Correction to: Emergence of Slow-Switching Assemblies in Structured Neuronal Networks (PLOS Computational Biology, (2015), 11, 7, (e1004196), 10.1371/journal.pcbi.1004196);Emergence of Slow-Switching Assemblies in Structured Neuronal Networks

PLoS Computational Biology

Volumn 11, Issue 7, 2015, Pages

  Schaub, Michael T ^a   Billeh, Yazan N ^b   Anastassiou, Costas A ^c   Koch, Christof ^c   Barahona, Mauricio ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^c ALLEN INSTITUTE FOR BRAIN SCIENCE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMICS; EIGENVALUES AND EIGENFUNCTIONS; NEURAL NETWORKS;

ASSEMBLY DYNAMICS; COMPUTATIONAL BIOLOGY; EIGEN-VALUE; NETWORK CONNECTIVITY; NEURAL-NETWORKS; NEURONAL NETWORKS; SPATIO-TEMPORAL DYNAMICS; SPECTRAL PROPERTIES; SYNAPTIC WEIGHT; WEIGHT MATRICES;

NEURONS;

ARTICLE; CELLULAR DISTRIBUTION; CONNECTOME; CONTROLLED STUDY; EXCITATORY POSTSYNAPTIC POTENTIAL; INHIBITORY POSTSYNAPTIC POTENTIAL; LEAKY INTEGRATE AND FIRE NETWORK; NERVE CELL; NERVE CELL NETWORK; NERVOUS SYSTEM ELECTROPHYSIOLOGY; PHENOMENOLOGY; SLOW SWITCHING ASSEMBLY; ACTION POTENTIAL; ANATOMIC MODEL; ANATOMY AND HISTOLOGY; ANIMAL; BIOLOGICAL MODEL; COMPUTER SIMULATION; HUMAN; LONG TERM POTENTIATION; NERVE CELL PLASTICITY; PHYSIOLOGY; SPATIOTEMPORAL ANALYSIS; SYNAPTIC TRANSMISSION;

ACTION POTENTIALS; ANIMALS; COMPUTER SIMULATION; CONNECTOME; HUMANS; LONG-TERM POTENTIATION; MODELS, ANATOMIC; MODELS, NEUROLOGICAL; NERVE NET; NEURONAL PLASTICITY; SPATIO-TEMPORAL ANALYSIS; SYNAPTIC TRANSMISSION;

EID: 84938634138     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005288     Document Type: Erratum

References (74)

