Geir Halnes

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.

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.

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 validates a computational framework for modelling ion electrodiffusion during cortical spreading depression using experimental comparisons.

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

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

The paper introduces an electrodiffusive Pinsky-Rinzel model that accounts for ion concentration dynamics and homeostatic mechanisms, showing its utility in simulating pathological conditions like epilepsy.

The paper introduces a 3-D Kirchhoff-Nernst-Planck framework to model electrodiffusive effects around neurons, demonstrating its efficiency for large-scale simulations.

Uncertainpy provides a Python toolbox for uncertainty quantification and sensitivity analysis in neuroscience models, demonstrating its use on three case studies.

The paper uses a firing-rate model to analyze how cortical feedback affects visual responses in dLGN relay cells, identifying a specific configuration of excitatory and inhibitory feedback that best matches experimental data.