The relevance of network micro-structure for neural dynamics

Frontiers in Computational Neuroscience

Volumn , Issue JUN, 2013, Pages

  Pernice, Volker ^a   Deger, Moritz ^a   Cardanobile, Stefano ^a   Rotter, Stefan ^a  

^a UNIVERSITY OF FREIBURG   (Germany)

Author keywords

Asynchronous irregular state; Cortical networks; Integrate and fire neuron; Microstructure; Multifractal network generator

Indexed keywords

ASYNCHRONOUS IRREGULAR STATE; CORTICAL NETWORK; HIGHER ORDER CORRELATION; INTEGRATE-AND-FIRE NEURONS; MULTI FRACTALS; RANDOM NETWORK MODELS; STATISTICAL RELATIONS; STRUCTURAL CHARACTERISTICS;

ELECTRIC NETWORK TOPOLOGY; FRACTALS; MICROSTRUCTURE; NETWORKS (CIRCUITS); NEURONS; RECURRENT NEURAL NETWORKS;

COMPUTER SIMULATION;

ALGORITHM; ARTICLE; CELL FRACTIONATION; LEAKY INTEGRATE AND FIRE NEURON; NERVE CELL CHARACTERISTICS AND FUNCTIONS; NERVE CELL NETWORK; NERVOUS SYSTEM PARAMETERS; SIMULATION; SPIKE WAVE; STATISTICAL ANALYSIS; SYNAPTIC POTENTIAL;

EID: 84878802262     PISSN: 16625188     EISSN: None     Source Type: Journal    
DOI: 10.3389/fncom.2013.00072     Document Type: Article

References (60)

