Birgit Kriener

• Senk, Johanna; Kriener, Birgit; Djurfeldt, Mikael; Voges, Nicole; Jiang, Han-Jia & Schüttler, Lisa [Show all 10 contributors for this article] (2022). Connectivity concepts in neuronal network modeling. PLoS Computational Biology. ISSN 1553-734X. 18(9). doi: 10.1371/journal.pcbi.1010086. Full text in Research Archive Show summary

  Neuronal network models are simplified and abstract representations of biological brains that allow researchers to study the influence of network connectivity on the dynamics in a controlled environment. Which neurons in a network are connected is determined by connectivity rules and even small differences between rules may lead to qualitatively different network dynamics. These rules either specify explicit pairs of source and target neurons or describe the connectivity on a statistical level abstracted from neuroanatomical data. We review articles describing models together with their implementations published in community repositories and find that incomplete and imprecise descriptions of connectivity are common. Our study proposes guidelines for the unambiguous description of network connectivity by formalizing the connectivity concepts already in use in the computational neuroscience community. Further we propose a graphical notation for network diagrams unifying existing diagram styles. These guidelines serve as a reference for future descriptions of connectivity and facilitate the reproduction of insights obtained with a model as well as its further use.

• Kriener, Birgit; Hu, Hua & Vervaeke, Koen Gerard Alois (2022). Parvalbumin interneuron dendrites enhance gamma oscillations. Cell reports. ISSN 2211-1247. 39(11). doi: 10.1016/j.celrep.2022.110948. Full text in Research Archive
• Senk, Johanna; Kriener, Birgit; Djurfeldt, Mikael; Voges, Nicole; Jiang, Han-Jia & Schüttler, Lisa [Show all 10 contributors for this article] (2021). Connectivity Concepts in Neuronal Network Modeling. arXiv.org. ISSN 2331-8422. doi: 10.5281/zenodo.5550352.
• Kriener, Birgit; Hu, Hua & Vervaeke, Koen Gerard Alois (2021). Parvalbumin interneuron dendrites enhance gamma oscillations. bioRxiv. ISSN 2692-8205. doi: 10.1101/2021.06.22.449483.
• Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Einevoll, Gaute & Plesser, Hans Ekkehard (2018). Firing-rate models for neurons with a broad repertoire of spiking behaviors. Journal of Computational Neuroscience. ISSN 0929-5313. 45(2), p. 103–132. doi: 10.1007/s10827-018-0693-9. Full text in Research Archive
• Kriener, Birgit; Helias, Moritz; Rotter, Stefan; Diesmann, Markus & Einevoll, Gaute Tomas (2014). How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regime. Frontiers in Computational Neuroscience. ISSN 1662-5188. 7. doi: 10.3389/fncom.2013.00187.
• Kriener, Birgit; Enger, Håkon; Tetzlaff, Tom; Plesser, Hans Ekkehard; Gewaltig, Marc-Oliver & Einevoll, Gaute Tomas (2014). Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses. Frontiers in Computational Neuroscience. ISSN 1662-5188. 8. doi: 10.3389/fncom.2014.00136. Full text in Research Archive
• Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Casti, Alex; Einevoll, Gaute Tomas & Plesser, Hans Ekkehard (2013). Firing-rate models capture essential response dynamics of LGN relay cells. Journal of Computational Neuroscience. ISSN 0929-5313. 35(3), p. 359–375. doi: 10.1007/s10827-013-0456-6.
• Yousaf, Muhammad; Kriener, Birgit; Wyller, John Andreas & Einevoll, Gaute Tomas (2013). Generation and annihilation of localized persistent-activity states in a two-population neural-field model. Neural Networks. ISSN 0893-6080. 46, p. 75–90. doi: 10.1016/j.neunet.2013.04.012.
• Kriener, Birgit; Anand, Lishma & Timme, Marc (2012). Complex networks: when random walk dynamics equals synchronization. New Journal of Physics. ISSN 1367-2630. 14. doi: 10.1088/1367-2630/14/9/093002.
• Kriener, Birgit (2012). How synaptic weights determine stability of synchrony in networks of pulse-coupled excitatory and inhibitory oscillators. Chaos. ISSN 1054-1500. 22(3). doi: 10.1063/1.4749794.
• Crook, Sharon M.; Bednar, James A.; Berger, Sandra; Cannon, Robert; Davison, Andrew P. & Djurfeldt, Mikael [Show all 15 contributors for this article] (2012). Creating, documenting and sharing network models. Network. ISSN 0954-898X. 23(4), p. 131–149. doi: 10.3109/0954898X.2012.722743.
• Van Bussel, Frank; Kriener, Birgit & Timme, Marc (2011). Inferring synaptic connectivity from spatio-temporal spike patterns. Frontiers in Computational Neuroscience. ISSN 1662-5188. 5. doi: 10.3389/fncom.2011.00003.
• Kriener, Birgit; Tetzlaff, Tom; Aertsen, Ad; Diesmann, Markus & Rotter, Stefan (2008). Correlations and population dynamics in cortical networks. Neural Computation. ISSN 0899-7667. 20(9), p. 2185–2226. Show summary