1. Buzsaki G, (2004) Large-scale recording of neuronal ensembles. Nature Neuroscience 7: 446–451. doi: 10.1038/nn1233 15114356
2. Du J, Blanche TJ, Harrison RR, Lester HA, Masmanidis SC, (2011) Multiplexed, High Density Electrophysiology with Nanofabricated Neural Probes. PLoS ONE 6. doi: 10.1371/journal.pone.0026204
3. Ahrens MB, Orger MB, Robson DN, Li JM, Keller PJ, (2013) Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nature Methods 10: 413+. doi: 10.1038/nmeth.2434 23524393
4. Buzsaki G, (2010) Neural Syntax: Cell Assemblies, Synapsembles, and Readers. Neuron 68: 362–385. doi: 10.1016/j.neuron.2010.09.023 21040841
5. Hebb DO, (1949) The Organization of Behavior. JohnWiley & Sons.
6. Harris K, (2005) Neural signatures of cell assembly organization. Nature Reviews Neuroscience 6: 399–407. doi: 10.1038/nrn1669 15861182
7. Song S, Sjostrom P, Reigl M, Nelson S, Chklovskii D, (2005) Highly nonrandom features of synaptic connectivity in local cortical circuits. Plos Biology 3: 507–519. doi: 10.1371/journal.pbio.0030068
8. Perin R, Berger TK, Markram H, (2011) A synaptic organizing principle for cortical neuronal groups. Proceedings of the National Academy of Sciences of the United States of America 108: 5419–5424. doi: 10.1073/pnas.1016051108 21383177
9. Yoshimura Y, Dantzker J, Callaway E, (2005) Excitatory cortical neurons form fine-scale functional networks. Nature 433: 868–873. doi: 10.1038/nature03252 15729343
10. Otsuka T, Kawaguchi Y, (2011) Cell Diversity and Connection Specificity between Callosal Projection Neurons in the Frontal Cortex. Journal of Neuroscience 31: 3862–3870. doi: 10.1523/JNEUROSCI.5795-10.2011 21389241
11. Ko H, Hofer SB, Pichler B, Buchanan KA, Sjoestroem PJ, et al. (2011) Functional specificity of local synaptic connections in neocortical networks. Nature 473: 87–U100. doi: 10.1038/nature09880 21478872
12. Harris KD, Mrsic-Flogel TD, (2013) Cortical connectivity and sensory coding. Nature 503: 51–58. doi: 10.1038/nature12654 24201278
13. Lefort S, Tomm C, Sarria JCF, Petersen CCH, (2009) The Excitatory Neuronal Network of the C2 Barrel Column in Mouse Primary Somatosensory Cortex. Neuron 61: 301–316. doi: 10.1016/j.neuron.2008.12.020 19186171
14. Yassin L, Benedetti BL, Jouhanneau JS, Wen JA, Poulet JFA, et al. (2010) An Embedded Subnetwork of Highly Active Neurons in the Neocortex. Neuron 68: 1043–1050. doi: 10.1016/j.neuron.2010.11.029 21172607
15. Shimono M, Beggs JM, (2014) Functional Clusters, Hubs, and Communities in the Cortical Micro- connectome. Cerebral Cortex. doi: 10.1093/cercor/bhu252 25336598
16. McGinley MJ, Westbrook GL, (2013) Hierarchical excitatory synaptic connectivity in mouse olfactory cortex. Proceedings of the National Academy of Sciences of the United States of America 110: 16193–16198. doi: 10.1073/pnas.1303813110 24043834
17. Savic I, Gulyas B, Larsson M, Roland P, (2000) Olfactory functions are mediated by parallel and hierarchical processing. Neuron 26: 735–745. doi: 10.1016/S0896-6273(00)81209-X 10896168
18. Felleman DJ, Van Essen DC, (1991) Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex 1: 1–47. doi: 10.1093/cercor/1.1.1 1822724
19. Ito M, Masuda N, Shinomiya K, Endo K, Ito K, (2013) Systematic Analysis of Neural Projections Reveals Clonal Composition of the Drosophila Brain. Current Biology 23: 644–655. doi: 10.1016/j.cub.2013.03.015 23541729
20. J Pavlides C, Greenstein Y, Grudman M, J W, (1988) Long-term potentiation in the dentate gyrus is induced preferentially on the positive phase of theta-rhythm. Brain Research 439: 383–387. doi: 10.1016/0006-8993(88)91499-0
21. Hyman J, Wyble B, Goyal V, Rossi C, Hasselmo M, (2003) Stimulation in hippocampal region CA1 in behaving rats yields long-term potentiation when delivered to the peak of theta and long-term depression when delivered to the trough. Journal of Neuroscience 23: 11725–11731. 14684874
22. Litwin-Kumar A, Doiron B, (2012) Slow dynamics and high variability in balanced cortical networks with clustered connections. Nature Neuroscince 15: 1498–1505. doi: 10.1038/nn.3220
23. van Vreeswijk C, Sompolinsky H, (1998) Chaotic balanced state in a model of cortical circuits. Neural Computation 10: 1321–1371. doi: 10.1162/089976698300017214 9698348
