Computational Neuroscience Group, UMB, Ås

Summary of activity

  • Modeling of neurons and neural networks at various levels of biological detail, that is, using compartmental models, simplified spiking-neuron models and firing-rate models. The modeling is aimed both at understanding the signal processing properties of particular neural systems (i.e., the early visual system in mammals, the rat somatosensory cortex, and the hippocampal formation) and generic properties of cortical network models.
  • Development of the neural network simulation tool NEST tailored for simulations of large network of simplified spiking neurons and descriptive high-level languages for brain models to facilitate easier and more reliable application of network simulators.
  • Development of new methods for the interpretation of both the high-frequency (MUA) and low-frequency (LFP) parts of potentials recorded in vivo in rat barrel cortex with multicellular electrodes, e.g., inverse current-source density method (iCSD) and laminar population analysis (LPA).

Contact information

Computational Neuroscience Group, Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences (UMB), P.O. Box 5003, 1432 Ås.
See arken.umb.no/compneuro

Senior personnel

Professor Gaute T. Einevoll
Assoc. professor Hans E. Plesser
Professor John Wyller

Adjunct Associate Professor (20%):
Dr. Mahmood Amiry-Moghaddam

Personnel's neuroinformatic skills/background

Professor Gaute T. Einevoll (computational neuroscience, physics)
Assoc. professor Hans E. Plesser (computational neuroscience, computer science)
Professor John Wyller (computational neuroscience, applied mathematics)

Modeling tools

NEST (www.nest-initiative.org) - simulation of large networks of simplified spiking neurons
NEURON (neuron.duke.edu) - compartmental modelling of biophysically realistic neuron models
XPP (www.math.pitt.edu/~bard/xpp/xpp.html) - nonlinear systems analysis
CSDplotter (arken.umb.no/~compneuro/iCSD_download.php) - tool for estimation of current source density from local field potentials recorded with laminar multielectrodes

Key publications

  • Amplitude variability and extracellular low-pass filtering of neuronal spikes K.H. Pettersen and G.T. Einevoll. Biophysical Journal (in press).
  • Estimation of population firing rates and current source densities from laminar electrode recordings K.H. Pettersen, E. Hagen and G.T. Einevoll. Journal of Computational Neuroscience (in press).
  • Ø. Nordbø, J. Wyller and G.T. Einevoll: Neural network firing-rate models on integral form - Effects of temporal coupling kernels on equilibrium-state stability, Biological Cybernetics (2007).
  • G.T. Einevoll, K.H. Pettersen, A. Devor, I. Ulbert, E. Halgren, A.M. Dale: Laminar Population Analysis: Estimating firing rates and evoked synaptic activity from multielectrode recordings in rat barrel cortex, Journal of Neurophysiology 97, 2174-2190 (2007).
  • J. Wyller, P. Blomquist, and G.T. Einevoll: Turing instability and pattern formation in a two-population neuronal network model, Physica D 225, 75-93 (2007).
  • A. Morrison, S. Straube, H. E. Plesser, and M. Diesmann. Exact subthreshold integration with continuous spike times in discrete time neural network simulations, Neural Computation 19:47-79 (2007).
  • T. Solstad, E.I. Moser, and G.T. Einevoll: From grid cells to place cells: a mathematical model, Hippocampus 154, 1026-1031 (2006).
  • K. Pettersen, A. Devor, I. Ulbert, A.M. Dale and G.T. Einevoll: Current-source density estimation based on inversion of electrostatic forward solution: Effects of finite extent of neuronal activity and conductivity discontinuities, Journal of Neuroscience Methods 154, 116-133 (2006)
  • P. Blomquist, J. Wyller and G.T. Einevoll: Localized activity patterns in two-population neuronal networks, Physica D 206, 180-212 (2005)
  • G.T. Einevoll and H. E. Plesser: Response of the difference-of-Gaussians model to circular drifting-grating patches, Visual Neuroscience 22, 437-446 (2005)
  • G.T. Einevoll and H. E. Plesser: Linear mechanistic models for the dorsal lateral geniculate nucleus of cat probed using drifting-grating stimuli, Network: Comput. Neural Systems 13, 503-530 (2002).
  • G.T. Einevoll and P. Heggelund: Mathematical models for the spatial receptive-field organization of nonlagged X cells in dorsal lateral geniculate nucleus of cat, Visual Neuroscience 17, 871-886 (2000).