Gaute Tomas Einevoll

• Eissa, Ahmed; Fredrik Kismul, Jan; Pentz, Atle Bråthen; Elvsåshagen, Torbjørn; Metzner, Christoph & Akkouh, Ibrahim Ahmed [Vis alle 12 forfattere av denne artikkelen] (2026). Computational Modelling of Novelty Detection in the Mismatch Negativity Protocols and Its Impairments in Schizophrenia. European Journal of Neuroscience. ISSN 0953-816X. 63(6). doi: 10.1111/ejn.70453. Fulltekst i vitenarkiv
• Ness, Torbjørn V.; Koch, Christof & Einevoll, Gaute Tomas (2025). Predicting neural responses to intra- and extra-cranial electric brain stimulation by means of the reciprocity theorem. PLoS Computational Biology. ISSN 1553-734X. 21(12), s. e1013782–e1013782. doi: 10.1371/journal.pcbi.1013782. Fulltekst i vitenarkiv Vis sammendrag

  Electrical stimulation of nervous tissue is grounded in well-established biophysical principles, yet understanding its precise effect on neural activity remains challenging. This represents a major obstacle to both scientific and clinical applications of electrical brain stimulation. We here show that the reciprocity theorem of electromagnetism can be applied more broadly than previously acknowledged, providing a framework for elucidating the effects of electrical brain stimulation on neurons. Through this new perspective, we account for the observed weak frequency-dependence of extracellular electrical stimulation–induced membrane potential changes, and demonstrate a 1/ r falloff for nearby and 1/ r 2 falloff for distant neurons. We also show that for transcranial electrical stimulation, the susceptibility of a neuron to the stimulation is directly proportional to the size of the current dipole moment resulting from somatic current input. The induced somatic membrane potential changes are small and exhibit weak spatial but strong orientation dependency across human neocortex. The reciprocity-based approach to electrical stimulation reproduces a large body of previous experimental data, and provides new insights into how ES affects neurons.

• Ye, Zhiwen; Shelton, Andrew M.; Shaker, Jordan R.; Boussard, Julien; Colonell, Jennifer & Birman, Daniel [Vis alle 34 forfattere av denne artikkelen] (2025). Ultra-high-density Neuropixels probes improve detection and identification in neuronal recordings. Neuron. ISSN 0896-6273. 113(23), s. 3966–3982. doi: 10.1016/j.neuron.2025.08.030. Fulltekst i vitenarkiv Vis sammendrag

  To understand the neural basis of behavior, it is essential to sensitively and accurately measure neural activity at single-neuron and single-spike resolution. Extracellular electrophysiology delivers this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To minimize these limitations, we developed a silicon probe with much smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). Using NP Ultra, neuronal yield in mouse visual cortex recordings increased by more than 2-fold. With ultra-high spatial resolution, we discovered that a feature of extracellular waveforms, the spatial extent or “footprint,” distinguished axonal from somatic recordings. In addition, three genetically identified cortical cell types could be discriminated from one another with ∼80% accuracy and from other neurons with ∼85% accuracy. NP Ultra improves yield, detection of subcellular compartments, and cell type identification to enable a more powerful dissection of neural circuit activity during behavior.

• Mäki-Marttunen, Tuomo; Kismul, Jan Fredrik; Pajo, Kadri; Schulz, Jan Michael; Manninen, Tiina & Einevoll, Gaute Tomas [Vis alle 9 forfattere av denne artikkelen] (2025). Pre- and Post-synaptic Mechanisms of Neuronal Inhibition Assessed Through Biochemically Detailed Modeling of GABAB Receptor Signaling. Journal of Neuroscience. ISSN 0270-6474. 45(36). doi: 10.1523/JNEUROSCI.0544-25.2025. Fulltekst i vitenarkiv
• Meneghetti, Nicolò; Rimehaug, Atle Eskeland; Einevoll, Gaute; Mazzoni, Alberto & Ness, Torbjørn V. (2025). Kernel-based LFP estimation in detailed large-scale spiking network model of mouse visual cortex. Neural Networks. ISSN 0893-6080. 192, s. 1–22. doi: 10.1016/j.neunet.2025.107866. Fulltekst i vitenarkiv Vis sammendrag