24. von Luxburg U, (2007) A tutorial on spectral clustering. Statistics and Computing 17: 395–416. doi: 10.1007/s11222-007-9033-z
25. Zhang X, Rao Nadakuditi R, Newman MEJ (2013) Spectra of random graphs with community structure and arbitrary degrees. URL http://arxiv.org/abs/1310.0046v1. arXiv:1310.0046.
26. Nadakuditi RR, Newman MEJ, (2013) Spectra of random graphs with arbitrary expected degrees. Physical Review E 87: 012803. doi: 10.1103/PhysRevE.87.012803
27. Murphy BK, Miller KD, (2009) Balanced Amplification: A New Mechanism of Selective Amplification of Neural Activity Patterns. Neuron 61: 635–648. doi: 10.1016/j.neuron.2009.02.005 19249282
28. Simon HA, Ando A, (1961) Aggregation of Variables in Dynamic Systems. Econometrica 29: 111–138. doi: 10.2307/1909285
29. Galán RF, (2008) On How Network Architecture Determines the Dominant Patterns of Spontaneous Neural Activity. PLoS ONE 3: e2148. doi: 10.1371/journal.pone.0002148 18478091
30. Delvenne JC, Yaliraki SN, Barahona M, (2010) Stability of graph communities across time scales. Proceedings of the National Academy of Sciences 107: 12755–12760. doi: 10.1073/pnas.0903215107
31. Schaub MT, Delvenne JC, Yaliraki SN, Barahona M, (2012) Markov Dynamics as a Zooming Lens for Multiscale Community Detection: Non Clique-Like Communities and the Field-of-View Limit. PLoS ONE 7: e32210. doi: 10.1371/journal.pone.0032210 22384178
32. Delvenne JC, Schaub MT, Yaliraki SN, Barahona M, Mukherjee A, Choudhury M, Peruani F, Ganguly N, Mitra B, (2013) The Stability of a Graph Partition: A Dynamics-Based Framework for Community Detection. In: Dynamics On and Of Complex Networks, Volume 2, Springer New York. pp. 221–242. URL http://dx.doi.org/10.1007/978-1-4614-6729-8_11.
33. Billeh YN, Schaub MT, Anastassiou CA, Barahona M, Koch C, (2014) Revealing cell assemblies at multiple levels of granularity. Journal of Neuroscience Methods 236: 92–106. doi: 10.1016/j.jneumeth.2014.08.011 25169050
34. Rajan K, Abbott LF, (2006) Eigenvalue Spectra of Random Matrices for Neural Networks. Physical Review Letters 97: 188104. doi: 10.1103/PhysRevLett.97.188104 17155583
35. Sommers HJ, Crisanti A, Sompolinsky H, Stein Y, (1988) Spectrum of Large Random Asymmetric Matrices. Physical Review Letters 60: 1895–1898. doi: 10.1103/PhysRevLett.60.1895 10038170
36. Goldman MS, (2009) Memory without Feedback in a Neural Network. Neuron 61: 621–634. doi: 10.1016/j.neuron.2008.12.012 19249281
37. Trefethen LN, Embree M, (2005) Spectra and pseudospectra: the behavior of nonnormal matrices and operators. Princeton University Press.
38. Golub H, Van Loan FC, (1996) Matrix Computations. Baltimore and London: John Hopkins University Press, 3 edition.
39. Stewart GW (2001) Matrix Algorithms Volume 2: Eigensystems, volume 2. Siam.
40. Rutishauser U, Douglas RJ, Slotine JJ, (2011) Collective stability of networks of winner-take-all circuits. Neural Computation 23: 735–773. doi: 10.1162/NECO_a_00091 21162667
41. Watts DJ, Strogatz SH, (1998) Collective dynamics of ‘small-world’ networks. Nature 393: 440–442. doi: 10.1038/30918 9623998
42. Bullmore E, Sporns O, (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10: 186–198. doi: 10.1038/nrn2618 19190637
43. Meunier D, Lambiotte R, Bullmore ET, (2010) Modular and hierarchically modular organization of brain networks. Frontiers in Neuroscience 4. doi: 10.3389/fnins.2010.00200 21151783
44. Barahona M, Pecora LM, (2002) Synchronization in Small-World Systems. Physical Review Letters 89: 054101. doi: 10.1103/PhysRevLett.89.054101 12144443
45. Barabsi AL, Albert R, (1999) Emergence of Scaling in Random Networks. Science 286: 509–512. doi: 10.1126/science.286.5439.509
46. Ambrosingerson J, Granger R, Lynch G, (1990) Simulation of paleocortex performs hierarchical clustering. Science 247: 1344–1348. doi: 10.1126/science.2315702
47. Laughlin S, Sejnowski T, (2003) Communication in neuronal networks. Science 301: 1870–1874. doi: 10.1126/science.1089662 14512617
48. Kepecs A, Fishell G, (2014) Interneuron cell types are fit to function. Nature 505: 318326. doi: 10.1038/nature12983