  The function of cortical networks depends on the collective interplay between neurons and neuronal populations, which is reflected in the correlation of signals that can be recorded at different levels. To correctly interpret these observations it is important to understand the origin of neuronal correlations. Here we study how cells in large recurrent networks of excitatory and inhibitory neurons interact and how the associated correlations affect stationary states of idle network activity. We demonstrate that the structure of the connectivity matrix of such networks induces considerable correlations between synaptic currents as well as between subthreshold membrane potentials, provided Dale's principle is respected. If, in contrast, synaptic weights are randomly distributed, input correlations can vanish, even for densely connected networks. Although correlations are strongly attenuated when proceeding from membrane potentials to action potentials (spikes), the resulting weak correlations in the spike output can cause substantial fluctuations in the population activity, even in highly diluted networks. We show that simple mean-field models that take the structure of the coupling matrix into account can adequately describe the power spectra of the population activity. The consequences of Dale's principle on correlations and rate fluctuations are discussed in the light of recent experimental findings.

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• Kriener, Birgit; Albada, Sacha van; Djurfeldt, Mikael & Senk, Johanna (2023). Connectivity concepts in neuronal network modeling.
• Kriener, Birgit & Waszak, Sebastian Martin (2023). A deep learning approach to infer somatic alterations in glioma using digital tissue slides.
• Kriener, Birgit; Mueller, Sabine & Waszak, Sebastian Martin (2023). DIGITAL PATHOLOGY IN NEURO-ONCOLOGY.
• Kriener, Birgit; Mueller, Sabine; Phillips, Joanna J. & Waszak, Sebastian Martin (2023). A deep learning-approach to infer somatic alterations in glioma using digital tissue slides.
• Kriener, Birgit (2022). Parvalbumin interneuron dendrites enhance gamma oscillations.
• Kriener, Birgit (2022). Digital pathology in neuro-oncology: Towards deep learning-based prediction of clinically-actionable genomic alterations from histopathological images.
• Kriener, Birgit; Mueller, Sabine & Waszak, Sebastian Martin (2022). Digital pathology in pediatric neuro-oncology: deep learning-based prediction of clinically-actionable genomic alterations from histopathological images.
• Kriener, Birgit; Mueller, Sabine & Waszak, Sebastian Martin (2022). Digital pathology in neuro-oncology: deep learning-based prediction of clinically-actionable genomic alterations from histopathological images.
• Kriener, Birgit & Waszak, Sebastian Martin (2022). Digital pathology in neuro-oncology: Towards deep learning-based prediction of clinically-actionable genomic alterations from histopathological images.
• Kriener, Birgit (2020). Robust parallel decision-making in neural circuits with nonlinear inhibition. Proceedings of the National Academy of Sciences of the United States of America. ISSN 0027-8424. doi: 10.1073/pnas.1917551117.
• Vervaeke, Koen Gerard Alois; Hu, Hua & Kriener, Birgit (2019). Dendritic properties of parvalbumin interneurons enhance robustness of gamma oscillations.
• Chaudhuri, Rishidev; Kriener, Birgit & Fiete, Ila (2018). How fast is neural winner-take-all when deciding between many options?
• Kriener, Birgit (2018). On the speed of decision-making with multiple alternatives.
• Kriener, Birgit; Hu, Hua & Vervaeke, Koen Gerard Alois (2018). Stabilization of gamma oscillations in basket cell networks: the role of dendritic attenuation.
• Kriener, Birgit (2018). The role of strong synapses in the making and breaking of synchrony.
• Kriener, Birgit; Hu, Hua & Vervaeke, Koen Gerard Alois (2018). Dendritic properties of parvalbumin interneurons enhance robustness of gamma oscillations.
• Kriener, Birgit (2017). BartosEtAl2002.
• Kriener, Birgit (2017). NorenbergEtAl2010_DGBasketCell.
