Hawrylycz, Michael J; Koch, Christof; Einevoll, Gaute & Anastassiou, Costas A (2017). From Maxwell's equations to the theory of current-source density analysis. European Journal of Neuroscience.  ISSN 0953-816X.  45(8), s 1013- 1023 . doi: 10.1111/ejn.13534 Fulltekst i vitenarkiv.
• Hagen, Espen; Fossum, Janne- Christine; Pettersen, Klas; Alonso, Jose-Manuel; Swadlow, Harvey A. & Einevoll, Gaute (2017). Focal local field potential signature of the single-axon monosynaptic thalamocortical connection. Journal of Neuroscience.  ISSN 0270-6474.  37(20), s 5123- 5143 . doi: 10.1523/JNEUROSCI.2715-16.2017 Fulltekst i vitenarkiv.
• Halnes, Geir; Mäki-Marttunen, Tuomo; Pettersen, Klas; Andreassen, Ole Andreas & Einevoll, Gaute (2017). Ion diffusion may introduce spurious current sources in current-source density (CSD) analysis. Journal of Neurophysiology.  ISSN 0022-3077.  118(1), s 114- 120 . doi: 10.1152/jn.00976.2016 Fulltekst i vitenarkiv.
• Lee, Jin Hyung; Carlson, David E.; Shokri Razaghi, Hooshmand; Yao, Weichi; Goetz, Georges A.; Hagen, Espen; Batty, Eleanor; Chichilnisky, E.J.; Einevoll, Gaute & Paninski, Liam (2017). YASS: Yet Another Spike Sorter. Advances in Neural Information Processing Systems.  ISSN 1049-5258.  30, s 4005- 4015 . doi: 10.1101/151928 Fulltekst i vitenarkiv.
• Mäki-Marttunen, Tuomo; Lines, Glenn Terje; Edwards, Andrew G; Tveito, Aslak; Dale, Anders M.; Einevoll, Gaute & Andreassen, Ole Andreas (2017). Pleiotropic effects of schizophrenia-associated genetic variants in neuron firing and cardiac pacemaking revealed by computational modeling. Translational psychiatry.  ISSN 2158-3188.  7(5) . doi: 10.1038/s41398-017-0007-4
• Miceli, Stephanie; Ness, Torbjørn Vefferstad; Einevoll, Gaute & Schubert, Dirk (2017). Impedance spectrum in cortical tissue: Implications for propagation of LFP signals on the microscopic level. eNeuro.  ISSN 2373-2822.  4(1) . doi: 10.1523/ENEURO.0291-16.2016 Fulltekst i vitenarkiv.
• Næss, Solveig; Chintaluri, Chaitanya; Ness, Torbjørn Vefferstad; Dale, Anders; Einevoll, Gaute & Wójcik, Daniel K. (2017). Corrected four-sphere head model for EEG signals. Frontiers in Human Neuroscience.  ISSN 1662-5161.  11, s 1- 7 . doi: 10.3389/fnhum.2017.00490 Fulltekst i vitenarkiv. Vis sammendrag

  The EEG signal is generated by electrical brain cell activity, often described in terms of current dipoles. By applying EEG forward models we can compute the contribution from such dipoles to the electrical potential recorded by EEG electrodes. Forward models are key both for generating understanding and intuition about the neural origin of EEG signals as well as inverse modeling, i.e., the estimation of the underlying dipole sources from recorded EEG signals. Different models of varying complexity and biological detail are used in the field. One such analytical model is the four-sphere model which assumes a four-layered spherical head where the layers represent brain tissue, cerebrospinal fluid (CSF), skull, and scalp, respectively. While conceptually clear, the mathematical expression for the electric potentials in the four-sphere model is cumbersome, and we observed that the formulas presented in the literature contain errors. Here, we derive and present the correct analytical formulas with a detailed derivation. A useful application of the analytical four-sphere model is that it can serve as ground truth to test the accuracy of numerical schemes such as the Finite Element Method (FEM). We performed FEM simulations of the four-sphere head model and showed that they were consistent with the corrected analytical formulas. For future reference we provide scripts for computing EEG potentials with the four-sphere model, both by means of the correct analytical formulas and numerical FEM simulations.

• Tveito, Aslak; Jæger, Karoline Horgmo; Lines, Glenn Terje; Paszkowski, Lukasz; Sundnes, Joakim; Edwards, Andrew; Mäki-Marttunen, Tuomo; Halnes, Geir & Einevoll, Gaute (2017). An evaluation of the accuracy of classical models for computing the membrane potential and extracellular potential for neurons. Frontiers in Computational Neuroscience.  ISSN 1662-5188.  11 . doi: 10.3389/fncom.2017.00027 Fulltekst i vitenarkiv.