1. Atay, F. M., Biyikoglu, T., and Jost, J. (2006). Network synchronization: spectral versus statistical properties. Physica D 224, 35-41. doi: 10.1016/j.physd.2006.09.018
2. Barabási, A.-L., and Albert, R. (1999). Emergence of scaling in random networks. Science 286:509. doi: 10.1126/science.286.5439.509
3. Barth, A. L., and Poulet, J. F. A. (2012). Experimental evidence for sparse firing in the neocortex. Trends Neurosci. 35, 345-355. doi: 10.1016/j.tins.2012.03.008
4. Benayoun, M., Cowan, J. D., van Drongelen, W., and Wallace, E. (2010). Avalanches in a stochastic model of spiking neurons. PLoS Comput. Biol. 6:e1000846. doi: 10.1371/journal.pcbi.1000846
5. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., and Hwang, D. (2006). Complex networks: structure and dynamics. Phys. Rep. 424, 175-308. doi: 10.1016/j.physrep. 2005.10.009
6. Brunel, N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J. Comput. Neurosci. 8, 183-208.
7. Bullmore, E., and Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186-198. doi: 10.1038/nrn2575
8. Cardanobile, S., Pernice, V., Deger, M., and Rotter, S. (2012). Inferring general relations between network characteristics from specific network ensembles. PLoS ONE 7:e37911. doi: 10.1371/journal. pone.0037911
9. Cohen, M. R., and Kohn, A. (2011). Measuring and interpreting neu-ronal correlations. Nat. Neurosci. 14, 811-819. doi: 10.1038/nn.2842
10. Ecker, A. S., Berens, P., Keliris, G. A., Bethge, M., Logothetis, N. K., and Tolias, A. S. (2010). Decorrelated neuronal firing in cortical microcir-cuits. Science 327, 584-587. doi: 10.1126/science.1179867
11. Fagiolo, G. (2007). Clustering in complex directed networks. Phys. Rev. E. 76:026107. doi: 10.1103/ PhysRevE.76.026107
12. Gaiteri, C., and Rubin, J. E. (2011). The interaction of intrinsic dynamics and network topology in determining network burst synchrony. Front. Comput. Neurosci. 5:10. doi: 10.3389/fncom.2011.00010
13. Ganmor, E., Segev, R., and Schneidman, E. (2011). Sparse low-order interaction network underlies a highly correlated and learnable neural population code. Proc. Natl. Acad. Sci. U.S.A. 108, 9679-9684. doi: 10.1073/pnas.1019641108
14. Gewaltig, M.-O., and Diesmann, M. (2007). NEST (NEural simulation tool). Scholarpedia 2:1430. doi: 10.4249/scholarpedia.1430
15. Hamaguchi, K., Riehle, A., and Brunel, N. (2011). Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons. J. Neurophysiol. 105, 487-500. doi: 10.1152/jn.00858.2009
16. Hennequin, G., Vogels, T. P., and Gerstner, W. (2012). Non-normal amplification in random balanced neuronal networks. Phys. Rev. E. 86:011909. doi: 10.1103/PhysRevE.86.011909
17. Hu, Y., Trousdale, J., Josić, K., and Shea-Brown, E. (2013). Motif statistics and spike correlations in neuronal networks. J. Stat. Mech. Theory E 2013:P03012. doi: 10.1088/1742-5468/2013/03/P03012
18. Jarvis, S., Rotter, S., and Egert, U. (2010). Extending stability through hierarchical clusters in echo state networks. Front. Neuroinf. 4:11. doi: 10.3389/fninf.2010.00011
19. Jones, E., Oliphant, T., Peterson, P., and others. (2001). SciPy: Open source scientific tools for Python. Available online at: http://www.scipy.org
20. Kaiser, M., and Hilgetag, C. C. (2010). Optimal hierarchical modular topologies for producing limited sustained activation of neural networks. Front. Neuroinf. 4:8. doi: 10.3389/fninf.2010.00008
21. Kalisman, N., Silberberg, G., and Markram, H. (2005). The neocortical microcircuit as a tabula rasa. Proc. Natl. Acad. Sci. U.S.A. 102, 880-885. doi: 10.3410/f.1023301.269659
22. Kremkow, J., Perrinet, L. U., Masson, G. S., and Aertsen, A. (2010). Functional consequences of correlated excitatory and inhibitory conductances in cortical networks. J. Comput. Neurosci. 28, 579-594. doi: 10.1007/s10827-010-0240-9
23. Kriener, B., Helias, M., Aertsen, A., and Rotter, S. (2009). Correlations in spiking neuronal networks with distance dependent connections. J. Comput. Neurosci. 27, 177-200. doi: 10.1007/s10827-008-0135-1
24. Kumar, A., Schrader, S., Aertsen, A., and Rotter, S. (2008). The high-conductance state of cortical networks. Neural. Comput. 20, 1-43. doi: 10.1162/neco.2008.20.1.1
25. Leicht, E., and Newman, M. (2008). Community structure in directed networks. Phys. Rev. Lett. 100:118703. doi: 10.1103/ PhysRevLett.100.118703
26. Litwin-Kumar, A., and doiron, B. (2012). Slow dynamics and high variability in balanced cortical networks with clustered connections. Nat. Neurosci. 15, 1498-1505. doi: 10.1038/nn.3220
27. Macke, J. H., Opper, M., and Bethge, M. (2011). Common input explains higher-order correlations and entropy in a simple model of neural population activity. Phys. Rev. Lett. 106:208102. doi: 10.1103/PhysRevLett.106.208102
28. Mäki-Marttunen, T., Aćimović, J., Nykter, M., Kesseli, J., Ruohonen, K., Yli-Harja, O., et al. (2011). Information diversity in struc ture and dynamics of simulated neuronal networks. Front. Comput. Neurosci. 5:26. doi: 10.3389/fncom.2011.00026
29. Markram, H. (1997). A network of tufted layer 5 pyramidal neurons. Cereb. Cortex. 7, 523-533. doi: 10.1093/cercor/ 7.6.523
30. Mongillo, G., Hansel, D., and van Vreeswijk, C. (2012). Bistability and spatiotemporal irregularity in neuronal networks with nonlinear synaptic transmission. Phys. Rev. Lett. 108, 13-17. doi: 10.1103/ PhysRevLett.108.158101
