Gaute Einevoll

• 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
• 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
• 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
• 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
• 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.
• Glabska, Helena T.; Norheim, Eivind Skjønsberg; Devor, Anna; Dale, Anders M.; Einevoll, Gaute & Wòjcik, Daniel K. (2016). Generalized laminar population analysis (gLPA) for interpretation of multielectrode data from cortex. Frontiers in Neuroinformatics.  ISSN 1662-5196.  10 . doi: 10.3389/fninf.2016.00001 Fulltekst i vitenarkiv.
• Hagen, Espen; Dahmen, David; Stavrinou, Maria; Linden, Henrik; Tetzlaff, Tom; Van Albada, Sacha; Gruen, Sonja; Diesmann, Markus & Einevoll, Gaute (2016). Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.. Cerebral Cortex.  ISSN 1047-3211.  26(12), s 4461- 4496 . doi: 10.1093/cercor/bhw237 Fulltekst i vitenarkiv. Vis sammendrag

  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 ∼1 mm2 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.

• Halnes, Geir; Mäki-Marttunen, Tuomo; Keller, Daniel; Pettersen, Klas; Andreassen, Ole Andreas & Einevoll, Gaute (2016). Effect of Ionic Diffusion on Extracellular Potentials in Neural Tissue. PloS Computational Biology.  ISSN 1553-734X.  12(11) . doi: 10.1371/journal.pcbi.1005193 Fulltekst i vitenarkiv.
• Heiberg, Thomas; Hagen, Espen; Halnes, Geir & Einevoll, Gaute (2016). Biophysical Network Modelling of the dLGN Circuit: Different Effects of Triadic and Axonal Inhibition on Visual Responses of Relay Cells. PloS Computational Biology.  ISSN 1553-734X.  12(5) . doi: 10.1371/journal.pcbi.1004929 Fulltekst i vitenarkiv.
• Mäki-Marttunen, Tuomo; Halnes, Geir; Devor, Anna; Witoelar, Aree; Bettella, Francesco; Djurovic, Srdjan; Wang, Yunpeng; Einevoll, Gaute; Andreassen, Ole Andreas & Dale, Anders M. (2016). Functional effects of schizophrenia-linked genetic variants on intrinsic single-neuron excitability: a modeling study. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.  ISSN 2451-9022.  1(1), s 49- 59 . doi: 10.1016/j.bpsc.2015.09.002 Vis sammendrag

  BACKGROUND: Recent genome-wide association studies have identified a large number of genetic risk factors for schizophrenia (SCZ) featuring ion channels and calcium transporters. For some of these risk factors, independent prior investigations have examined the effects of genetic alterations on the cellular electrical excitability and calcium homeostasis. In the present proof-of-concept study, we harnessed these experimental results for modeling of computational properties on layer V cortical pyramidal cells and identified possible common alterations in behavior across SCZ-related genes. METHODS: We applied a biophysically detailed multicompartmental model to study the excitability of a layer V pyramidal cell. We reviewed the literature on functional genomics for variants of genes associated with SCZ and used changes in neuron model parameters to represent the effects of these variants. RESULTS: We present and apply a framework for examining the effects of subtle single nucleotide polymorphisms in ion channel and calcium transporter-encoding genes on neuron excitability. Our analysis indicates that most of the considered SCZ-related genetic variants affect the spiking behavior and intracellular calcium dynamics resulting from summation of inputs across the dendritic tree. CONCLUSIONS: Our results suggest that alteration in the ability of a single neuron to integrate the inputs and scale its excitability may constitute a fundamental mechanistic contributor to mental disease, alongside the previously proposed deficits in synaptic communication and network behavior. Keywords: Biophysical modeling, Functional genomics, Genome-wide association studies, Layer V pyramidal cell, Neuron excitability, Schizophrenia

• Ness, Torbjørn Vefferstad; Remme, Michiel W H & Einevoll, Gaute (2016). Active subthreshold dendritic conductances shape the local field potential. Journal of Physiology.  ISSN 0022-3751.  594(13), s 3809- 3825 . doi: 10.1113/JP272022

Se alle arbeider i Cristin

• 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.
• 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.
• 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.
• 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.
• 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.
• Einevoll, Gaute (2016). Modeling the brain.
• Einevoll, Gaute (2016, 18. april). Modellerer hjernen som en elektrokjemisk maskin. [Internett].  Titan.uio.no.
• Einevoll, Gaute (2016). Nytt fra hjerneforskningen.
• Einevoll, Gaute (2016, 17. april). Nå er fysikere blitt hjerneforskere: Modellerer hjernen som en elektrokjemisk maskin. [Fagblad].  tu.no.
• Einevoll, Gaute (2016, 22. september). Når kjemien stemmer, episode 5. [TV].  NRK1.
• Einevoll, Gaute (2016). Programleder for lansering av "Hjernen er stjernen".
• Einevoll, Gaute (2016, 12. desember). UiO-forskere undersøker om Donald Trump er julenissen. [Internett].  Titan.uio.no.
• Einevoll, Gaute (2016). What can the local field potential (LFP) tell us about cortical network activity?.
• Einevoll, Gaute (2016). What can the local field potential (LFP) tell us about the cortical network activity?.
• Einevoll, Gaute (2016). What can the local field potential (LFP) tell us about the cortical network activity?.
• Einevoll, Gaute (2016). What can we learn from the local field potential?.
• Halnes, Geir; Mäki-Marttunen, Tuomo; Pettersen, Klas; Keller, Daniel; Andreassen, Ole Andreas & Einevoll, Gaute (2016). Effects of ionic diffusion on power spectra of local field potentials (LFP).
• Lepperød, Mikkel Elle; Wigestrand, Mattis B; Frey, M.M.; Lensjø, Kristian Kinden; Einevoll, Gaute; Solstad, Trygve; Fyhn, Marianne & Hafting, Torkel (2016). Role of input-specific perturbations of medial septal projections to the grid cell network.
• Lepperød, Mikkel Elle; Wigestrand, Mattis B; Frey, Mina Martine; Lensjø, Kristian Kinden; Einevoll, Gaute; Solstad, Trygve; Fyhn, Marianne & Hafting, Torkel (2016). On the role of input-specific perturbations of medial septal projections to the grid cell network.
• Lepperød, Mikkel Elle; Wigestrand, Mattis B; Lensjø, Kristian Kinden; Frey, M.M.; Dragly, Svenn-Arne; Einevoll, Gaute; Solstad, Trygve; Fyhn, Marianne & Hafting, Torkel (2016). Grid cell network resilient to input-specific medial septum perturbations.
• Ness, Torbjørn Vefferstad; Chintaluri, Chaitanya H.; Potworowski, Jan; Łęski, Szymon; Glabska, Helena T.; Wojcik, Daniel & Einevoll, Gaute (2016). Modelling And Analysis Of Electrical Potentials Recorded In Microelectrode Arrays (MEAs).
• Ness, Torbjørn Vefferstad; Remme, Michiel W. H. & Einevoll, Gaute (2016). Active subthreshold dendritic conductances shape the Local Field Potential.
• Ness, Torbjørn Vefferstad; Remme, Michiel W. H. & Einevoll, Gaute (2016). Active subthreshold dendritic conductances shape the local field potential.

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