Controlling complexity of cerebral cortex simulations. II: Streamlined microcircuits. (English) Zbl 1470.92024

Summary: Recently, Markram et al. (2015) presented a model of the rat somatosensory microcircuit (Markram model). Their model is high in anatomical and physiological detail, and its simulation requires supercomputers. The lack of neuroinformatics and computing power is an obstacle for using a similar approach to build models of other cortical areas or larger cortical systems. Simplified neuron models offer an attractive alternative to high-fidelity Hodgkin-Huxley-type neuron models, but their validity in modeling cortical circuits is unclear.
We simplified the Markram model to a network of exponential integrate-and-fire (EIF) neurons that runs on a single CPU core in reasonable time. We analyzed the electrophysiology and the morphology of the Markram model neurons with eFel and NeuroM tools, provided by the Blue Brain Project. We then constructed neurons with few compartments and averaged parameters from the reference model. We used the CxSystem simulation framework to explore the role of short-term plasticity and \(\mathrm{GABA}_\mathrm{B}\) and NMDA synaptic conductances in replicating oscillatory phenomena in the Markram model. We show that having a slow inhibitory synaptic conductance (\(\mathrm{GABA}_\mathrm{B}\)) allows replication of oscillatory behavior in the high-calcium state. Furthermore, we show that qualitatively similar dynamics are seen even with a reduced number of cell types (from 55 to 17 types). This reduction halved the computation time.
Our results suggest that qualitative dynamics of cortical microcircuits can be studied using limited neuroinformatics and computing resources supporting parameter exploration and simulation of cortical systems. The simplification procedure can easily be adapted to studying other microcircuits for which sparse electrophysiological and morphological data are available.
For Part I, see [the authors, ibid. 31, No. 6, 1048–1065 (2019; Zbl 1470.92018)].


92B20 Neural networks for/in biological studies, artificial life and related topics
92-08 Computational methods for problems pertaining to biology