31. Müller-Linow, M., Hilgetag, C. C., and Hütt, M.-T. (2008). Organization of excitable dynamics in hierarchical biological networks. PLoS. Comput. Biol. 4:e1000190. doi: 10.1371/journal.pcbi.1000190
32. Newman, M. (2002). Assortative mixing in networks. Phys. Rev. Lett. 89, 208701. doi: 10.1103/ PhysRevLett.89.208701
33. Palla, G. (2010). Multifractal network generator. Proc. Natl. Acad. Sci. U.S.A. 107, 7640-7645. doi: 10.1073/pnas.0912983107
34. Perin, R., Berger, T. K., and Markram, H. (2011). A synaptic organizing principle for cortical neu-ronal groups. Proc. Natl. Acad. Sci. U.S.A. 108, 5419-5424. doi: 10.1073/pnas.1016051108
35. Pernice, V., and Rotter, S. (2013). Reconstruction of sparse connectivity in neural networks from spike train covari-ances. J. Stat. Mech. Theory E 2013:P03008. doi: 10.1088/1742-5468/2013/03/P03008
36. Pernice, V., Staude, B., Cardanobile, S., and Rotter, S. (2011). How structure determines correlations in neuronal networks. PLoS Comput. Biol. 7:e1002059. doi: 10.1371/jour-nal.pcbi.1002059
37. Potjans, T. C., and Diesmann, M. (2012). The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model. Cereb. Cortex. doi: 10.1093/cercor/bhs358
38. Pouille, F., Marin-Burgin, A., Adesnik, H., Atallah, B. V., and Scanziani, M. (2009). Input normalization by global feed forward inhibition expands cortical dynamic range. Nat. Neurosci. 12, 1577-1585. doi: 10.1038/nn.2441
39. Renart, A., De la Rocha, J., Bartho, P., and Hollender, L. (2010). The asynchronous state in cortical circuits. Science 327, 587-590. doi: 10.1126/science.1179850
40. Reshef, D. N., Reshef, Y. A., Finucane, H. K., Grossman, S. R., McVean, G., Turnbaugh, P. J., et al. (2011). Detecting novel associations in large data sets. Science 334, 1518-1524. doi: 10.1126/science.1205438
41. Roxin, A. (2004). Self-sustained activity in a small-world network of excitable neurons. Phys. Rev. Lett. 92:198101. doi: 10.1103/PhysRevLett.92.198101
42. Roxin, A. (2011).The role of degree distribution in shaping the dynamics in networks of sparsely connected spiking neurons. Front. Comput. Neurosci. 5:8. doi: 10.3389/fncom.2011.00008
43. Roxin, A., Brunel, N., Hansel, D., Mongillo, G., and van Vreeswijk, C. (2011). On the distribution of firing rates in networks of cortical neurons. J. Neurosci. 31, 16217-16226. doi: 10.1523/JNEUROSCI.1677-11.2011
44. Rubinov, M., and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003
45. Shadlen, M. N., and Newsome, W. T. (1998). The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 18, 3870-3896.
46. Shandilya, S. G., and Timme, M. (2011). Inferring network topology from complex dynamics. New J. Phys. 13:013004. doi: 10.1088/1367-2630/13/1/013004
47. Softky, W. R., and Koch, C. (1993). The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334-350.
48. Song, S., Reigl, M., and Nelson, S. (2005). Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biol. 3:e68. doi: 10.1371/journal.pbio.0030068
49. Staude, B., Grün, S., and Rotter, S. (2010a). "Higher-order correlations and cumulants," in Analysis of Parallel Spike Trains, eds S. Rotter and S. Grün (Berlin: Springer), 253-283.
50. Staude, B., Rotter, S., and Grün, S. (2010b). CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains. J. Comput. Neurosci. 29, 327-350. doi: 10.1007/s10827-009-0195-x
51. van Vreeswijk, C., and Sompolinsky, H. (1996). Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274, 1724-1726. doi: 10.1126/sci-ence.274.5293.1724
52. Vogels, T. P., and Abbott, L. F. (2005). Signal propagation and logic gating in networks of integrate-and-fire neurons. J. Neurosci. 25, 10786-10795. doi: 10.1523/ JNEUROSCI.3508-05.2005
53. Vogels, T. P., Rajan, K., and Abbott, L. F. (2005). Neural network dynamics. Annu. Rev. Neurosci. 28, 357-376. doi: 10.1146/annurev. neuro.28.061604.135637
54. Voges, N., and Perrinet, L. (2009). Phase space analysis of networks based on biologically realistic parameters. J. Physiol. Paris 104, 51-60. doi: 10.1016/j.jphysparis. 2009.11.004
55. Voges, N., and Perrinet, L. (2012). Complex dynamics in recurrent cortical networks based on spatially realistic connectivities. Front. Comput. Neurosci. 6:41. doi: 10.3389/fncom.2012.00041
56. Watts, D. J., and Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature 393, 440-442. doi: 10.1038/30918
57. Yassin, L., Benedetti, B. L., Jouhanneau, J.-S., Wen, J. A., Poulet, J. F. A., and Barth, A. L. (2010). An embedded subnetwork of highly active neurons in the neocortex. Neuron 68, 1043-1050. doi: 10.1016/j.neuron.2010.11.029
58. Yger, P., El Boustani, S., Destexhe, A., and Frégnac, Y. (2011). Topologically invariant macro scopic statistics in balanced networks of conductance-based integrate-and-fire neurons. J. Comput. Neurosci. 31, 229-245. doi: 10.1007/s10827-010-0310-z
59. Yu, S., Yang, H., Nakahara, H., Santos, G. S., Nikolić, D., and Plenz, D. (2011). Higher-order interactions characterized in cortical activity. J. Neurosci. 31, 17514-17526. doi: 10.1523/JNEUROSCI.3127-11.2011
60. Zhao, L., Beverlin, B., Netoff, T., and Nykamp, D. Q. (2011). Synchronization from second order network connectivity statistics. Front. Comput. Neurosci. 5:28. doi: 10.3389/fncom.2011.00028