Gaute Einevoll

• Næss, Solveig; Halnes, Geir; Hagen, Espen; Hagler, Donald J.; Dale, Anders M.; Einevoll, Gaute & Ness, Torbjørn Vefferstad (2021). Biophysically detailed forward modeling of the neural origin of EEG and MEG signals. NeuroImage.  ISSN 1053-8119.  225 . doi: 10.1016/j.neuroimage.2020.117467 Fulltekst i vitenarkiv.
• Buccino, Alessio Paolo & Einevoll, Gaute (2020). MEArec: A fast and customizable testbench simulator for ground-truth extracellular spiking activity. Neuroinformatics.  ISSN 1539-2791.  s 1- 20 . doi: 10.1007/s12021-020-09467-7
• Geddes, Justen; Einevoll, Gaute; Acar, Evrim & Stasik, Alexander Johannes (2020). Multi-Linear Population Analysis (MLPA) of LFP Data Using Tensor Decompositions. Frontiers in Applied Mathematics and Statistics.  ISSN 2297-4687.  6 . doi: 10.3389/fams.2020.00041
• Mäki-Marttunen, Tuomo; Iannella, Nicolangelo Libero; Edwards, Andrew; Einevoll, Gaute & Blackwell, Kim T. (2020). A unified computational model for cortical post-synaptic plasticity. eLIFE.  ISSN 2050-084X.  9, s 1- 37 . doi: 10.7554/eLife.55714
• Skaar, Jan-Eirik Welle; Stasik, Alexander Johannes; Hagen, Espen; Ness, Torbjørn V & Einevoll, Gaute (2020). Estimation of neural network model parameters from local field potentials (LFPs). PLoS Computational Biology.  ISSN 1553-734X.  16(3) . doi: 10.1371/journal.pcbi.1007725 Fulltekst i vitenarkiv.
• Skaar, Jan-Eirik Welle; Stasik, Alexander Johannes; Hagen, Espen; Ness, Torbjørn Vefferstad & Einevoll, Gaute (2020). Estimation of neural network model parameters from local field potentials (LFPs). PLoS Computational Biology.  ISSN 1553-734X. . doi: 10.1371/journal.pcbi.1007725
• Sætra, Marte Julie; Einevoll, Gaute & Halnes, Geir (2020). An electrodiffusive, ion conserving Pinsky-Rinzel model with homeostatic mechanisms. PLoS Computational Biology.  ISSN 1553-734X.  16(4), s 1- 36 . doi: 10.1371/journal.pcbi.1007661
• Einevoll, Gaute; Destexhe, Alain; Diesmann, Markus; Grün, Sonja; Jirsa, Viktori; de Kamps, Marc; Migliore, Michele; Ness, Torbjørn Vefferstad; Plesser, Hans Ekkehard & Schürmann, Felix (2019). The scientific case for brain simulations. Neuron.  ISSN 0896-6273.  102(4), s 735- 744 . doi: 10.1016/j.neuron.2019.03.027 Vis sammendrag

  A key element of the EU Human Brain Project (HBP) and other large-scale brain research projects is simulation of large-scale model networks of neurons. Here we argue why such simulations will likely be indispensable for bridging the scales between the neuron and system levels in the brain, and a set of brain simulators based on neuron models at different levels of biological detail should thus be developed. To allow for systematic refinement of candidate network models by comparison with experiments, the simulations should be multimodal in the sense that they should not only predict action potentials, but also electric, magnetic, and optical signals measured at the population and system levels.

• Halnes, Geir; Pettersen, Klas Henning; Øyehaug, Leiv; Rognes, Marie Elisabeth & Einevoll, Gaute (2019). Astrocytic Ion Dynamics: Implications for Potassium Buffering and Liquid Flow, In Hugues Berry & Maurizio De Pittà (ed.),  Computational Glioscience.  Springer Nature.  ISBN 978-3-030-00817-8.  Chapter.  s 363 - 391
• Halnes, Geir; Tennøe, Simen; Haug, Trude; Einevoll, Gaute; Weltzien, Finn-Arne & Hodne, Kjetil (2019). A computational model for gonadotropin releasing cells in the teleost fish medaka. PLoS Computational Biology.  ISSN 1553-734X.  15(8), s 1- 28 . doi: 10.1371/journal.pcbi.1006662 Fulltekst i vitenarkiv. Vis sammendrag

