Controlling complexity of cerebral cortex simulations. I: CxSystem, a flexible cortical simulation framework. (English) Zbl 1470.92018

Summary: Simulation of the cerebral cortex requires a combination of extensive domain-specific knowledge and efficient software. However, when the complexity of the biological system is combined with that of the software, the likelihood of coding errors increases, which slows model adjustments. Moreover, few life scientists are familiar with software engineering and would benefit from simplicity in form of a high-level abstraction of the biological model.
Our primary aim was to build a scalable cortical simulation framework for personal computers. We isolated an adjustable part of the domain-specific knowledge from the software. Next, we designed a framework that reads the model parameters from comma-separated value files and creates the necessary code for Brian2 model simulation. This separation allows rapid exploration of complex cortical circuits while decreasing the likelihood of coding errors and automatically using efficient hardware devices.
Next, we tested the system on a simplified version of the neocortical microcircuit proposed by Markram and colleagues (2015). Our results indicate that the framework can efficiently perform simulations using Python, C++, and GPU devices. The most efficient device varied with computer hardware and the duration and scale of the simulated system. The speed of Brian2 was retained despite an overlying layer of software. However, the Python and C++ devices inherited the single core limitation of Brian2.
The CxSystem framework supports exploration of complex models on personal computers and thus has the potential to facilitate research on cortical networks and systems.
For Part II, see [the authors, ibid. 31, No. 6, 1066–1084 (2019; Zbl 1470.92024)].


