Torbjørn V. Ness

This paper challenges the assumption that local field potentials (LFPs) universally reflect population activity by showing that their interpretability depends on neuronal morphology and synchronization state in the subthalamic nucleus.

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.

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.

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.

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

The paper examines how perineuronal net disruption affects firing rate via CaHVA and SK currents in neuron models, finding SK currents have a larger impact.

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.

This paper introduces a 3D neuro-microphysiological system with microelectrodes for high-throughput electrical recording and analysis of human neurons.

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 presents a method to compute current dipoles from simulated neural activity to model EEG and MEG signals with biophysical detail.

This paper uses convolutional neural networks to estimate synaptic connection weights in a neuronal network model from local field potential (LFP) signals.

LFPy calculates extracellular brain signals from simulated neuron models using Python and NEURON.

This paper assesses how neural probe shape and size affect extracellular potentials and introduces a correction method to account for their influence.

The paper extends LFPy to model neural networks and compute LFP, ECoG, EEG, and MEG signals using biophysically accurate forward models and head models.

The paper derives and corrects analytical formulas for scalp potentials in the four-sphere EEG head model and compares them with FEM simulations.