The cortical neuronal network is among the most complex structures found in nature. The functional role of its dynamics exhibited on many spatio-temporal scales is presently not understood. Furthermore, in contrast to other systems, the structure of the cortex is in fact not static but undergoes a continuous activity dependent reorganization. The Diesmann Research Unit studies the mechanisms and functional consequences of spike synchronization and plasticity in biologically realistic models of the cortical network. However, this bottom-up approach alone may not lead to an understanding of brain function. For this reason we also incorporate top-down approaches in our research. At the interface of top-down and bottom up approaches, our strategy is to implement established formal theories of system function like temporal-difference learning in biologically constrained network models. These investigations depend on large-scale simulations requiring non-standard algorithms and high-performance parallel computing. Therefore, the unit is also concerned with the creation of appropriate simulation technology.

See Projects and also the NEST Initiative for detailed descriptions.

Selected Publications

• Schrader S, Diesmann M, Morrison A (2010). A compositionality machine realized by a hierarchic architecture of synfire chains. Front. Comput. Neurosci. 4:154
• Louis S, Gerstein GL, Grün S, Diesmann M (2010). Surrogate spike train generation through dithering in operational time. Front Comput Neurosci 4:127. doi: 10.3389/fncom.2010.00127 (free)
• Bruederle et al. (2010). A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems arXiv:1011.2861v1
• Potjans W, Morrison A and Diesmann M (2010). Enabling functional neural circuit simulations with distributed computing of neuromodulated plasticity. Front. Comput. Neurosci. 4:141 (free)
• Hanuschkin A, Herrmann J M, Morrison A and Diesmann M (2010). Compositionality of arm movements can be realized by propagating synchrony. Journal of Computational Neuroscience. doi:10.1007/s10827-010-0285-9
• Helias M, Deger M, Rotter S, Diesmann M (2010) Instantaneous Non-Linear Processing by Pulse-Coupled Threshold Units PLoS Comput Biol 6(9): e1000929
• Hanuschkin A, Kunkel S, Helias M, Morrison A and Diesmann M (2010) A general and efficient method for incorporating precise spike times in globally time-driven simulations Front. Neuroinform. 4:113. doi:10.3389/fninf.2010.00113 (free)
• Helias M, Deger M, Diesmann M, Rotter S (2010) Equilibrium and response properties of the integrate-and-fire neuron in discrete time Front Comput Neurosci 3, 29 doi:10.3389/neuro.10.029.2009 (free)
• Nordlie E, Plesser HE (2010) Visualizing neuronal network connectivity with connectivity pattern tables Front Neuroinform 3:39 (free)
• Djurfeldt M, Hjorth J, Eppler JM, Dudani N, Helias M, Potjans TC, Bhalla US, Diesmann M, Kotaleski JH, Ekeberg O (2010) Run-Time Interoperability Between Neuronal Network Simulators Based on the MUSIC Framework Neuroinformatics DOI 10.1007/s12021-010-9064-z
• Nordlie E, Gewaltig M-O, Plesser HE (2009) Towards reproducible descriptions of neuronal network models PLoS Comput Biol 5(8):e1000456 (free)
• Eppler JM, Helias M, Muller E, Diesmann M, Gewaltig M-O (2009) Pynest: a convenient interface to the nest simulator Front Neuroinform 2, 12. doi:10.3389/neuro.11.012.2008 (free)
• Potjans W, Morrison A, Diesmann M (2009) A spiking neural network model of an actor-critic learning agent Neural Computation, 21: 353-359 (free, among top downloads from NECO)
• Plesser H E, Diesmann M (2009) Simplicity and efficiency of integrate-and-fire neuron models Neural Computation, 21: 353-359
• Grün S, Abeles M, and Diesmann M. (2008) Impact of higher-order correlations on coincidence distributions of massively parallel data. Lecture Notes in Computer Science, 'Dynamic Brain - from Neural Spikes to Behaviors' , vol 5286, 96-114 DOI:10.1007/978-3-540-88853-6 preprint
• Helias M, Rotter S, Gewaltig M-O, Diesmann M (2008) Structural plasticity controlled by calcium based correlation detection Front Comput Neurosci 2, 7. doi:10.3389/neuro.10.007.2008 (free)
• Schrader S, Grün S, Diesmann M, Gerstein G (2008) Detecting synfire chain activity using massively parallel spike train recording J Neurophysiol100: 2165-2176 (free)
• Pazienti A, Maldonado P, Diesmann M, Grün S (2008) The effectiveness of systematic spike dithering depends on the precision of cortical synchronization. Brain Research, 1225: 39-46
• Morrison A, Diesmann M, Gerstner W (2008) Phenomenological Models of Synaptic Plasticity based on Spike-Timing Biological Cybernetics 98(6): 459-478 (free)
• Tetzlaff T, Rotter S, Stark E, Abeles M, Aertsen A, Diesmann M (2008) Dependence of neuronal correlations on filter characteristics and marginal spike-train statistics Neural Computation, 20: 2133-2184 (free, among top downloads from NECO)
• Kriener B, Tetzlaff T, Aertsen A, Diesmann M, Rotter S (2008) Correlations and population dynamics in cortical networks Neural Computation, 20: 2185-2226
• Goedeke S, Diesmann M (2008) The mechanism of synchronization in feed-forward neuronal networks New Journal of Physics 10:015007 (free)
• Morrison A, Aertsen A, Diesmann M (2007) Spike-time dependent plasticity in balanced recurrent networks Neural Computation 19: 1437–1467 (free, in 2008 among top downloads from NECO)