Se alle arbeider i Cristin

• Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute (2019). Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
• Aasebø, Ida Elisabeth Jørgensen; Røe, Malin Benum; Mobarhan, Milad; Lensjø, Kristian Kinden; Stöber, Tristan Manfred; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2018). Orientation selectivity in the freely moving rat: Movement-robust orientation-selectivity (MROS) of units in the deep layers of the rat visual cortex.
• Berthet, Pierre; Hagen, Espen; Ness, Torbjørn V & Einevoll, Gaute (2018). Selectivity and sensitivity of cortical neurons to electric stimulation using ECoG electrode arrays.
• Delsett, Lene Liebe; Einevoll, Gaute; Kjeldstad, Torunn & Jemterud, Torkild (2018, 26. januar). Abels tårn 26. januar 2018. [Radio].  NRK P2 Ekko.
• Einevoll, Gaute (2018). Active subthreshold dendritic conductances shape the local field potential (LFP).
• Einevoll, Gaute (2018, 19. oktober). Deltagelse i Abels Tårn, NRK P2. [Radio].  NRL.
• Einevoll, Gaute (2018, 29. januar). Deltagelse i TV-programmet Abels Tårn. [TV].  NRK.
• Einevoll, Gaute (2018, 22. oktober). Deltagelse i TV-programmet Abels Tårn. [TV].  NRK.
• Einevoll, Gaute (2018, 18. mai). Deltagelse i radioprogrammet "Abels Tårn" på NRK P2. [Radio].  NRK.
• Einevoll, Gaute (2018, 14. mars). Deltagelse i radioprogrammet "NRK Alltid Nyheter" på NRK (om Stephen Hawking). [Radio].  NRK.
• Einevoll, Gaute (2018, 14. mars). Deltagelse i radioprogrammet "Nyhetslunsj" på NRK P2. [Radio].  NRK.
• Einevoll, Gaute (2018, 22. februar). Deltagelse i radioprogrammet "Nyhetsmorgen" på NRK P2 (om synsproteser for blinde). [Radio].  NRK.
• Einevoll, Gaute (2018, 25. mai). Deltagelse i radioprogrammet "Slettan & Martin tar helg" på NRK P1. [Radio].  NRK.
• Einevoll, Gaute (2018, 07. september). Deltagelse i radioprogrammet "Slettan & Martin tar helg" på NRK P1. [Radio].  NRK.
• Einevoll, Gaute (2018). DigiBrain: Biophysical psychiatry - Can computers model thoughts and emotions?.
• Einevoll, Gaute (2018). Fortell meg om atomer og himmelen.
• Einevoll, Gaute (2018). Hjernen 2.0.
• Einevoll, Gaute (2018, 14. mars). Intervju i NRK Nyheter om Stephen Hawking. [TV].  NRK.
• Einevoll, Gaute (2018). Modelling and analysis of extracellular potentials: Tutorial at Computational Neuroscience Meeting 2018, Seattle.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2018). Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and MEG signals with LFPy 2.0.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2018). Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and MEG signals with LFPy2.0.
• Næss, Solveig; Ness, Torbjørn V; Hagen, Espen & Einevoll, Gaute (2018). Biophysical modelling of EEG signals from large neural populations.
• Røe, Malin Benum; Aasebø, Ida Elisabeth Jørgensen; Mobarhan, Milad; Lensjø, Kristian Kinden; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2018). Movement-resistant orientation selectivity of cells in the deep layers of the rat visual cortex.
• Røe, Malin Benum; Aasebø, Ida Elisabeth Jørgensen; Mobarhan, Milad; Lensjø, Kristian Kinden; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2018). Stable orientation tuning in the freely moving rat: Movement-robust orientation selective neurons in the deep layers of the primary visual cortex.
• Skaar, Jan-Eirik Welle; Hagen, Espen; Stasik, Alexander Johannes; Einevoll, Gaute & Ness, Torbjørn V (2018). Estimation of model parameters from LFPs of spiking neuron networks using deep learning.
• Solbrå, Andreas Våvang; Bergersen, Aslak Wigdahl; van den Brink, Jonas; Malthe-Sørenssen, Anders; Einevoll, Gaute & Halnes, Geir (2018). Modeling electrodiffusion in the extracellular space around a morphologically detailed neuron.
• Stasik, Alexander Johannes; Mobarhan, Milad; Hagen, Espen & Einevoll, Gaute (2018). Generating Input from LGN for Simulation of Visual Cortex.
• Tennøe, Simen; Halnes, Geir & Einevoll, Gaute (2018). Uncertainpy: A Python toolbox for uncertainty quantification and sensitivity analysis of computational neuroscience models.
• Thunemann, Martin; Ness, Torbjørn V; Kılıç, Kıvılcım; Ferri, Christopher G.L.; Sakadzic, Sava; Dale, Anders M.; Fainman, Yeshaiahu; Boas, David A.; Einevoll, Gaute & Devor, Anna (2018). DOES LIGHT PROPAGATE BETTER ALONG PYRAMIDAL APICAL DENDRITES IN CEREBRAL CORTEX?.
• Aasebø, Ida E. J.; Mobarhan, Milad; Røe, Malin Benum; Lensjø, Kristian Kinden; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2017). Movement-resistant orientation-selective units in the deep layers of the visual cortex.
• Buccino, Alessio Paolo; Ness, Torbjørn Vefferstad; Einevoll, Gaute; Cauwenberghs, Gert; Fyhn, Marianne; Hafting, Torkel & Häfliger, Philipp (2017). Classification of Neural Cell-types from Extracellular Signatures on Multi-Electrode Arrays using Deep Learning.
• Dragly, Svenn-Arne; Mobarhan, Milad; Solbrå, Andreas Våvang; Tennøe, Simen; Hafreager, Anders; Malthe-Sørenssen, Anders; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute (2017). Neuronify: An Educational Simulator for Neural Circuits. Vis sammendrag

