Impact of network topology on inference of synaptic connectivity from multi-neuronal spike data simulated by a large-scale cortical network model

Journal of Computational Neuroscience

Volumn 35, Issue 1, 2013, Pages 109-124

  Kobayashi, Ryota ^a   Kitano, Katsunori ^a  

^a RITSUMEIKAN UNIVERSITY   (Japan)

Author keywords

Connectivity inference; Functional connections; Large scale network simulation; Network topology; Synaptic connectivity

Indexed keywords

NEURONS; POPULATION STATISTICS; SMALL-WORLD NETWORKS;

CONNECTIVITY INFERENCE; CORTICAL NETWORK MODEL; FUNCTIONAL CONNECTION; INFER NETWORKS; LARGE POPULATION; LARGE-SCALE NETWORK SIMULATION; LARGE-SCALES; NETWORK TOPOLOGY; NEURAL-PROCESSING; SYNAPTIC CONNECTIVITY;

NETWORK TOPOLOGY;

ACCURACY; ALGORITHM; ARTICLE; CORTICAL NETWORK MODEL; DATA ANALYSIS; ENTROPY; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; NERVE CELL EXCITABILITY; NERVE CELL INHIBITION; NERVE CELL NETWORK; PRIORITY JOURNAL; SENSITIVITY ANALYSIS; SIMULATION; SYNAPTIC INHIBITION; SYNAPTIC TRANSMISSION;

EID: 84880240783     PISSN: 09295313     EISSN: 15736873     Source Type: Journal    
DOI: 10.1007/s10827-013-0443-y     Document Type: Article

References (54)