  Simulations of large-scale neural activity are an increasingly important tool for investigating neural network activity. Calculating measurable brain signals, like the local field potential (LFP) from such simulations is crucial because it bridges the gap between model predictions and experimental observations and helps us better understand the information content of such signals. Accurately simulating LFPs from large-scale neural network models has, however, required highly biologically detailed models, which pose significant computational challenges, and have limited their practical application. In this study, we demonstrate that a kernel-based method can accurately and efficiently estimate the LFPs simulated in a highly detailed multicompartmental network model of the mouse primary visual cortex (V1), in response to both drifting gratings and full-field flashes of light. Beyond enabling computationally efficient and accurate LFP estimation, the kernel method also aids analysis by disentangling the contributions of individual neuronal populations to the LFP. Leveraging this capability, we found that the LFP in the mouse V1 model was dominated by external synaptic inputs: feedback from lateromedial visual areas in the upper visual layers, and thalamic afferents in layer 4. In contrast, local synaptic activity from V1 neuronal families contributed only marginally to the LFP. We further demonstrated how correlations between external and local neural activity could mask this insight in experimental data. Our findings demonstrate the kernel method as an accurate tool for LFP estimation in state-of-the-art large-scale network models. Moreover, we highlight its potential to reveal novel insights into the neural mechanisms that shape measurable brain signals.

• Ness, Torbjørn Vefferstad; Tetzlaff, Tom; Einevoll, Gaute & Dahmen, David (2025). On the validity of electric brain signal predictions based on population firing rates. PLoS Computational Biology. ISSN 1553-734X. 21(4), s. 1–29. doi: 10.1371/journal.pcbi.1012303. Fulltekst i vitenarkiv Vis sammendrag

  Neural activity at the population level is commonly studied experimentally through measurements of electric brain signals like local field potentials (LFPs), or electroencephalography (EEG) signals. To allow for comparison between observed and simulated neural activity it is therefore important that simulations of neural activity can accurately predict these brain signals. Simulations of neural activity at the population level often rely on point-neuron network models or firing-rate models. While these simplified representations of neural activity are computationally efficient, they lack the explicit spatial information needed for calculating LFP/EEG signals. Different heuristic approaches have been suggested for overcoming this limitation, but the accuracy of these approaches has not fully been assessed. One such heuristic approach, the so-called kernel method, has previously been applied with promising results and has the additional advantage of being well-grounded in the biophysics underlying electric brain signal generation. It is based on calculating rate-to-LFP/EEG kernels for each synaptic pathway in a network model, after which LFP/EEG signals can be obtained directly from population firing rates. This amounts to a massive reduction in the computational effort of calculating brain signals because the brain signals are calculated for each population instead of for each neuron. Here, we investigate how and when the kernel method can be expected to work, and present a theoretical framework for predicting its accuracy. We show that the relative error of the brain signal predictions is a function of the single-cell kernel heterogeneity and the spike-train correlations. Finally, we demonstrate that the kernel method is most accurate for contributions which are also dominating the brain signals: spatially clustered and correlated synaptic input to large populations of pyramidal cells. We thereby further establish the kernel method as a promising approach for calculating electric brain signals from large-scale neural simulations.

• Beshkov, Kosio & Einevoll, Gaute (2024). [2404.19710] A rank decomposition for the topological classification of neural representations. arXiv. Fulltekst i vitenarkiv Vis sammendrag

  Neural networks can be thought of as applying a transformation to an input dataset. The way in which they change the topology of such a dataset often holds practical significance for many tasks, particularly those demanding non-homeomorphic mappings for optimal solutions, such as classification problems. In this work, we leverage the fact that neural networks are equivalent to continuous piecewise-affine maps, whose rank can be used to pinpoint regions in the input space that undergo non-homeomorphic transformations, leading to alterations in the topological structure of the input dataset. Our approach enables us to make use of the relative homology sequence, with which one can study the homology groups of the quotient of a manifold $\mathcal{M}$ and a subset $A$, assuming some minimal properties on these spaces. As a proof of principle, we empirically investigate the presence of low-rank (topology-changing) affine maps as a function of network width and mean weight. We show that in randomly initialized narrow networks, there will be regions in which the (co)homology groups of a data manifold can change. As the width increases, the homology groups of the input manifold become more likely to be preserved. We end this part of our work by constructing highly non-random wide networks that do not have this property and relating this non-random regime to Dale's principle, which is a defining characteristic of biological neural networks. Finally, we study simple feedforward networks trained on MNIST, as well as on toy classification and regression tasks, and show that networks manipulate the topology of data differently depending on the continuity of the task they are trained on.