• Kriener, Birgit; Enger, Håkon; Tetzlaff, Tom; Plesser, Hans Ekkehard; Gewaltig, Marc-Oliver & Einevoll, Gaute Tomas (2013). Dynamics and lifetime of persistent activity states in random networks of spiking neurons with strong synapses. BMC Neuroscience. ISSN 1471-2202. 14. doi: 10.1186/1471-2202-14-S1-P121.
• Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Einevoll, Gaute Tomas & Plesser, Hans Ekkehard (2013). Firing-rate models for neurons with a broad repertoire of spiking behaviors. BMC Neuroscience. ISSN 1471-2202. 14. doi: 10.1186/1471-2202-14-S1-P317.
• Hjertholm, Daniel; Kriener, Birgit & Plesser, Hans Ekkehard (2013). A Python test suite for statistical properties of probabilistic networks with & without structure. Frontiers in Neuroinformatics. ISSN 1662-5196. doi: 10.3389/conf.fninf.2013.09.00043.
• Hjertholm, Daniel; Kriener, Birgit & Plesser, Hans Ekkehard (2013). A Python test suite for statistical properties of probabilistic networks with & without structure.
• Kriener, Birgit; Enger, Håkon; Tetzlaff, Tom; Plesser, Hans Ekkehard; Gewaltig, Marc-Oliver & Einevoll, Gaute Tomas (2013). Dynamics and lifetime of persistent activity states in random networks of spiking neurons with strong synapses.
• Kriener, Birgit; Helias, Moritz; Rotter, Stefan; Diesmann, Markus & Einevoll, Gaute Tomas (2013). How pattern formation in ring networks of excitatory and inhibitory spiking neurons depends on the input current regime.
• Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Einevoll, Gaute Tomas & Plesser, Hans Ekkehard (2013). Firing-rate models for neurons with a broad repertoire of spiking behaviors.
• Kriener, Birgit & Einevoll, Gaute Tomas (2013). Input-dependent decorrelation of neuronal activity in barrel cortex.
• Kriener, Birgit (2013). Weight for it: How strong synapses shape network dynamics.
• Kriener, Birgit (2012). Description and testing of network connectivity.
• Kriener, Birgit (2012). The NEST simulation tool - an introduction.
• Kriener, Birgit & Einevoll, Gaute Tomas (2012). Input-dependent decorrelation of neuronal activity in barrel cortex.
• Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Casti, Alex; Einevoll, Gaute Tomas & Plesser, Hans Ekkehard (2012). Firing-rate models for the retina-LGN connection derived from experiments.
• Kriener, Birgit (2011). From spiking neurons to neural fields: the dynamics of ring networks.
• Kriener, Birgit (2011). Cortical networks: how structure shapes activity dynamics.
• Kriener, Birgit; Grabow, Carsten & Timme, Marc (2011). Suppression of synchronous population activity in developing neuronal networks by targeted stimulation of functional hubs – a modeling study. Show summary

  A recent experimental study by Bonifazi et al. (2009) showed that the functional topology of developing hippocampal networks has scale-free features, implying that some few neurons, so-called hubs, are heavily connected while most neurons have only a small number of connections. Morphological analysis revealed that these hub neurons are a sub-population of GABA-ergic interneurons with widespread axonal arbors. In the early stages of development GABA acting via GABA_A receptors is known to cause excitatory responses in immature neurons due to a high level of intracellular chloride (Ben-Ari et al. 2007). In an ongoing spontaneous activity regime this excitatory effect of GABA is a key feature in the generation of synchronous population events, so-called Giant Depolarizing Potentials (GDP). By targeted depolarizing current stimulation of a hub neuron these GDPs are suppressed, while no observable effect is generated by applying the same current step to a non-hub neuron. Here, we present a biologically inspired network model that accounts for the basic neuron and connectivity properties and reproduces the experimental findings. This allows to analyze the respective roles of topology on the one hand, as well as the electrophysiological properties of neurons and synapses on the other hand in the observed suppression of synchrony. Moreover, we discuss our findings within the framework of a reduced coupled oscillator model.