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


Zbl 1470.92024
Full Text: DOI


[1] Aisa, B., Mingus, B., & O’Reilly, R. (2008). The emergent neural modeling system. Neural Networks, 21(8), 1146-1152. ,
[2] Almog, M., & Korngreen, A. (2016). Is realistic neuronal modeling realistic?Journal of Neurophysiology, 116(5), 2180-2209. ,
[3] Bekolay, T., Bergstra, J., Hunsberger, E., DeWolf, T., Stewart, T. C., Rasmussen, D., … Eliasmith, C. (2014). Nengo: A Python tool for building large-scale functional brain models. Frontiers in Neuroinformatics, 7, 48. ,
[4] Betizeau, M., Dehay, C., & Kennedy, H. (2014). Conformity and specificity of primate corticogenesis. In J. S. Werner and L. M. Chalupa (Eds.), The new visual neurosciences (pp. 1407-1422). Cambridge, MA: MIT Press.
[5] Bower, J. M. (1992). Modeling the nervous system. Trends in Neurosciences, 15(11), 411-412. ,
[6] 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. J. Comput. Neurosci., 23(3), 349-398. ,
[7] Davison, A., Brüderle, D., Eppler, J., Kremkow, J., Muller, E., Pecevski, D., … Yger, P. (2009). PyNN: A common interface for neuronal network simulators. Frontiers in Neuroinformatics, 2, 11.
[8] Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M., & Friston, K. (2008). The dynamic brain: From spiking neurons to neural masses and cortical fields. PLoS Computational Biology, 4(8), e1000092. ,
[9] Elston, G. N., Oga, T., Okamoto, T., & Fujita, I. (2010). Spinogenesis and pruning from early visual onset to adulthood: An intracellular injection study of layer III pyramidal cells in the ventral visual cortical pathway of the macaque monkey. Cerebral Cortex, 20(6), 1398-1408. ,
[10] Elston, G. N., & Rosa, M. G. P. (1997). The occipitoparietal pathway of the macaque monkey: Comparison of pyramidal cell morphology in layer III of functionally related cortical visual areas. Cerebral Cortex, 7(5), 432-452. ,
[11] Eppler, J. M. (2009). PyNEST: A convenient interface to the NEST simulator. Frontiers in Neuroinformatics, 2, 12.
[12] Feldman, D. E. (2009). Synaptic mechanisms for plasticity in neocortex. Annual Review of Neuroscience, 32(1), 33-55. ,
[13] Fourcaud-Trocmé, N., Hansel, D., van Vreeswijk, C., & Brunel, N. (2003). How spike generation mechanisms determine the neuronal response to fluctuating inputs. J. Neurosci., 23(37), 11628-11640. ,
[14] Gerstner, W., Sprekeler, H., & Deco, G. (2012). Theory and simulation in neuroscience. Science, 338(6103), 60-65. ,
[15] Gewaltig, M.-O., & Diesmann, M. (2007). NEST (NEural Simulation Tool). Scholarpedia, 2(4), 1430. ,
[16] Gleeson, P., Crook, S., Cannon, R. C., Hines, M. L., Billings, G. O., Farinella, M., … Silver, R. A. (2010). NeuroML: A language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS Computational Biology, 6(6), 1-19. ,
[17] Gleeson, P., Steuber, V., & Silver, R. A. (2007). neuroConstruct: A tool for modeling networks of neurons in 3D space. Neuron, 54(2), 219-235. ,
[18] Goodman, D., & Brette, R. (2009). The Brian simulator. Frontiers in Neuroscience, 3, 26. ,
[19] Heikkinen, H., Sharifian, F., Vigário, R., & Vanni, S. (2015). Feedback to distal dendrites links fMRI signals to neural receptive fields in a spiking network model of the visual cortex. Journal of Neurophysiology, 114(1), 57-69. ,
[20] Herculano-Houzel, S., Collins, C. E., Wong, P., Kaas, J. H., & Lent, R. (2008). The basic nonuniformity of the cerebral cortex. Proceedings of the National Academy of Sciences, 105(34), 12593-12598. ,
[21] Hines, M. L., & Carnevale, N. T. (1997). The NEURON simulation environment. Neural Computation, 9(6), 1179-1209. ,
[22] Hoang, R. V., Tanna, D., Bray, L. C. J., Dascalu, S. M., & Harris Jr., F. C. (2013). A novel CPU/GPU simulation environment for large-scale biologically realistic neural modeling. Frontiers in Neuroinformatics, 7, 19. ,
[23] Hokkanen, H., Andalibi, V., & Vanni, S. (2019). Controlling complexity of cerebral cortex simulations—II: Streamlined microcircuits. Neural Computation, 31(6), 1066-1084. ,
[24] Horvát, S., Gămănuț, R., Ercsey-Ravasz, M., Magrou, L., Gămănuț, B., Van Essen, D. C., … Kennedy, H. (2016). Spatial embedding and wiring cost constrain the functional layout of the cortical network of rodents and primates. PLoS Biology, 14(7), e1002512. ,
[25] Larkum, M. E., Nevian, T., Sandler, M., Polsky, A., & Schiller, J. (2009). Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: A new unifying principle. Science, 325(5941), 756-760. ,
[26] Markov, N. T., Ercsey-Ravasz, M. M., Ribeiro Gomes, A. R., Lamy, C., Magrou, L., Vezoli, J., … Kennedy, H. (2014). A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cerebral Cortex, 24(1), 17-36. ,
[27] Markov, N. T., Ercsey-Ravasz, M., Van Essen, D. C., Knoblauch, K., Toroczkai, Z., & Kennedy, H. (2013). Cortical high-density counterstream architectures. Science, 342(6158), 1238406. ,
[28] Markov, N. T., Misery, P., Falchier, A., Lamy, C., Vezoli, J., Quilodran, R., … Knoblauch, K. (2011). Weight consistency specifies regularities of macaque cortical networks. Cerebral Cortex, 21(6), 1254-1272. ,
[29] 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. ,
[30] Markram, H., Wang, Y., & Tsodyks, M. (1998). Differential signaling via the same axon of neocortical pyramidal neurons. Proc. Natl. Acad. Sci. USA, 95(9), 5323-5328. ,
[31] McConnell, S. (2004). Code complete (2nd ed.). Seattle: Microsoft Press.
[32] Nowotny, T., Cope, A. J., Yavuz, E., Stimberg, M., Goodman, D. F. M., Marshall, J., & Gurney, K. (2014). SpineML and Brian 2.0 interfaces for using GPU enhanced neuronal networks (GeNN). BMC Neuroscience, 15(1), P148. ,
[33] Raikov, I., Cannon, R., Clewley, R., Cornelis, H., Davison, A., De Schutter, E., … Szatmary, B. (2011). NineML: The network interchange for neuroscience modeling language. BMC Neuroscience, 12(Suppl. 1), P330. ,
[34] Rall, W. (1962). Theory of physiological properties of dendrites. Annals of the New York Academy of Sciences, 96(4), 1071-1092. ,
[35] Rasch, B., & Born, J. (2013). About sleep’s role in memory. Physiological Reviews, 93(2), 681-766. ,
[36] Sidiropoulou, K., Pissadaki, E. K., & Poirazi, P. (2006). Inside the brain of a neuron. EMBO Rep., 7(9), 886-892. ,
[37] Sjöström, P. J., Rancz, E. A., Roth, A., & Häusser, M. (2008). Dendritic excitability and synaptic plasticity. Physiological Reviews, 88(2), 769-840. ,
[38] 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. ,
[39] Tosi, Z., & Yoshimi, J. (2016). Simbrain 3.0: A flexible, visually-oriented neural network simulator. Neural Networks, 83, 1-10. ,
[40] Turrigiano, G. G. (2008). The self-tuning neuron: Synaptic scaling of excitatory synapses. Cell, 135(3), 422-435. ,
[41] Van Essen, D. C., Glasser, M. F., Dierker, D. L., & Harwell, J. (2012). Cortical parcellations of the macaque monkey analyzed on surface-based atlases. Cerebral Cortex, 22(10), 2227-2240. ,
[42] Vogels, T. P., & Abbott, L. F. (2005). Signal propagation and logic gating in networks of integrate-and-fire neurons. Journal of Neuroscience, 25(46), 10786-10795. ,
[43] Yavuz, E., Turner, J., & Nowotny, T. (2016). GeNN: A code generation framework for accelerated brain simulations. Scientific Reports, 6, 18854. ,
[44] Zucker, R. S., & Regehr, W. G. (2002). Short-term synaptic plasticity. Annual Review of Physiology, 64(1), 355-405. ,
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