• Verhellen, Jonas; Beshkov, Kosio; Amundsen, Sebastian; Ness, Torbjørn V & Einevoll, Gaute (2024). Multitask learning of a biophysically-detailed neuron model. PLoS Computational Biology. ISSN 1553-734X. 20(7). doi: 10.1371/journal.pcbi.1011728. Fulltekst i vitenarkiv Vis sammendrag

  The human brain operates at multiple levels, from molecules to circuits, and understanding these complex processes requires integrated research efforts. Simulating biophysically-detailed neuron models is a computationally expensive but effective method for studying local neural circuits. Recent innovations have shown that artificial neural networks (ANNs) can accurately predict the behavior of these detailed models in terms of spikes, electrical potentials, and optical readouts. While these methods have the potential to accelerate large network simulations by several orders of magnitude compared to conventional differential equation based modelling, they currently only predict voltage outputs for the soma or a select few neuron compartments. Our novel approach, based on enhanced state-of-the-art architectures for multitask learning (MTL), allows for the simultaneous prediction of membrane potentials in each compartment of a neuron model, at a speed of up to two orders of magnitude faster than classical simulation methods. By predicting all membrane potentials together, our approach not only allows for comparison of model output with a wider range of experimental recordings (patch-electrode, voltage-sensitive dye imaging), it also provides the first stepping stone towards predicting local field potentials (LFPs), electroencephalogram (EEG) signals, and magnetoencephalography (MEG) signals from ANN-based simulations. While LFP and EEG are an important downstream application, the main focus of this paper lies in predicting dendritic voltages within each compartment to capture the entire electrophysiology of a biophysically-detailed neuron model. It further presents a challenging benchmark for MTL architectures due to the large amount of data involved, the presence of correlations between neighbouring compartments, and the non-Gaussian distribution of membrane potentials.

• Rimehaug, Atle Eskeland; Dale, Anders M.; Arkhipov, Anton & Einevoll, Gaute Tomas (2024). Uncovering population contributions to the extracellular potential in the mouse visual system using Laminar Population Analysis. BioRxiv. ISSN 0362-4331. 20(12). doi: 10.1371/journal.pcbi.1011830. Fulltekst i vitenarkiv
• Amunts, Katrin; Axer, Markus; Banerjee, Swati; Bitsch, Lise; Bjaalie, Jan G. & Brauner, Philipp [Vis alle 50 forfattere av denne artikkelen] (2024). The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing. Imaging Neuroscience. 2. doi: 10.1162/imag_a_00137. Fulltekst i vitenarkiv Vis sammendrag

  In recent years, brain research has indisputably entered a new epoch, driven by substantial methodological advances and digitally enabled data integration and modelling at multiple scales—from molecules to the whole brain. Major advances are emerging at the intersection of neuroscience with technology and computing. This new science of the brain combines high-quality research, data integration across multiple scales, a new culture of multidisciplinary large-scale collaboration, and translation into applications. As pioneered in Europe’s Human Brain Project (HBP), a systematic approach will be essential for meeting the coming decade’s pressing medical and technological challenges. The aims of this paper are to: develop a concept for the coming decade of digital brain research, discuss this new concept with the research community at large, identify points of convergence, and derive therefrom scientific common goals; provide a scientific framework for the current and future development of EBRAINS, a research infrastructure resulting from the HBP’s work; inform and engage stakeholders, funding organisations and research institutions regarding future digital brain research; identify and address the transformational potential of comprehensive brain models for artificial intelligence, including machine learning and deep learning; outline a collaborative approach that integrates reflection, dialogues, and societal engagement on ethical and societal opportunities and challenges as part of future neuroscience research.