• Eppler, Jochen M; Enger, Håkon; Heiberg, Thomas; Kriener, Birgit; Plesser, Hans Ekkehard & Diesmann, Markus [Show all 7 contributors for this article] (2011). Evaluating the connection-set algebra for the neural simulator NEST.
• Heiberg, Thomas; Kriener, Birgit; Tetzlaff, Tom; Plesser, Hans Ekkehard & Einevoll, Gaute Tomas (2011). Rate dynamics of the retina-LGN connection.
• Heiberg, Thomas; Tetzlaff, Tom; Kriener, Birgit; Plesser, Hans Ekkehard & Einevoll, Gaute Tomas (2011). Rate dynamics of the retina-LGN connection.
• Enger, Håkon; Tetzlaff, Tom; Kriener, Birgit; Gewaltig, Marc-Oliver & Einevoll, Gaute T. (2011). Dynamics of Self-Sustained Activity in Random Networks with Strong Synapses.
• Tetzlaff, Tom; Kriener, Birgit; Aertsen, Ad; Diesmann, Markus & Rotter, Stefan (2008). Correlations and population dynamics in cortical networks. Show summary

  The function of cortical networks depends on the collective interplay between neurons and neuronal populations, which is reflected in the correlation of signals that can be recorded at different levels. To correctly interpret these observations it is important to understand the origin of neuronal correlations. Here we study how cells in large recurrent networks of excitatory and inhibitory neurons interact and how the associated correlations affect stationary states of idle network activity. We demonstrate that the structure of the connectivity matrix of such networks induces considerable correlations between synaptic currents as well as between subthreshold membrane potentials, provided Dale's principle is taken into account. If, in contrast, synaptic weights are randomly distributed, input correlations can vanish, even for densely connected networks. Although correlations are strongly attenuated when proceeding from membrane potentials to action potentials (spikes), the resulting weak correlations in the spike output can cause substantial fluctuations in the population activity, even in highly diluted networks. We show that simple mean-field models that take the structure of the coupling matrix into account can adequately describe the power spectra of the population activity. The consequences of Dale's principle on correlations and rate fluctuations are discussed in the light of recent experimental findings.

• Kriener, Birgit; Tetzlaff, Tom; Diesmann, Markus; Aertsen, Ad & Rotter, Stefan (2007). Correlations induced by the cortical structure. Show summary

  The function of cortical networks depends on the collective interplay between neurons and neuronal populations, which is reflected in the correlation of signals that can be recorded at different levels. To correctly interpret these observations, it is important to understand the origin of neuronal correlations. Here we study how cells in large recurrent networks of excitatory and inhibitory neurons interact, and how the associated correlations affect its dynamic state. Our main result is that the underlying recurrent structure of the network induces considerable correlations between synaptic currents, as well as between subthreshold membrane potentials. Although correlations are strongly attenuated when going to action potentials, the residual weak correlations in the spike output of the neurons nevertheless cause substantial fluctuations in the population activity. This is the case even for highly diluted networks and `asynchronous-irregular' states, which are distinguished by relatively asynchronous population activity and irregular firing of individual neurons. We show that these strong fluctuations arise from the functional segregation of excitation and inhibition, sometimes referred to as Dale's principle. The fluctuations are already prominent in random networks, a topology that is considered as a reasonable model for local cortical networks. Here we examine their impact also in more complex large-scale models of cortex, including networks with patchy connectivity as observed in layer 2/3 of V1 in cats. A statistical analysis of membrane potential and spike correlations can help to unravel structural properties of networks, as we demonstrate by comparing different model networks. Finally, we emphasize the important role of the functional segregation of inhibition and excitation by studying the effect of hybrid networks, where neurons can be excitatory and inhibitory at the same time. The activity dynamics in such networks is only weakly affected by structural features; they behave like random networks with a very narrow dynamical repertoire.

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