Hans Ekkehard Plesser: Research



NEST Developement of a versatile, efficient neuronal network simulator in collaboration with the NEST Initiative. The simulator is available for download from the NEST Initiative Homepage. Currently work is under way to improve simulator efficiency further, especially in parallel simulations.
Reproducibility With a number of colleagues, I have analyzed the lack of standards for model sharing and the resulting difficulties in reproducing results in computational neuroscience. In September 2011, I organized a workshop on the Creating, Documenting and Sharing Network Models together with Sharon Crook and Jim Bednar. I also participated in the INCF Program for Multiscale Modeling.


NEST Completely revised the way in which NEST generates connections, providing significant performance improvement, flexible parameterization of connections and extensibility (in collaboration with Hannah Bos and Jochen Eppler, FZ Jülich). 2014
NEST Record-breaking simulation of a network with 1.73 billion neurons and over 10 trillion synapses on the Japanse K supercomputer using NEST by German and Japanese collaborators. I contributed a crucial data structure for compact neuron representation. 2013
NEST Systematic statistical tests for correct generation of random connectivity in large neuronal networks (MSc thesis Daniel Hjertholm, co-supervised by Birgit Kriener). 2013
NEST Topology 3 Module for NEST released as part of NEST 2.2.0, providing support for 3D Networks and signficantly better scaling for > 10k CPUs (with Håkon Enger). 2012
NEST Significantly reduced memory overhead making very large network simulations possible on > 10k CPUs (with Susanne Kunkel, Tobias Potjans, Jochen Eppler, Abigail Morrison, and Markus Diesmann). 2011
NEST New Python interface and new user manual for NEST Topology Module; significantly enhanced user interest. 2010
Modelling Developed Connectivity Pattern Tables and ConnPlotter to visualize connectivity in complex networks. 2010
Modelling Analyzed model description practice and proposed Good Model Description Practice. 2009
NEST NEST now provides a multicompartment neuron and a smart new multimeter recorder. 2009
NEST NEST benchmarked on Norwegian national supercomputing resources Stallo, Titan and Hexagon. 2009
NEST NEST Topology Module makes it easy to create complex neuronal networks (with Kittel Austvoll). 2008
NEST Refactored NEST model code splitting data members into Parameters, State, Buffers, and Variables to enforce careful thinking, provide systematic initialization and reset and prepare for network serialization. 2008
NEST Completely refactored stimulation and recording devices in NEST. 2008
NEST Network connecting times in NEST reduced from quadratic to linear scaling by smart choice of data structure. 2007
NEST First million-neuron network simulation with NEST. 2007
NEST Achieved supralinear scaling in a prototype of the next-generation NEST kernel: run NEST on four processers and get five times as fast! 2004
NEST First Public NEST Release during the Advanced Course in Computational Neuroscience, Obidos, Portugal 2004
CoThaCo CoThaCo architecture defined, implementation in progress. 2003
Caspar Acquired funding for SMP Compute Server (1.7 mill NOK) and installed server as computing resource for my group, IMT and UMB. 2003
NEST Implemented conductance-based synapses in NEST (with my student Johan Hake). Ported NEST to IBM AIX, Linux for SGI Altix, and Mac OS X. 2003
Linear Thalamo-cortical model Complete linear model for the transfer function of relay cells in the dorsal LGN. With Gaute Einevoll. 2002
NEST Implemented new random number library in NEST. 2002
Escape Models Escape noise models equivalent to the leaky integrate-and-fire neuron, but mathematically more convenient. With Wulfram Gerstner. 2000
ModUhl Routines computing first-passage-time distributions of modulated Ornstein-Uhlenbeck processes. 2000
Stochastic Resonance Markov chain analysis of stochastic resonance in integrate-and-fire neurons using numerical methods instead of simulations. Demonstrated SR both in noise amplitude and signal frequency. See my thesis for details. 1999
Markov Classes A C-library for the efficient simulation of large stochastic systems, extending work by Thomas Fricke and Dietmar Wendt. 1995