1. Baldi, P.; Brunak, S.; Chauvin, Y.; Andersen, C. A. F.; Nielsen, H. (2000). Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics, 16, 412-424.
2. Becchetti, A.; Gullo, F.; Bruno, G.; Dossi, E.; Lecchi, M.; Wanke, E. (2012). Exact distinction of excitatory and inhibitory neurons in neural networks: a study with GFP-GAD67 neurons optically and electrophysiologically recognized on multielectrode arrays. Frontiers in Neural Circuits, 6, 63.
3. Beggs, J. M.; Plenz, D. (2003). Neuronal avalanches in neocortical circuits. Journal of Neuroscience, 23, 11167-11177.
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. Cocco, S.; Leibler, S.; Monasson, R. (2009). Neuronal coupling between retinal ganglion cells inferred by efficient inverse statistical physics methods. Proceedings of the National Academy of Sciences of the United States of America, 106, 14058-14062.
6. Cossart, R.; Aronov, D.; Yuste, R. (2003). Attractor dynamics of network UP states in the neocortex. Nature, 423, 283-288.
7. Destexhe, A.; Paré, D. (1999). Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo. Journal of Neurophysiology, 81, 1531-1547.
8. Destexhe, A.; Mainen, Z. F.; Sejnowski, T. J. (1998). Kinetic models of synaptic transmission. In C. Koch & I. Segev (Eds.), Methods in neural modeling (pp. 1-25). Cambridge, MA: MIT.
9. Destexhe, A.; Rudolph, M.; Fellous, J. M.; Sejnowski, T. J. (2001). Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience, 107, 13-24.
10. Erisir, A.; Lau, D.; Rudy, B.; Leonard, C. S. (1999). Function of specific K + channels in sustained high-frequency firing of fast-spiking neocortical interneurons. Journal of Neurophysiology, 82, 2476-2489.
11. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27, 861-874.
12. Field, G. D.; Gauthier, J. L.; Sher, A.; Greschner, M.; Machado, T. A.; Jepson, L. H.; et al. (2010). Functional connectivity in the retina at the resolution of photoreceptors. Nature, 467, 673-677.
13. Friston, K. J. (2011). Functional and effective connectivity: a review. Brain Connect, 1, 13-36.
14. Garofalo, M.; Nieus, T.; Massobrio, P.; Martinoia, S. (2009). Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks. PloS One, 4, e6482.
15. Gerhard, F.; Pipa, G.; Lima, B.; Neuenschwander, S.; Gerstner, W. (2011). Extraction of network topology from multi-electrode recording: is there a small-world effect? Frontiers in Computational Neuroscience, 5, 4.
16. Gerstner, W.; Kistler, W. M. (2002). Spiking neuron models: single neurons, populations, plasticity. Cambridge: Cambridge University Press.
17. Gourevitch, B.; Eggermont, J. (2007). Evaluating information transfer between auditory cortical neurons. Journal of Neurophysiology, 97, 2533-2543.
18. Gupta, A.; Wang, Y.; Markram, H. (2000). Organizing principles for a diversity of GABAergic interneurons and synapses in the neocortex. Science, 287, 273-278.
19. Ikegaya, Y.; Aaron, G.; Cossart, R.; Aronov, D.; Lampl, I.; Ferster, D.; et al. (2004). Synfire chains and cortical songs: temporal modules of cortical activity. Science, 304, 559-564.
20. Ito, S.; Hansen, M. E.; Heiland, R.; Lumsdaine, A.; Litke, A. M.; Beggs, J. M. (2011). Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model. PloS One, 6, e27431.
21. Jolivet, R.; Rauch, A.; Lüscher, H. R.; Gerstner, W. (2006). Predicting spike timing of neocortical pyramidal neurons by simple threshold models. Journal of Computational Neuroscience, 21, 35-49.
22. Kitano, K.; Fukai, T. (2007). Variability v.s. synchronicity of neural activity local cortical network models with different wiring topologies. Journal of Computational Neuroscience, 23, 237-250.
23. Kobayashi, R.; Shinomoto, S. (2007). State space method for predicting the spike times of a neuron. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 75, 011925.
24. Kobayashi, R.; Tsubo, Y.; Shinomoto, S. (2009). Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold. Frontiers in Computational Neuroscience, 3, 9.
25. Kulkarni, J. E.; Paninski, L. (2007). Common-input models for multiple neural spike-train data. Network, 18, 375-407.
26. Kummer, M.; Kirmse, K.; Witte, O. W.; Holthoff, K. (2012). Reliable in vivo identification of both GABAergic and glutamatergic neurons using Emx1-Cre driven fluorescent reporter expression. Cell Calcium, 52, 182-189.
27. Marquardt, D. W. (1963). An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics, 11, 431-441.
28. Mensi, S.; Naud, R.; Pozzorini, C.; Avermann, M.; Petersen, C. C. H.; Gerstner, W. (2012). Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. Journal of Neurophysiology, 107, 1756-1775.
29. Morrison, A.; Straube, S.; Plesser, H. E.; Diesmann, M. (2007). Exact subthreshold integration with continuous spike times in discrete-time neural network simulations. Neural Computation, 19, 47-79.
30. Nauhaus, I.; Busse, L.; Carandini, M.; Ringach, D. L. (2009). Stimulus contrast modulates functional connectivity in visual cortex. Nature Neuroscience, 12, 70-76.
31. Neymotin, S. A.; Jacobs, K. M.; Fenton, A. A.; Lytton, W. W. (2011). Synaptic information transfer in computer models of neocortical columns. Journal of Computational Neuroscience, 30, 69-84.
32. Ohiorhenuan, I. E.; Victor, J. D. (2011). Information-geometric measure of 3-neuron firing patterns characterizes scale-dependence in cortical networks. Journal of Computational Neuroscience, 30, 125-141.
33. Okatan, M.; Wilson, M. A.; Brown, E. N. (2005). Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity. Neural Computation, 17, 1927-1961.
34. Paninski, L. (2004). Maximum likelihood estimation of cascade point-process neural encoding models. Network, 15, 243-262.
35. Perin, R.; Berger, T. K.; Markram, H. (2011). A synaptic organizing principle for cortical neuronal groups. Proceedings of the National Academy of Sciences USA, 108, 5419-5424.
36. Perkel, D. H.; Gerstein, G. L.; Moore, G. P. (1967). Neural spike trains and stochastic point processes II. Simultaneous spike trains. Biophysical Journal, 7, 419-440.
37. Petersen, C. C. H. (2002). Short-term dynamics of synaptic transmission within the excitatory neuronal network of rat layer 4 barrel cortex. Journal of Neurophysiology, 87, 2904-2914.
38. Pillow, J. W.; Shlens, J.; Paninski, L.; Sher, A.; Litke, A. M.; Chichilnisky, E. J.; et al. (2008). Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature, 454, 995-999.
39. Platkiewicz, J.; Brette, R. (2010). A threshold equation for action potential initiation. PLoS Computational Biology, 6, 1000850.
40. Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85, 461-464.
41. Shinomoto, S. (2010). Fitting a stochastic spiking model to neuronal current injection data. Neural Networks, 23, 764-769.
42. Shlens, J.; Field, G. D.; Gauthier, J. L.; Grivich, M. I.; Petrusca, D.; Sher, A.; et al. (2006). The structure of multi-neuron firing patterns in primate retina. Journal of Neuroscience, 26, 8254-8266.
43. Song, S.; Sjöström, P. J.; Reigl, M.; Nelson, S.; Chklovskii, D. B. (2005). Highly nonrandom features of synaptic connectivity in local cortical circuits. PLoS Biology, 3, e68.
44. Sporns, O.; Zwi, J. D. (2004). The small world of the cerebral cortex. Neuroinformatics, 2, 145-162.
45. Stetter, O.; Battaglia, D.; Soriano, J.; Geisel, T. (2012). Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals. PLoS Computational Biology, 8, e1002653.
46. Takahashi, N.; Sasaki, T.; Matsumoto, W.; Matsuki, N.; Ikegaya, Y. (2010). Circuit topology for synchronizing neurons in spontaneously active networks. Proceedings of the National Academy of Sciences of the United States of America, 107, 10244-10249.
47. Tang, A.; Jackson, D.; Hobbs, J.; Chen, W.; Smith, J. L.; Patel, H.; et al. (2008). A maximum entropy model applied to spatial and temporal correlations from cortical networks in vitro. Journal of Neuroscience, 28, 505-518.
48. Truccolo, W.; Eden, U. T.; Fellows, M. R.; Donoghue, J. P.; Brown, E. N. (2005). A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. Journal of Neurophysiology, 93, 1074-1089.
49. 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, 710-723.
50. Tsodyks, M.; Uziel, A.; Markram, H. (2000). Synchrony generation in recurrent networks with frequency-depedent synapses. Journal of Neuroscience, 20, RC50.
51. Wang, X. J.; Buzsáki, G. (1996). Gamma oscillation by synaptic inhibition in a hippocampal interneuron network model. Journal of Neuroscience, 16, 6402-6413.
52. Watts, D. J.; Strogatz, S. H. (1998). Collective dynamics of 'small-world' networks. Nature, 393, 440-442.
53. Yamauchi, S.; Kim, H.; Shinomoto, S. (2011). Elemental spiking neuron model for reproducing diverse firing patterns and predicting precise firing times. Frontiers in Computational Neuroscience, 5, 42.
54. Yger, P.; Boustani, S. E.; Destexhe, A.; Frégnac, Y. (2011). Topologically invariant macroscopic statistics in balanced networks of conductance-based integrate-and-fire neurons. Journal of Computational Neuroscience, 31, 229-245.