Zbl 1470.92018


NEURON; CxSystem
Full Text: DOI


[1] Andalibi, V., Hokkanen, H., & Vanni, S. (2019). Controlling complexity of cerebral cortex simulations—I: CxSystem, a flexible cortical simulation framework. Neural Computation, 31(6), 1048-1065. , · Zbl 1470.92018
[2] Ascoli, G. A., Alonso-Nanclares, L., Anderson, S. A., Barrionuevo, G., Benavides-Piccione, R., Burkhalter, A., … Yuste, R. (2008). Petilla terminology: Nomenclature of features of GABAergic interneurons of the cerebral cortex. Nature Reviews Neuroscience, 9(7), 557-568. ,
[3] Borst, J. G. G. (2010). The low synaptic release probability in vivo. Trends in Neurosciences, 33(6), 259-266. ,
[4] Brette, R., & Gerstner, W. (2005). Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. Journal of Neurophysiology, 94, 3637-3642. ,
[5] Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J. M., … Destexhe, A. (2007). Simulation of networks of spiking neurons: A review of tools and strategies. Journal of Computational Neuroscience, 23(3), 349-398. ,
[6] Brunel, N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of Computational Neuroscience, 8(3), 183-208. , · Zbl 1036.92008
[7] DeFelipe, J., López-Cruz, P. L., Benavides-Piccione, R., Bielza, C., Larrañaga, P., Anderson, S., … Ascoli, G. A. (2013). New insights into the classification and nomenclature of cortical GABAergic interneurons. Nature Reviews Neuroscience, 14(3), 202-216. ,
[8] Fourcaud-Trocmé, N., Hansel, D., van Vreeswijk, C., & Brunel, N. (2003). How spike generation mechanisms determine the neuronal response to fluctuating inputs. Journal of Neuroscience, 23(37), 11628-11640. ,
[9] Hay, E., & Segev, I. (2015). Dendritic excitability and gain control in recurrent cortical microcircuits. Cerebral Cortex, 25, 3561-3571. ,
[10] Herz, A. V. M., Gollisch, T., Machens, C. K., & Jaeger, D. (2006). Modeling single-neuron dynamics and computations: A balance of detail and abstraction. Science, 314(5796), 80-85. , · Zbl 1226.92007
[11] Hille, B. (2001). Ionic channels of excitable membranes. Sunderland, MA: Sinauer.
[12] Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117(4), 500-544. ,
[13] Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569-1572. ,
[14] Jahr, C. E., & Stevens, C. F. (1990). Voltage dependence of NMDA-activated macroscopic conductances predicted by single-channel kinetics. Journal of Neuroscience, 10(9), 3178-3182. ,
[15] Knight, B. W. (1972). Dynamics of encoding in a population of neurons. Journal of General Physiology, 59(6), 734-766. ,
[16] Kumar, A., Schrader, S., & Rotter, S. (2008). The high-conductance state of cortical networks. Neural Computation, 20(1), 1-43. , · Zbl 1149.92307
[17] Lapicque, L. (1907). Recherches quantitatives sur l’excitation électrique des nerfs traitée comme une polarisation. Journal de physiologie et de pathologie générale, 9, 620-635.
[18] Luebke, J. I. (2017). Pyramidal neurons are not generalizable building blocks of cortical networks. Frontiers in Neuroanatomy, 11, 11. ,
[19] Markram, H., Muller, E., Ramaswamy, S., Reimann, M. W., Abdellah, M., Sanchez, C. A., … Schürmann, F. (2015). Reconstruction and simulation of neocortical microcircuitry. Cell, 163(2), 456-492. ,
[20] Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004). Interneurons of the neocortical inhibitory system. Nature Reviews Neuroscience, 5(10), 793-807. ,
[21] Markram, H., Wang, Y., & Tsodyks, M. (1998). Differential signaling via the same axon of neocortical pyramidal neurons. Proceedings of the National Academy of Sciences of the United States of America, 95(9), 5323-5328. ,
[22] Mel, B. W. (1994). Information processing in dendritic trees. Neural Computation, 6(6), 1031-1085. , · Zbl 0810.92007
[23] Myme, C. I. O., Sugino, K., Turrigiano, G. G., & Nelson, S. B. (2003). The NMDA-to-AMPA ratio at synapses onto layer 2/3 pyramidal neurons is conserved Across prefrontal and visual cortices. Journal of Neurophysiology, 90(2), 771-779. ,
[24] Pospischil, M., Piwkowska, Z., Bal, T., & Destexhe, A. (2011). Comparison of different neuron models to conductance-based post-stimulus time histograms obtained in cortical pyramidal cells using dynamic-clamp in vitro. Biological Cybernetics, 105(2), 167-180. , · Zbl 1248.92012
[25] Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z., Bal, T., Frégnac, Y., … Destexhe, A. (2008). Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons. Biological Cybernetics, 99(4-5), 427-441. , · Zbl 1161.92013
[26] Potjans, T. C., & Diesmann, M. (2014). The cell-type specific cortical microcircuit: Relating structure and activity in a full-scale spiking network model. Cerebral Cortex, 24(3), 785-806. ,
[27] Rössert, C., Pozzorini, C., Chindemi, G., Davison, A. P., Eroe, C., King, J., … Muller, E. (2016). Automated point-neuron simplification of data-driven microcircuit models. arXiv:1604.00087.
[28] Rozov, A., Burnashev, N., Sakmann, B., & Neher, E. (2001). Transmitter release modulation by intracellular Ca2+ buffers in facilitating and depressing nerve terminals of pyramidal cells in layer 2/3 of the rat neocortex indicates a target cell-specific difference in presynaptic calcium dynamics. Journal of Physiology, 531(3), 807-826. ,
[29] Silberberg, G., Gupta, A., & Markram, H. (2002). Stereotypy in neocortical microcircuits. Trends in Neurosciences, 25(5), 227-230. ,
[30] Silberberg, G., & Markram, H. (2007). Disynaptic inhibition between neocortical pyramidal cells mediated by martinotti cells. Neuron, 53(5), 735-746. ,
[31] Spruston, N. (2008). Pyramidal neurons: Dendritic structure and synaptic integration. Nature Reviews Neuroscience, 9(3), 206-221. ,
[32] Stein, R. B. (1965). A theoretical analysis of neuronal variability. Biophysical Journal, 5(2), 173-194. ,
[33] Stimberg, M., Goodman, D. F. M., Benichoux, V., & Brette, R. (2014). Equation-oriented specification of neural models for simulations. Frontiers in Neuroinformatics, 8, 6. ,
[34] Tikidji-Hamburyan, R. A., Narayana, V., Bozkus, Z., & El-Ghazawi, T. A. (2017). Software for brain network simulations: A comparative study. Frontiers in Neuroinformatics, 11, 46. ,
[35] Tsodyks, M., & Markram, H. (1997). The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proceedings of the National Academy of Sciences of the United States of America, 94(2), 719-723. ,
[36] Wang, Y., Toledo-Rodriguez, M., Gupta, A., Wu, C., Silberberg, G., Luo, J., & Markram, H. (2004). Anatomical, physiological and molecular properties of Martinotti cells in the somatosensory cortex of the juvenile rat. Journal of Physiology, 561(Pt. 1), 65-90. ,
[37] Zucker, R. S., & Regehr, W. G. (2002). Short-term synaptic plasticity. Annual Review of Physiology, 64(1), 355-405. ,
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