  Pituitary endocrine cells fire action potentials (APs) to regulate their cytosolic Ca2+ concentration and hormone secretion rate. Depending on animal species, cell type, and biological conditions, pituitary APs are generated either by TTX-sensitive Na+ currents (INa), high-voltage activated Ca2+ currents (ICa), or by a combination of the two. Previous computational models of pituitary cells have mainly been based on data from rats, where INa is largely inactivated at the resting potential, and spontaneous APs are predominantly mediated by ICa. Unlike in rats, spontaneous INa-mediated APs are consistently seen in pituitary cells of several other animal species, including several species of fish. In the current work we develop a computational model of gonadotropin releasing cells in the teleost fish medaka (Oryzias latipes). The model stands out from previous modeling efforts by being (1) the first model of a pituitary cell in teleosts, (2) the first pituitary cell model that fires sponateous APs that are predominantly mediated by INa, and (3) the first pituitary cell model where the kinetics of the depolarizing currents, INa and ICa, are directly fitted to voltage-clamp data. We explore the firing properties of the model, and compare it to the properties of previous models that fire ICa-based APs. We put a particular focus on how the big conductance K+ current (IBK) modulates the AP shape. Interestingly, we find that IBK can prolong AP duration in models that fire ICa-based APs, while it consistently shortens the duration of the predominantly INa-mediated APs in the medaka gonadotroph model. Although the model is constrained to experimental data from gonadotroph cells in medaka, it may likely provide insights also into other pituitary cell types that fire INa-mediated APs.

• Mäki-Marttunen, Tuomo; Devor, Anna; Phillips, William A; Dale, Anders M.; Andreassen, Ole Andreas & Einevoll, Gaute (2019). Computational Modeling of Genetic Contributions to Excitability and Neural Coding in Layer V Pyramidal Cells: Applications to Schizophrenia Pathology. Frontiers in Computational Neuroscience.  ISSN 1662-5188.  13 . doi: 10.3389/fncom.2019.00066 Fulltekst i vitenarkiv.
• Mäki-Marttunen, Tuomo; Kaufmann, Tobias; Elvsåshagen, Torbjørn; Devor, Anna; Djurovic, Srdjan; Westlye, Lars Tjelta; Linne, Marja-Leena; Rietschel, Marcella; Schubert, Dirk; Borgwardt, Stefan; Efrim-Budisteanu, Magdalena; Bettella, Francesco; Halnes, Geir; Hagen, Espen; Næss, Solveig; Ness, Torbjørn V; Moberget, Torgeir; Edwards, Andrew G.; Metzner, Christoph; Fyhn, Marianne; Dale, Anders M.; Einevoll, Gaute & Andreassen, Ole Andreas (2019). Biophysical Psychiatry—How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders.. Frontiers in Psychiatry.  ISSN 1664-0640.  10 . doi: doi: 10.3389/fpsyt.2019.00534 Fulltekst i vitenarkiv.
• Mäki-Marttunen, Tuomo; Krull, Florian; Bettella, Francesco; Hagen, Espen; Næss, Solveig; Ness, Torbjørn V; Moberget, Torgeir; Elvsåshagen, Torbjørn; Metzner, Christoph; Devor, Anna; Edwards, Andrew G.; Fyhn, Marianne; Djurovic, Srdjan; Dale, Anders; Andreassen, Ole Andreas & Einevoll, Gaute (2019). Alterations in schizophrenia-associated genes can lead to increased power in delta oscillations. Cerebral Cortex.  ISSN 1047-3211.  29(2), s 875- 891 . doi: 10.1093/cercor/bhy291 Fulltekst i vitenarkiv.
• Tennøe, Simen; Hodne, Kjetil; Haug, Trude; Weltzien, Finn-Arne; Einevoll, Gaute & Halnes, Geir (2019). [Re] Fast-Activating Voltage- and Calcium-Dependent Potassium (BK) Conductance Promotes Bursting in Pituitary Cells: A Dynamic Clamp Study.. ReScience.  ISSN 2430-3658.  5(1) . doi: 10.5281/zenodo.2611252 Fulltekst i vitenarkiv.
• 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 Fulltekst i vitenarkiv.
• 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

