Moritz Deger

Scientific publications:

20. Stability of working memory in continuous attractor networks under the control of short-term plasticity
    Seeholzer A, Deger M, Gerstner W
    PLoS Computational Biology, 2019
19. Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation
    Setareh H, Deger M, Gerstner W
    PLoS Computational Biology, 2018
18. Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick (prospect article)
    Rost T, Deger M, Nawrot MP
    Biological Cybernetics, 2018, Special Issue on Neural Coding
17. Foreword for the special issue on Neural Coding (editorial article)
    Nawrot MP, Kloppenburg P, Deger M
    Biological Cybernetics, 2018, Special Issue on Neural Coding
16. Multicontact co-operativity in spike-timing-dependent structural plasticity stabilizes networks •
    Deger M, Seeholzer A, Gerstner W
    Cerebral Cortex, 2018
15. Efficient low-dimensional approximation of continuous attractor networks (preprint)
    Seeholzer A, Deger M, Gerstner W
    arXiv:1711.08032, 2017
14. Spike-timing dependent structural plasticity of multi-contact synaptic connections (book chapter)
    Deger M
    Van Ooyen & Butz-Ostendorf (eds.), The Rewiring Brain, Academic Press, 2017
13. Cortical dynamics in presence of assemblies of densely connected weight-hub neurons
    Setareh S, Deger M, Petersen C, Gerstner W
    Frontiers in Computational Neuroscience, 2017
12. Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size •
    Schwalger T, Deger M, Gerstner W
    PLoS Computational Biology, 2017
11. On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs
    Gerhard F, Deger M, Truccolo W
    PLoS Computational Biology, 2017
10. Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity •
    Zaytsev YV, Morrison A, Deger M
    Journal of Computational Neuroscience, 2015
9. Fluctuations and information filtering in coupled populations of spiking neurons with adaptation •
   Deger M,* Schwalger T,* Naud R, Gerstner W
   Physical Review E, 2014
8. The relevance of network micro-structure for neural dynamics
   Pernice V, Deger M, Cardanobile S, Rotter S
   Frontiers in Computational Neuroscience, 2013
7. Spike-timing dependence of structural plasticity explains cooperative synapse formation in the neocortex
   Deger M, Helias M, Rotter S, Diesmann M
   PLoS Computational Biology, 2012
6. Inferring general relations between network characteristics from specific network ensembles
   Cardanobile S, Pernice V, Deger M, Rotter S
   PLoS One, 2012
5. Statistical properties of superimposed stationary spike trains •
   Deger M,* Helias M,* Boucsein C, Rotter S
   Journal of Computational Neuroscience, 2012
4. Finite post synaptic potentials cause a fast neuronal response (focused review article)
   Helias M, Deger M, Rotter S, Diesmann M
   Frontiers in Neuroscience, 2011
3. Nonequilibrium dynamics of stochastic point processes with refractoriness
   Deger M, Helias M, Cardanobile S, Atay FM, Rotter S
   Physical Review E, 2010
2. Instantaneous non-linear processing by pulse-coupled threshold units
   Helias M,* Deger M,* Rotter S, Diesmann M
   PLoS Computational Biology, 2010
1. Equilibrium and response properties of the integrate-and-fire neuron in discrete time
   Helias M,* Deger M,* Diesmann M, Rotter S
   Frontiers in Computational Neuroscience, 2010

• Abstracts and further information can be found on PubMed.
• See also my Scopus author profile, my Google Scholar profile or my ORCID profile.

Software:

• Theory, simulation and analysis of the activity of populations of spiking neurons with adaptation -> Code / Paper [9]
• Estimation of spiking neuronal network models from parallel spike train recordings -> Code / Data / Paper [10]
• Neural Simulation Tool (NEST) -> Code / Introduction
• Spike train superposition generators -> NEST code (gamma_sup_generator) / NEST code (ppd_sup_generator) / Paper [5]
• Versatile point process neuron model -> NEST code (pp_psc_delta) / Paper [10]
• Neuronal population activity model -> NEST code (gif_pop_psc_exp) / Standalone C++ code / Paper [12]
• Structural plasticity model -> NEST code (stdp_structpl_connection_hom) / NEST module / Paper [16]