  Neurons are cells in the brain that are able to rapidly change the electric field across their cell membrane. These changes allow neurons to communicate with each other and is the basis for the complex computations in the brain. Understanding how neurons communicate and the properties of neuronal networks is essential for neuroscience students. Traditionally, students draw networks with pen and paper and qualitatively deduce features of the network by analyzing the static drawings. Here, we present Neuronify, an app that allows students to draw the same networks on a computer or mobile device and run dynamic simulations without programming. The students can test their analysis by running the network and check their predictions against the outcome. Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. Such software is readily available in many areas of natural science such as physics and electrical engineering. However, few educational apps are available for simulation of neural networks. Neuronify allows the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can drag and drop network elements such as neurons, electrical stimulation tools and recording devices. The components can then easily be connected to one another. Building intuition for how neurons and neural networks behave has been a top priority in designing Neuronify. We aim to provide a low entry point to simulation-based neuroscience. Most undergraduate students do not have the computational experience to create their own neural simulator. Neuronify offers them an opportunity to build and experiment with neural networks in a graphical and easy-to-understand interface. By playing around with the networks, the students can develop a good understanding of their properties. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt. It has been downloaded more than 30,000 times since its launch and is available on smart phones (Android, iOS), tablet computers as well personal computers (Windows, Mac, Linux).