Se alle arbeider i Cristin

• Einevoll, Gaute (2020). Leveraging data, tools and models to gain new insights in mouse visual cortex.
• Einevoll, Gaute (2020). Why model brain signals?.
• Einevoll, Gaute & Spurkland, Anne (2020, 26. juni). Om coronaviruset. En oppdatering – med Anne Spurkland #21. [Internett].  Vett og Viten.
• Einevoll, Gaute & Spurkland, Anne (2020, 26. mars). Om koronaviruset – Med Anne Spurkland #14. [Internett].  Vett og Viten.
• Iannella, Nicolangelo Libero; Prydz, Kristian; Malthe-Sørensen, Anders; Einevoll, Gaute & Fyhn, Marianne (2020). A framework for modelling neuronal-ECM signalling.
• Mäki-Marttunen, Tuomo; Edwards, Andrew G; Einevoll, Gaute & Blackwell, Kim T (2020). Biochemically detailed single compartmental model for neocortical postsynaptic long term plasticity.
• Berthet, Pierre; Einevoll, Gaute & Ness, Torbjørn V (2019). Excitability of cortical neurons by electrical stimulation from the cortical surface.
• Cañada, Pablo Martinez; Ness, Torbjørn V; Einevoll, Gaute; Fellin, Tommaso & Panzeri, Stefano (2019). Computing the Electroencephalogram (EEG) from Point-Neuron Networks.
• Einevoll, Gaute (2019, 26. februar). Deltagelse i Abels Tårn (NRK P2, NRK2). [TV].  NRK.
• Einevoll, Gaute (2019, 13. september). Deltagelse i Abels Tårn, NRK P2. [Radio].  NRK.
• Einevoll, Gaute (2019, 08. mai). Deltagelse i Slettan & Martin tar helg i NRK P1. [Radio].  NRK.
• Einevoll, Gaute (2019). Deltagelse i paneldebatt om hjernen og den nevrovitenskapelige revolusjon.
• Einevoll, Gaute (2019, 16. mai). Deltagelse i podcasten Mentaltrener. [Internett].  Podcast.
• Einevoll, Gaute (2019, 24. september). Dommer i Forsker Grand Prix. [TV].  NRK.
• Einevoll, Gaute (2019). Evaluating the merit of candidate brain models with brain simulations.
• Einevoll, Gaute (2019, 07. mai). Hjerneforsker: Du lager din egen, personlige modell av virkeligheten. [Internett].  forskning.no.
• Einevoll, Gaute (2019, 01. juni). Kunstig syn - hjelper blinde å se igjen. [Fagblad].  Optikeren.
• Einevoll, Gaute (2019, 28. april). Lager kunstig tale av hjernesignaler. [Internett].  NRK Viten.
• Einevoll, Gaute (2019). Modeling brain signals.
• Einevoll, Gaute (2019). Neuroscience with both hands: Bridging scales with mathematics.
• Einevoll, Gaute (2019). Physics of the mind.
• Einevoll, Gaute (2019, 31. oktober). Podcast #1: Splitter ny podcast i podcasten Vett og vitenskap med Gaute Einevoll. [Internett].  Podcast.
• Einevoll, Gaute (2019). The scientific case for brain simulations.
• Einevoll, Gaute (2019). The too large role of statistics in neuroscience.
• Einevoll, Gaute (2019). Towards a brain theory.
• Einevoll, Gaute (2019). Towards biophysical psychiatry – basic principles and transformative potential.
• Einevoll, Gaute (2019). Towards multipurpose biophysics-based mathematical models of cortical circuits.
• Einevoll, Gaute (2019). Using AI to interpret electrical brain signals. Bernstein Feature 2019.
• Einevoll, Gaute & Hagen, Espen (2019). Tutorial: Biophysical modeling of extracellular potentials (using LFPy).
• Einevoll, Gaute & Nordengen, Kaja (2019, 06. november). Podcast #2 om hjernen i podcasten Vett og vitenskap med Gaute Einevoll. [Internett].  Podcast.
• Einevoll, Gaute & Sverdrup-Thygeson, Anne (2019, 28. november). Podcast #6 om insekter i podcasten Vett og vitenskap med Gaute Einevoll. [Internett].  Podcast.
• Einevoll, Gaute & Svihus, Birger (2019, 11. november). Podcast #3 om mat i podcasten Vett og vitenskap med Gaute Einevoll. [Internett].  Podcast.
• Einevoll, Gaute & Østby, Hilde (2019, 17. november). Podcast #5 om hukommelse i podcasten Vett og vitenskap med Gaute Einevoll. [Internett].  Podcast.
• Einevoll, Gaute & Øygard, Svein Harald (2019, 11. desember). Podcast #7 om å være Islands sentralbanksjef i podcasten Vett og vitenskap med Gaute Einevoll. [Internett].  Podcast.
