Gaute T. Einevoll

This study investigates how different neuron types respond to transcranial electric stimulation across frequencies using biophysically detailed models, finding frequency-specific and cell-type-dependent effects.

This study presents a computational framework to explore the functional distinction between ionic and electric ephaptic effects on neuronal firing dynamics, showing they regulate population rates and spike timing differently.

This study develops a spiking network model of auditory novelty detection and shows how SCZ-related cellular changes impair mismatch negativity.

The paper applies the reciprocity theorem to predict neural responses to intra- and extra-cranial electric brain stimulation, showing frequency-dependent effects and spatial orientation dependencies.

The paper uses computational modeling to show schizophrenia-associated changes in ion-channel expression reduce delta-band power in neural simulations.

The paper introduces a kernel-based method to efficiently estimate LFPs in a mouse V1 model, showing external inputs dominate LFPs over local activity.

The paper evaluates the kernel method for predicting electric brain signals from population firing rates, showing its accuracy depends on kernel heterogeneity and spike-train correlations.

The paper develops a computational model of GABAB receptor signaling to study pre- and postsynaptic mechanisms of neuronal inhibition and validates it using electrophysiological data.

This paper introduces and validates Laminar Population Analysis (LPA), a method to decompose extracellular potentials in the mouse visual system by identifying contributions from different neural populations and pathways.

This paper introduces a multitask learning approach to predict membrane potentials in all neuron compartments, significantly speeding up simulations and enabling comparisons with experimental data.

The paper applies metamodels to a spiking neural network to approximate its dynamics and invert parameter distributions from simulation outputs.

This paper introduces NP Ultra, a high-density electrode that improves neuronal recording yield, detection, and cell type classification in mice.

The paper uses biophysical simulations of mouse primary visual cortex to show that current sinks and sources are shaped by both synaptic weights and dendritic placement, with distinct contributions from thalamocortical inputs and cortical feedback.

The paper uses computational modeling and post-mortem gene expression data to show that schizophrenia-associated genes impair PKA-mediated LTP in GluR1-containing synapses, linking genetic factors to synaptic plasticity deficits.

The paper analyzes neural activity in mouse visual cortex to show that population activity encodes visual scene rotations through a circular manifold structure.

The paper uses two-photon calcium imaging and surface electrophysiology to show that only a small fraction of layer 2 neurons follow surface MUA, suggesting MUA may primarily reflect axonal inputs.

This paper introduces a framework for predicting extracellular brain signals from neuronal network models, showing that linearized biophysical models can accurately capture spatiotemporal dynamics of these signals.

The paper shows cortical feedback sharpens receptive fields and increases surround suppression in mouse thalamus via visTRN neurons.

The paper measures oxygen consumption across cortical layers in awake mice and finds a decrease in baseline CMRO2 from layer I to IV, accompanied by increased tissue oxygenation.

This study investigates the functional effects of two CACNA1C mutations in zebrafish, showing altered mRNA levels, neurotransmitter profiles, and behavioral and electrophysiological changes linked to psychiatric disorders.

The paper introduces a synthetic voltage sensor and optogenetic actuator for all-optical electrophysiology in hiPSC-derived neurons, demonstrating its effectiveness in measuring activity across distinct genetic backgrounds.

The paper assesses LTP-like visual cortical plasticity in schizophrenia and bipolar disorder using VEP modulation, finding impaired N1b modulation in SZ and BDI but not BDII, linked to symptom severity.

This paper develops and validates EEG proxies derived from point-neuron network models to accurately estimate EEG signals for comparison with experimental data.

This paper introduces RippleNet, a deep LSTM-based model for detecting SPW-R events from LFP data, achieving high precision with minimal manual curation.

The paper introduces BMTK, an open-source software suite for multiscale brain circuit modeling, enabling simulations across different resolution levels with reduced coding requirements.

The paper introduces a new method to estimate spatially resolved CMRO2 from pO2 measurements using the Laplace method, balancing noise reduction and bias.

The paper examines VEP modulation in 415 healthy individuals, finding significant effects for several components and age-related correlations, supporting VEP as a measure of synaptic plasticity.

The paper develops a biochemically detailed computational model of cortical post-synaptic plasticity that integrates multiple signalling pathways and reproduces observed plasticity phenomena.

The paper introduces an electrodiffusive model of neuron-extracellular-glia interactions to simulate ion dynamics and pathological conditions like spreading depression.

MEArec simulates extracellular spiking activity to evaluate spike sorting algorithms by generating customizable ground-truth data with challenging features.