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• Sterratt, David; Graham, Bruce; Gillies, Andrew; Einevoll, Gaute Tomas & Willshaw, David (2024). Principles of Computational Modelling in Neuroscience (2nd ed.). Cambridge University Press. ISBN 9781108483148. 535 s. Fulltekst i vitenarkiv
• Halnes, Geir; Ness, Torbjørn V; Næss, Solveig; Hagen, Espen; Pettersen, Klas Henning & Einevoll, Gaute Tomas (2024). Electric Brain Signals - Foundations and Applications of Biophysical Modeling. Cambridge University Press. ISBN 9781009039826. 400 s. Fulltekst i vitenarkiv

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• Stänicke, Erik; Solms, Mark & Einevoll, Gaute Tomas (2026). Er moderne hjerneforskning i ferd med å gi Freud rett? [Journal]. Morgenbladet. doi: https:/www.morgenbladet.no/samfunn/er-moderne-hjerneforskning-i-ferd-med-a-gi-freud-rett/10483327. Fulltekst i vitenarkiv
• Einevoll, Gaute (2026). Modeling of electric brain signals (and stimulation). Fulltekst i vitenarkiv
• Einevoll, Gaute (2026). How to do interdisciplinary research. Fulltekst i vitenarkiv
• Einevoll, Gaute (2026). Power laws in electric brain signals: a sign of criticality? Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #99: Om oljelandet i klimakampen - med Anne Karin Sæther. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #100: Om nevroteknologi og mitt liv i akademia - solo jubileumsepisode. Fulltekst i vitenarkiv
• Einevoll, Gaute & Bondi, Johan Fridtjof (2025). #101: Om kirurgi - med Johan Bondi. Fulltekst i vitenarkiv
• Einevoll, Gaute & Bakken, Lars (2025). #102: Om bakterier som fjerner klimagasser fra landbruket - med Lars Bakken. Fulltekst i vitenarkiv
• Einevoll, Gaute & Knutsen, Carl Henrik (2025). #103: Om å Trump-sikre demokratiet i Norge { med Carl Henrik Knutsen. Fulltekst i vitenarkiv
• Einevoll, Gaute & Valseth, Eirik (2025). #104: Om matematisk vær- og stormvarsling - med Eirik Valseth. Fulltekst i vitenarkiv
• Einevoll, Gaute & Paus-Knudsen, Julie Sørlie (2025). #105: Om intelligensen til bier og humler - med Julie Sørlie Paus-Knudsen. Fulltekst i vitenarkiv
• Einevoll, Gaute & Gravensteen, Ida Kathrine (2025). #106: Om rettsmedisin - med Ida Gravensteen. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #107: Bonusepisode: Utvikle, ikke avvikle. Fulltekst i vitenarkiv
• Einevoll, Gaute & Innset, Ola (2025). #108: Om penger, skatt og kampen om verdiene { med Ola Innset. Fulltekst i vitenarkiv
• Einevoll, Gaute & Elgarøy, Øystein (2025). #109: Om hva som skjedde før Big Bang - med  Øystein Elgarøy. Fulltekst i vitenarkiv
• Einevoll, Gaute & Sandtorv, Alexander Harald (2025). #110: Om gleder og sorger med plast - med Alexander Sandtorv. Fulltekst i vitenarkiv
• Einevoll, Gaute & Røislien, Jo (2025). #111: Om å TENKe før du snakker - med Jo Røislien. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Om mitt liv som narkoman - med Knut Martin Folkestad - #112. [Internet]. Vett og vitenskap. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #23: On human whole-brain models - with Viktor Jirsa. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #24: On neuroscience foundation models - with Andreas Tolias. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #25: On growing synthetic dendrites - with Hermann Cuntz. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #26: On the population code in visual cortex - with Kenneth Harris. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #27: On construction and clinical use of multipurpose neuron models - with Etay Hay. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #28: On whole-cell modeling of bacteria - with Markus Covert. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #29: On the philosophy of simplification in computational neuroscience - with Mazviita Chirimuuta and Terrence Sejnowski. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #30: On co-dependent excitatory and inhibitory plasticity - with Tim Vogels. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #31: On how to cure brain diseases - with Nicole Rust. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #32: On IIT and adversarial testing of consciousness theories - with Christof Koch. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #33: On computational neurotechnology for the clinic - with Anthony Burkitt, Nada Yousif and Esra Neufeld. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #34: On balanced neural networks - with Nicolas Brunel. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). #35: On modeling metabolic networks in the brain with Polina Shichkova. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Deltagelse i radioprogrammet Abels Tårn. [Radio]. NRK P2. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Deltagelse i radioprogrammet Abels Tårn. [Radio]. NRK P2. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Deltagelse i radioprogrammet Abels Tårn. [Radio]. NRK P2. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Deltagelse i radioprogrammet Abels Tårn. [Radio]. NRK P2. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Deltagelse i radioprogrammet Abels Tårn. [Radio]. NRK P2. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). To keep or not to keep: Neurophysiology's data dilemma. [Journal]. The Transmitter. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Hvorfor jeg tror på klimavitenskapen? Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Bevissthet. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Nevroteknologi - kan tanker avlyttes og styres", Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Modeling electric brain signals and stimulation. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Kan vi regne ut hvordan vi tenker? Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Hva er god vitenskap? Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Modeling of electric brain signals (and stimulation). Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Modeling of electric brain signals (and stimulation). Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Biophysical modeling of electric signals in the brain. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Predicting neural responses to intra- and extracranial electric brain stimulation by means of the reciprocity theorem. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Modeling of electric brain signals (and stimulation). Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Predicting neural responses to intra- and extracranial electric brain stimulation by means of the reciprocity theorem. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Biophysical models for prediction of neural population dynamics. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). From biological intelligence (BI) to AI and back. Fulltekst i vitenarkiv
• Einevoll, Gaute (2025). Why Simulate the Brain? The future of Brain-Scale Models. Fulltekst i vitenarkiv
• Ness, Torbjørn V; Koch, Christof & Einevoll, Gaute (2025). Predicting neural responses to intra-and extra-cranial electric brain stimulation by means of the reciprocity theorem. Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). Biophysical modeling of electric and magnetic brain signals: Applications. Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). Simulating the brain. Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). Kan bevissthet forst as fra fysikkbaserte simuleringer? Fulltekst i vitenarkiv
• Grøtan, Åshild & Einevoll, Gaute Tomas (2024). Vett og Vitenskap med Gaute Einevoll - Podcast #87: Om strømmarkedet – med Åshild Grøtan. [Internet]. https://vettogvitenskap.no/vov87/. Fulltekst i vitenarkiv
• Kismul, Jan Fredrik; Ness, Torbjørn V; Einevoll, Gaute; Linne, Marja Leena & Mäki-Marttunen, Tuomo (2024). Modeling the effects of schizophrenia related ion-channel encoding genes on P50 suppression. Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). Modeling of electric brain signals and stimulation. Fulltekst i vitenarkiv
• Einevoll, Gaute & Joner, Erik (2024). Om jord - med Erik Joner, 30.08.2024. [Internet]. Podcast "Vett og vitenskap". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #97: Om norsk toppolitikk - med Trine Skei Grande. [Internet]. Podcast "Vett og vitenskap". Fulltekst i vitenarkiv
• Einevoll, Gaute & Barua, Ishita (2024). #95: Om bruk av AI i helsevesenet - med Ishita Barua. [Internet]. Podcast "Vett og vitenskap". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #98: Om informasjonsbevaring i sorte hull - med Åsmund Folkestad. [Internet]. Podcast "Vett og vitenskap". Fulltekst i vitenarkiv
• Einevoll, Gaute & Bernstein, Ralph William (2024). #96: Om databrikker - med Ralph Bernstein. [Internet]. Podcast "Vett og vitenskap". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #90: Om vitenskap og samfunn - soloepisode. [Internet]. Podcast "Vett og vitenskap". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #7: On topological data analysis and Hofield-like network models - with Carina Curto. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #6: On central pattern generators in the spinal cord - with Henrik Linden. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #18: On electric brain signals - solo episode. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #13: On quantum biology - with Johnjoe McFadden. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #11: On synaptic learning rules for spiking neurons - with Friedemann Zenke. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #14: On the molecular memory code - with Sam Gershman. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). Modeling of electric brain signals and stimulation. Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). Om matematisk nevrovitenskap. Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). AI og kiropraktikk. Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). Modeling electric brain signals. Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). Biologisk og kunstig intelligens. Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). Towards multipurpose biophysics-based models of cortical circuits. Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #16: On consciousness - with Christof Koch. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #9: On origins of computational neuroscience and AI as scientific  fields - with Terrence Sejnowski. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #8: On reverse engineering of the roundworm C.elegans - with Konrad Kording, [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #12: On modeling of signaling pathways inside the neuron - with Avrama Blackwell. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #15: On the simulation tool NEURON - with Michael Hines. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #17: On dendritic function - with Yiota Poirazi. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). #10: On large scale modeling of mouse primary visual cortex - with Anton Arkhipov. [Internet]. Podcast "Theoretical Neuroscience Podcast". Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). Modeling of electric brain signals and stimulation. Fulltekst i vitenarkiv
• Einevoll, Gaute (2024). Aksjonærer har mistet troen på evighetsmaskin: Ville vært århundrets sensasjon. [Journal]. Teknisk Ukeblad. Fulltekst i vitenarkiv
• Boccara, Charlotte; Einevoll, Gaute & Nikolaisen, Myrland, Henriette (2025). Methods for Sleep Deprivation in Laboratory Rodent Models: A Systematic Review. Norges miljø- og biovitenskapelige universitet (NMBU). Fulltekst i vitenarkiv Vis sammendrag