• Fyhn, Marianne & Einevoll, Gaute (2019). Hjernen digitaliseres - hvordan moderne datateknologi brukes til å forstå biologiske mekanismer i hjernen.
• Hagen, Espen; Næss, Solveig; Ness, Torbjørn V & Einevoll, Gaute (2019). LFPy: Multimodal Modeling of Extracellular Neuronal Recordings in Python.
• Hagen, Espen; Stasik, Alexander Johannes; Billeh, Yazan N.; Siegle, Joshua H.; Dai, Kael; Rimehaug, Atle Eskeland; Ness, Torbjørn V; Røe, Malin Benum; Fyhn, Marianne; Hafting, Torkel; Koch, Christof; Olsen, Shawn R.; Einevoll, Gaute & Arkhipov, Anton (2019). Comparing spikes and the local field potential (LFP) in V1 between experimental data and a comprehensive biophysical model.
• Hagen, Espen; Stasik, Alexander Johannes; Billeh, Yazan; Siegle, Joshua H.; Dai, Kael; Rimehaug, Atle Eskeland; Ness, Torbjørn V; Røe, Malin Benum; Fyhn, Marianne; Hafting, Torkel; Koch, Christof; Olsen, Shawn R.; Einevoll, Gaute & Arkhipov, Anton (2019). Comparing spikes and the local field potential (LFP) in V1 between experimental data and a comprehensive biophysical model.
• Halnes, Geir & Einevoll, Gaute (2019). LFPy - exercises.
• Halnes, Geir; Tennøe, Simen; Einevoll, Gaute; Haug, Trude; Weltzien, Finn-Arne & Hodne, Kjetil (2019). A computational model of the spontaneous activity of gonadotrophin-releasing cells in the teleost fish models.
• Iannella, Nicolangelo Libero; Prydz, Kristian; Malthe-Sørensen, Anders; Einevoll, Gaute & Fyhn, Marianne (2019). A new framework for modelling neural-ECM signaling.
• Iannella, Nicolangelo Libero; Prydz, Kristian; Malthe-Sørensen, Anders; Einevoll, Gaute & Fyhn, Marianne (2019). A new framework for modelling neural-ECM signalling and interaction.
• Mäki-Marttunen, Tuomo; Einevoll, Gaute; Devor, Anna; Phillips, Wiliam; Dale, Anders M. & Andreassen, Ole Andreas (2019). A computational model of the spontaneous activity of gonadotropin-releasing cells in the teleos fish medaka.
• Næss, Solveig; Halnes, Geir; Hagen, Espen; Hagler, Donald J.; Dale, Anders M.; Einevoll, Gaute & Ness, Torbjørn V (2019). Biophysical modelling of EEG signals.
• Røe, Malin Benum; Aasebø, Ida Elisabeth Jørgensen; Mobarhan, Milad; Lensjø, Kristian Kinden; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2019). Stable orientation tuning in the freely moving rat: Movement-robust orientation-selective neurons in the deep layers of the primary visual cortex.
• Røe, Malin Benum; Aasebø, Ida Elisabeth Jørgensen; Mobarhan, Milad; Lensjø, Kristian Kinden; Einevoll, Gaute; Hafting, Torkel & Fyhn, Marianne (2019). Stable orientation tuning in the freely moving rat: Movement-robust orientation-selective neurons in the deep layers of the primary visual cortex.
• Røyne, Anja & Einevoll, Gaute (2019, 17. november). Vett og vitenskap: Om grunnstoffer. [Internett].  Podcast.
• Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute (2019). Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
• Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute (2019). Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
• Stasik, Alexander Johannes; Hagen, Espen; Skaar, Jan Eirik; Ness, Torbjørn V & Einevoll, Gaute (2019). Estimating parameters of biological neuronal networks using Convolutional Neural Networks.
• Sætra, Marte Julie; Einevoll, Gaute & Halnes, Geir (2019). A two-compartment neuron model with ion conservation and ion pumps.
• Aasebø, Ida Elisabeth Jørgensen; Fyhn, Marianne; Hafting, Torkel & Einevoll, Gaute (2018). Behavioural state and neuronal activity: how the freely moving paradigm can uncover novel response characteristics..
• 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.

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