Information diversity in structure and dynamics of simulated neuronal networks

Frontiers in Computational Neuroscience

Volumn 5, Issue , 2011, Pages

  Mäki Marttunen, Tuomo ^a   Aćimović, Jugoslava ^a   Nykter, Matti ^a   Kesseli, Juha ^a   Ruohonen, Keijo ^a   Yli Harja, Olli ^a   Linne, Marja Leena ^a  

^a TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

Author keywords

Complexity; Information diversity; Neuronal network; Structure dynamics relationship

Indexed keywords

COMPLEX NETWORKS; DYNAMICS; INFORMATION THEORY; NEURONS; SIGNAL ENCODING;

CLUSTERING COEFFICIENT; COMPLEXITY; IN-DEGREE DISTRIBUTION; INFORMATION DIVERSITY; NEURONAL NETWORKS; NORMALIZED COMPRESSION DISTANCE; STRUCTURE AND DYNAMICS; STRUCTURE-DYNAMICS RELATIONSHIP;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; COMPUTER SIMULATION; CONCEPTUAL FRAMEWORK; CONNECTOME; INFORMATION PROCESSING; IZHIKEVICH MODEL; MATHEMATICAL ANALYSIS; MATHEMATICAL MODEL; MORPHOLOGY; NERVE CELL NETWORK; NERVE CELL PLASTICITY; SPIKE WAVE; SYNAPTIC TRANSMISSION;

EID: 84874916770     PISSN: None     EISSN: 16625188     Source Type: Journal    
DOI: 10.3389/fncom.2011.00026     Document Type: Article

References (39)