• Morrison A, Straube S, Plesser H E, Diesmann M (2007) Exact subthreshold integration with continuous spike times in discrete time neural network simulations Neural Computation 19: 47-79
• Pazienti A, Diesmann M, and Grün S. (2007) Bounds of the ability to destroy precise coincidences by spike dithering. Lecture Notes in Computer Science 4729, pp. 428–437.
• Plesser H E, Eppler J M, Morrison A, Diesmann M, Gewaltig M-O (2007) Efficient Parallel Simulation of Large-Scale Neuronal Networks on Clusters of Multiprocessor Computers Euro-Par 2007, Proceedings of the 13th International Euro-Par Conference, LCNS vol. 4641 p. 672-681. Springer
• Brette R et al. incl. MD Simulation of networks of spiking neurons: A review of tools and strategies (2007) Journal of Computational Neuroscience 23(3): 349-398
• Gewaltig M-O, Diesmann M (2007) NEST Scholarpedia 2(4):1430
• Backofen R et al. incl. MD A Bottom-up approach to Grid-Computing at a University: the Black-Forest-Grid Initiative (2006) PIK - Praxis der Informationsverarbeitung 29:81--87
• Guerrero-Rivera R, Morrison A, Diesmann M, Pearce, T C (2006) Programmable Logic Construction Kits for Hyper Real-time Neuronal Modeling Neural Computation 18:2651--2679
• Morrison A, Mehring C, Geisel T, Aertsen A, Diesmann M (2005) Advancing the boundaries of high connectivity network simulation with distributed computing Neural Computation 17(8):1776--1801
• Tetzlaff T, Morrison A, Geisel, T, and Diesmann M (2004) Consequences of Realistic Network Size on the Stability of Embedded Synfire Chains Neurocomputing 58--60:117--121
• Denker M, Timme M, Diesmann M, Wolf F, and Geisel T (2004) Breaking synchrony by heterogeneity in complex networks Phys Rev Lett 92(7):074103.
• Grün S, Riehle A, and Diesmann M (2003) Effect of cross-trial non-stationarity on joint-spike events Biological Cybernetics 88(5):335--351
• Grün S, Riehle A, Aertsen A, and Diesmann M (2003) Temporal Scales of Cortical Interactions Nova Acta Leopoldina 332:189--206
• Pipa G, Diesmann M, and Grün S (2003) Significance of Joint-Spike Events Based on Trial-Shuffling by Efficient Combinatorial Methods Complexity 8(4):79--86
• Mehring C, Hehl U, Kubo M, Diesmann M, and Aertsen A (2003) Activity Dynamics and Propagation of Synchronous Spiking in Locally Connected Random Networks Biological Cybernetics 88(5):395--408
• Tetzlaff T, Buschermoehle M, Geisel T, and Diesmann M (2003) The spread of rate and correlation in stationary cortical networks Neurocomputing 52--54:949--954
• Tetzlaff T, Geisel T, and Diesmann M (2002) The Ground State of Cortical Feed-Forward Networks Neurocomputing 44--46:673--678
• Egert U, Knott Th, Schwarz C, Nawrot M, Brandt A, Rotter S, and Diesmann M (2002) MEA-Tools: an open source toolbox for the analysis of multi-electrode data with MATLAB Journal of Neuroscience Methods 117:33--42
• Grün S, Diesmann M, and Aertsen A (2002) `Unitary Events' in Multiple Single Neuron Spiking Activity. II. Non-Stationary Data Neural Computation 14(1):81--119
• Grün S, Diesmann M, and Aertsen A (2002) Unitary Events' in Multiple Single Neuron Spiking Activity. I. Detection and Significance Neural Computation 14(1):43--80
• Gewaltig M-O, Diesmann M, and Aertsen A (2001) Propagation of cortical synfire activity: survival probability in single trials and stability in the mean Neural Networks 14:657--673
• Gewaltig M-O, Diesmann M, and Aertsen A (2001) Cortical Synfire-Activity: Configuration Space and Survival Probability Neurocomputing 38--40:621--626
• Diesmann M, Gewaltig M-O, Rotter S, and Aertsen A (2001) State Space Analysis of Synchronous Spiking in Cortical Neural Networks Neurocomputing 38--40:565--571
• Riehle A, Grammont F, Diesmann M, and Grün S (2000) Dynamical Changes and Temporal Precision of Synchronized Spiking Activity in Monkey Motor Cortex During Movement Preparation Journal of Physiology (Paris) 94:569--582
• Rotter, S and Diesmann M (1999) Exact Digital Simulation of Time-Invariant Linear Systems with Applications to Neuronal Modeling Biological Cybernetics 81:381-402
• Grün S, Diesmann M, Grammont F, Riehle A, and Aertsen A Detecting unitary events without discretization of time (1999) Journal of Neuroscience Methods 94:67--79
• Diesmann M, Gewaltig M-O, and Aertsen A (1999) Conditions for Stable Propagation of Synchronous Spiking in Cortical Neural Networks Nature 402:529--533
• Riehle A, Grün S, Diesmann M, Aertsen A (1997) Spike Synchronization and Rate Modulation Differentially Involved in Motor Cortical Function Science 278:1950--1953
• Aertsen A, Diesmann M, and Gewaltig M-O (1996) Propagation of synchronous spiking activity in feedforward neural networks Journal of Physiology (Paris) 90:243--247

Chapters

• Morrison A, Diesmann M (2008) Maintaining Causality in Discrete Time Neuronal Network Simulations In: P. Beim Graben, C. Zhou, M. Thiel, and J. Kurths eds. Lectures in supercomputational neuroscience: dynamics in complex brain networks. Chapter IV.10 p 267-278. Springer preprint

Books

• Gewaltig M-O, Diesmann M, Morrison A, Plesser H E The Computer Science of Neural Systems Simulation Springer, Lecture notes in computer science (in preparation)

Selected Abstracts

CV

Research Positions

Academic Background

Scientific Honors, Editorial Boards, Other Scientific Activities