  Sleep is an essential and conserved biological function, yet its specific functions remain difficult to pinpoint, and much of our current knowledge on the subject comes from clinical studies. While these studies were essential to establish what general functions sleep may support, they often fail to identify the underlying mechanisms that explain why sleep is essential to health or demonstrate causal links between sleep issues and pathological outcomes. Rodent sleep deprivation models offer a unique opportunity to study these mechanisms and establish how sleep perturbations may directly cause dysfunction. However, rodent sleep deprivation methods to perturbate sleep differ in precision, efficiency and interpretability, and a comprehensive, comparative overview has been lacking. My aim with this thesis is to address that knowledge gap by systematically cataloguing and comparing different rodent sleep deprivation methods. I conducted a systematic review and meta-analysis of 100 papers and 161 experiments to compare mechanical paradigms, lesions, pharmacologic interventions, gene editing, optogenetics, and chemogenetics across wake, NREM and REM sleep. I quantified method effectiveness by sleep state and mapped typical brain area targets to identify how these choices may shape outcomes. The analyses reveal a precision-efficiency trade-off. Tools that target specific areas and circuits tend to be less efficient in wake and sleep-state changes, whereas broad approaches produce larger but less selective effects. I integrated these findings into a decision framework to align method selection with research aims, emphasizing state targeting goals, anatomical targeting and practical constraints. Overall, this thesis offers a structured, transparent comparison of rodent sleep deprivation tools, balancing precision against the magnitude and reliability of sleep state-specific effects. It sets the necessary framework to outline future priorities for sleep deprivation-based research that I predict likely to focus on naturalistic design, transparency and broader coverage across development and sex.

• Vang, Georg; Einevoll, Gaute Tomas; Stasik, Alexander Johannes & Beshkov, Kosio (2024). Exploration of reservoir computing and artificial neural network architectures as saliency detectors. Norges miljø- og biovitenskapelige universitet (NMBU). Fulltekst i vitenarkiv Vis sammendrag

  This study explores the performance of reservoir-type and conventional artificial neural networks as saliency detectors, inspired by Li Zhaoping's hypothesis regarding the V1's role as a saliency detector. A comparative analysis evaluates various models on a simple input image time series task, focusing on their effectiveness in training and out-of-sample validation data. Results indicate that while reservoir models exhibit higher training data loss compared to non-reservoir networks, they outperform other models on new data. Modifications to the reservoir structure show promise in improving both loss and Intersection over Union (IoU) score performance on the training and out-of-sample validation data. However, the study does not find that imposing a more visual cortex-like structure in the reservoir enhances its performance. Further analysis suggests that while reservoir models offer advantages such as cost-effectiveness and robustness for changes in input data, they may lack the precision of more trainable models like CNNs. Limitations to the thesis include the scope of hyperparameter exploration and the lack of overfitting mitigation techniques like the use of dropout. Future research should focus on refining all models to measure the potential performance on both training and out-of-sample data. This includes exploring ensemble methods and improving weight initialization techniques to enhance model precision and adaptability, leading to advancements in various real-world applications.

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