1. Aćimović, J., Mäki-Marttunen, T., Havela, R., Teppola, H., and Linne, M.-L. (2011). Modeling of neuronal growth in vitro: comparison of simulation tools NETMORPH and CX3D. EURASIP J. Bioinform. Syst. Biol. 2011, Article ID 616382.
2. Albert, R., and Barabási, A.-L. (2002). Statistical mechanics of complex net-works. Rev. Mod. Phys. 74, 47-97
3. Amigó, J. M., Szczepański, J., Wajnryb, E., and Sanchez-Vivez, M. V. (2004). Estimating the entropy rate of spike trains via Lempel-Ziv complexity. Neural Comput. 16, 717-736.
4. Benayon, 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.-U. (2006). Complex networks: structure and dynamics. Phys. Rep. 424, 175-308.
6. Brunel, N. (2000). Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J. Comput. Neurosci. 8, 183-208.
7. Chiappalone, M., Bove, M., Vato, A., Tedesco, M., and Martinoia, S. (2006). Dissociated cortical networks show spontaneously correlated activity patterns during in vitro development. Brain Res. 1093, 41-53.
8. Christen, M., Kohn, A., Ott, T., and Stoop, R. (2006). Measuring spike pattern variability with the LempelZiv-distance. J. Neurosci. Methods 156, 342-350.
9. Emmert-Streib, F., and Scalas, E. (2010). Statistic complexity: combining Kolmogorov complexity with an ensemble approach. PLoS ONE 5, e12256. doi: 10.1371/journal.pone.0012256
10. Frégnac, Y., Rudolph, M., Davison, A. P., and Destexhe A. (2007). “Complexity in neuronal networks,” in Biological Networks, ed. François Képès (Singapore: World Scientific), 291-338.
11. Galas, D. J., Nykter, M., Carter, G. W., Price, N. D., and Shmulevich, I. (2010). Biological information as set-based complexity. IEEE Trans. Inf. Theory 56, 667-677.
12. Gewaltig, M.-O., and Diesmann, M. (2007). Nest (neural simulation tool). Scholarpedia 2, 1430-1434.
13. Gritsun, T. A., Le Feber, J., Stegenga, J., and Rutten, W. L. C. (2010). Network bursts in cortical cultures are best simulated using pacemaker neurons and adaptive synapses. Biol. Cybern. 102, 293-310
14. Itzhack, R., and Louzoun, Y. (2010). Random distance dependent attachment as a model for neural network generation in the Caenorhabditis elegans. Bioinformatics 26, 647-652
15. Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Trans. Neural Netw. 14, 1569-1572.
16. Koene, R. A., Tijms, B., van Hees, P., Postma, F., de Ridder, A., Ramakers, G. J. A., van Pelt, J., and van Ooyen, A. (2009). NETMORPH: a framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies. Neuroinformatics 7, 195-210.
17. Kriegstein, A. R., and Dichter, M. A. (1983). Morphological classification of rat cortical neurons in cell culture. J. Neurosci. 3, 1634-1647.
18. Kriener, B., Tetzlaff, T., Aertsen, A., Diesmann, M., and Rotter, S. (2008). Correlations and population dynamics in cortical networks. Neural Comput. 20, 2185-2226.
19. Kumar, A., Rotter, S., and Aertsen, A. (2008). Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model. J. Neurosci. 28, 5268-5280.
20. Latham, P. E., Richmond, B. J., Nelson, P. G., and Nirenberg, S. (2000). Intrinsic dynamics in neuronal networks. I. Theory. J. Neurophysiol. 83, 808-827.
21. Li, M., Chen, X., Li, X., Ma, B., and Vitányi, P. M. B. (2004). The similarity metric. IEEE Trans. Inf. Theory 50, 3250-3264.
22. Li, M., and Vitanyi, P. (1997). An Introduction to Kolmogorov Complexity and Its Applications, 2nd Edn. New York: Springer-Verlag.
23. Marom, S., and Shahaf, G. (2002). Development, learning and memory in large random networks of cortical neurons: lessons beyond anatomy. Q. Rev. Biophys. 35, 63-87.
24. Neel, D. L., and Orrison, M. E. (2006). The linear complexity of a graph. Electron. J. Comb. 13, 1-19.
25. Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Rev. 45, 167-256.
26. Nykter, M., Price, N. D., Larjo, A., Aho, T., Kauffman, S. A., Yli-Harja, O., and Shmulevich, I. (2008). Critical networks exhibit maximal information diversity in structure-dynamics relationships. Phys. Rev. Lett. 100, 058702.
27. Ostojic, S., Brunel, N., and Hakim, V. (2009). How connectivity, back-ground activity, and synaptic properties shape the cross-correlations between spike trains. J. Neurosci. 29, 10234-10253.
28. Paninski, L. (2003). Estimation of entropy and mutual information. Neural Comput. 15, 1191-1253.
29. Rapp, P. E., Zimmerman, I. D., Vining, E. P., Cohen, N., Albano, A. M., and Jimenez-Montano, M. A. (1994). The algorithmic complexity of neural spike trains increases during focal seizures. J. Neurosci. 14, 4731-4739.
30. Rotter, S., and Diesmann, M. (1999). Exact digital simulation of time-invariant linear systems with applications to neuronal modeling. Biol. Cybern. 81, 381-402.
31. 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.
32. Soriano, J., Rodrígez Martinez, M., Tlusty, T., and Moses, E. (2008). Development of input connections in neural cultures. Proc. Natl. Acad. Sci. U.S.A. 105, 13758-13763.
33. Sporns, O. (2011). Networks of the Brain. Cambridge, MA: The MIT Press.
34. Tsodyks, M., Uziel, A., and Markram, H. (2000). Synchrony generation in recurrent networks with frequencydependent synapses. J. Neurosci. 20, 1-5.
35. Tuckwell, H. C. (2006). Cortical network modeling: analytical methods for firing rates and some properties of networks of LIF neurons. J. Physiol. Paris 100, 88-99.
36. Voges, N., Guijarro, C., Aertsen, A., and Rotter, S. (2010). Models of cortical networks with long-range patchy projections. J. Comput. Neurosci. 28, 137-154.
37. Wagenaar, D. A., Pine, J., and Potter, S. M. (2006). An extremely rich repertoire of bursting patterns during the development of cortical cultures. BMC Neurosci. 7, 11-29. doi: 10.1186/1471-2202-7-11
38. Watts, D. J., and Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature 393, 440-442.
39. Wheeler, B. C., and Brewer, G. J. (2010). Designing neural networks in culture. Proc. IEEE 98, 398-406.