• Einevoll, Gaute (2017). Biophysical modeling of benchmarking data for validation of methods for analysing electrophysiological data.
• Einevoll, Gaute (2017). Can we compute how we think?.
• Einevoll, Gaute (2017, 19. mai). Intervju i "Studio 2" i NRK P2 om hjernen til vidunderbarn. [Radio].  NRK P2.
• Einevoll, Gaute (2017, 31. oktober). Intervju i "Studio 2" i NRK P2 om obduksjon av hjerne til massemorder. [Radio].  NRK P2.
• Einevoll, Gaute (2017, 10. juni). Intervju om fremtidige hjerneteknologier i episode 5 av "Evig Ung". [TV].  NRK1.
• Einevoll, Gaute (2017). Neuroscience with both hands: Bridging scales with mathematics.
• Einevoll, Gaute (2017). Nytt fra hjerneforskningen.
• Einevoll, Gaute (2017). Nytt om hjernen.
• Einevoll, Gaute (2017). Physics-based signal modeling of the brain tissue underlying the NeuroGrid.
• Einevoll, Gaute (2017). Tools for teaching neurobiology.
• Einevoll, Gaute (2017). Virtual experiments: Modeling of population signals such as LFP and EEG.
• Einevoll, Gaute (2017). What can the local field potential (LFP) tell us about the cortical network activity?.
• Einevoll, Gaute (2017). What can we learn from local field potentials (LFPs)?.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2017). Computing Brain Signals (CBra): Concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2017). Computing Brain Signals: concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2017). Computing brain signals (CBra): Concurrent simulation of network activity, extracellular electric potentials and magnetic fields.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn Vefferstad & Einevoll, Gaute (2017). Computing Brain Signals: Concurrent simulation of network activity, extracellular electric poten- tials and magnetic fields..
• Haugen, Kjetil K.; Heen, Knut Peder; Guvåg, Bjørn; Oterhals, Geir; Holmgren, Johan; Hauge, Olav; Olsen, Kai; Arntzen, Halvard; Solenes, Oskar; Einevoll, Gaute & Eide, Jan Ragnvald (2017). Fra toppen av Haugen : 100/11. Panorama : nettavis for Høgskolen i Molde.
• Lee, Jin Hyung; Carlson, David E.; Shokri Razaghi, Hooshmand; Yao, Weichi; Goetz, Georges A.; Hagen, Espen; Batty, Eleanor; Chichilnisky, E.J.; Einevoll, Gaute & Paninski, Liam (2017). YASS: Yet Another Spike Sorter.
• Lepperød, Mikkel Elle; Lai, Yi Ming; Fyhn, Marianne; Einevoll, Gaute; Solstad, Trygve; Hafting, Torkel & de Kamps, Marc (2017). Population density model of the entorhinal stellate-cell network with adaptation.
• Mäki-Marttunen, Tuomo; Krull, Florian; Bettella, Francesco; Metzner, Christoph; Devor, Anna; Djurovic, Srdjan; Dale, Anders M.; Andreassen, Ole Andreas & Einevoll, Gaute (2017). Layer V pyramidal cells as mediators of delta oscillations: Insights from biophysically detailed modeling and connections with schizophrenia genetics..
• Mäki-Marttunen, Tuomo; Krull, Florian; Bettella, Francesco; Moberget, Torgeir; Elvsåshagen, Torbjørn; Metzner, Christoph; Devor, Anna; Djurovic, Srdjan; Dale, Anders M.; Andreassen, Ole Andreas & Einevoll, Gaute (2017). Delta oscillations in schizophrenia: Insights from biophysically detailed modeling of networks of layer V pyramidal cells..
• Mobarhan, Milad; Halnes, Geir; Martínez-Cañada, Pablo; Hafting, Torkel; Fyhn, Marianne & Einevoll, Gaute (2017). Firing-rate Based Network Modeling of the dLGN Circuit: Effects of Cortical Feedback on Spatiotemporal Response Properties of Relay Cells.
• Ness, Torbjørn Vefferstad; Remme, Michiel W. H. & Einevoll, Gaute (2017). Active subthreshold dendritic conductances shape the local field potential.
• Næss, Solveig; Ness, Torbjørn V; Halnes, Geir; Halgren, Eric; Dale, Anders M. & Einevoll, Gaute (2017). Biophysical modeling of single neuron contributions to ECoG and EEG signals.
• Næss, Solveig; Ness, Torbjørn V; Halnes, Geir; Halgren, Eric; Dale, Anders M. & Einevoll, Gaute (2017). Biophysical modeling of single-neuron contributions to ECoG and EEG signals.
• Næss, Solveig; Ness, Torbjørn V; Halnes, Geir; Halgren, Eric; Dale, Anders & Einevoll, Gaute (2017). Biophysical modeling of single-neuron contributions toECoG, EEG and MEG signals.
• Thunemann, Martin; Ness, Torbjørn V; Kılıç, Kıvılcım; Desjardins, Michèle; Bompierre, Ségolène; Vandenberghe, Matthieu; Cheng, Qun; Weldy, Kimberly A.; Djurovic, Srdjan; Andreassen, Ole Andreas; Boas, David A.; Herzog, Herbert; Einevoll, Gaute; Dale, Anders M & Devor, Anna (2017). ACTIVATION OF CORTICAL NPY-EXPRESSING INTERNEURONS PRODUCES VASCULAR EFFECTS WITHOUT MEASURABLE LFP.
• Angelsen, Arild; Bakken, Lars; Benjaminsen, Tor A; Bjørnstad, Åsmund; Eijsink, Vincent; Einevoll, Gaute; Eriksen, Siri; Frostegård, Åsa Helena; Moe, Stein Ragnar; Salbu, Brit; Svihus, Birger & Vatn, Arild (2016, 11. mai). Omorgansiering uten mål og mening.  Kronikk i Klassekampen.
• Einevoll, Gaute (2016). Brain modeling: Key challenges of the multiscale approach.
• Einevoll, Gaute (2016). Computational neuroscience: Bridging brain scales with mathematics.
• Einevoll, Gaute (2016). Future developments of theoretical neuroscience.
• Einevoll, Gaute (2016). Future developments of theoretical neuroscience.
• Einevoll, Gaute (2016, 18. april). Har du hørt om Jennifer Aniston-nevronet?. [Internett].  Titan.uio.no.
• Einevoll, Gaute (2016). Hjernen.
• Einevoll, Gaute (2016). Hjernen.
• Einevoll, Gaute (2016). Hjernetid. Naturfag : tidsskrift for Nasjonalt nettverk for naturfag.  ISSN 1501-8601.
• Einevoll, Gaute (2016). How to test ideas about how the brain works.
• Einevoll, Gaute (2016). Intervju om hjerneforskning.
• Einevoll, Gaute (2016, 12. januar). Intervju om hjernen og religion, "Salongen", NRK P2. [Radio].  NRK P2.
• Einevoll, Gaute (2016). Kan vi regne ut hvordan vi tenker? Er fysikk+biologi sant?.
• Einevoll, Gaute (2016, 15. oktober). Mister skoen i fallskjermhopp - så skjer dette. [TV].  VGTV.
• Einevoll, Gaute (2016). Modeling and interpretation of extracellular potentials.

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