49. Roux L, Buzsáki G, (2014) Tasks for inhibitory interneurons in intact brain circuits. Neuropharmacology: -.
50. Silberberg G, Markram H, (2007) Disynaptic inhibition between neocortical pyramidal cells mediated by martinotti cells. Neuron 53: 735–746. doi: 10.1016/j.neuron.2007.02.012 17329212
51. Xu H, Jeong HY, Tremblay R, Rudy B, (2013) Neocortical Somatostatin-Expressing GABAergic Interneurons Disinhibit the Thalamorecipient Layer 4. Neuron 77: 155–167. doi: 10.1016/j.neuron.2012.11.004 23312523
52. Lee S, Kruglikov I, Huang ZJ, Fishell G, Rudy B, (2013) A disinhibitory circuit mediates motor integration in the somatosensory cortex. Nature Neuroscience 16: 1662–1670. doi: 10.1038/nn.3544 24097044
53. Pi HJ, Hangya B, Kvitsiani D, Sanders JI, Huang ZJ, et al. (2013) Cortical interneurons that specialize in disinhibitory control. Nature 503: 521–524. doi: 10.1038/nature12676 24097352
54. Pfeffer CK, Xue M, He M, Huang ZJ, Scanziani M, (2013) Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nature Neuroscience 16: 1068–1076. doi: 10.1038/nn.3446 23817549
55. Niven JE, Laughlin SB, (2008) Energy limitation as a selective pressure on the evolution of sensory systems. Journal of Experimental Biology 211: 1792–1804. doi: 10.1242/jeb.017574 18490395
56. Larremore DB, Shew WL, Restrepo JG, (2011) Predicting Criticality and Dynamic Range in Complex Networks: Effects of Topology. Physical Review Letters 106. doi: 10.1103/PhysRevLett.106.058101 21405438
57. Beggs J, Plenz D, (2003) Neuronal avalanches in neocortical circuits. Journal of Neuroscience 23: 11167–11177. 14657176
58. Kenet T, Bibitchkov D, Tsodyks M, Grinvald A, Arieli A, (2003) Spontaneously emerging cortical representations of visual attributes. Nature 425: 954–956. doi: 10.1038/nature02078 14586468
59. Ben-Yishai R, Bar-Or R, Sompolinsky H, (1995) Theory of orientation tuning in visual-cortex. Proceedings of the National Academy of Sciences of the United States of America 92: 3844–3848. doi: 10.1073/pnas.92.9.3844 7731993
60. Somers D, Nelson S, Sur M, (1995) An emergent model of orientation selectivity in cat visual cortical simple cells. Journal of Neuroscience 15: 5448–5465. 7643194
61. Ernst U, Pawelzik K, Sahar-Pikielny C, Tsodyks M, (2001) Intracortical origin of visual maps. Nature Neuroscience 4: 431–436. doi: 10.1038/86089 11276235
62. Sasaki T, Matsuki N, Ikegaya Y, (2007) Metastability of active CA3 networks. Journal of Neuroscience 27: 517–528. doi: 10.1523/JNEUROSCI.4514-06.2007 17234584
63. Litwin-Kumar A, Doiron B, (2014) Formation and maintenance of neuronal assemblies through synaptic plasticity. Nature Communications 5. doi: 10.1038/ncomms6319 25395015
64. Harris K, Csicsvari J, Hirase H, Dragoi G, Buzsaki G, (2003) Organization of cell assemblies in the hippocampus. Nature 424: 552–556. doi: 10.1038/nature01834 12891358
65. Foster D, Wilson M, (2006) Reverse replay of behavioural sequences in hippocampal place cells during the awake state. Nature 440: 680–683. doi: 10.1038/nature04587 16474382
66. Riehle A, Grun S, Diesmann M, Aertsen A, (1997) Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278: 1950–1953. doi: 10.1126/science.278.5345.1950 9395398
67. Hahnloser R, Kozhevnikov A, Fee M, (2002) An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature 419: 65–70. doi: 10.1038/nature00974 12214232
68. Rokem A, Watzl S, Gollisch T, Stemmler M, Herz A, et al. (2006) Spike-timing precision underlies the coding efficiency of auditory receptor neurons. Journal of Neurophysiology 95: 2541–2552. doi: 10.1152/jn.00891.2005 16354733
69. Hromadka T, DeWeese MR, Zador AM, (2008) Sparse representation of sounds in the unanesthetized auditory cortex. PLoS Biology 6: 124–137. doi: 10.1371/journal.pbio.0060016
70. Buzsaki G, Mizuseki K, (2014) The log-dynamic brain: how skewed distributions affect network operations. Nature Reviews Neuroscience 15: 264–278. doi: 10.1038/nrn3687 24569488
71. Oh SW, Harris JA, Ng L, Winslow B, Cain N, et al. (2014) A mesoscale connectome of the mouse brain. Nature 508: 207–214. doi: 10.1038/nature13186 24695228
72. Clopath C, Buesing L, Vasilaki E, Gerstner W, (2010) Connectivity reflects coding: a model of voltage-based STDP with homeostasis. Nature Neuroscience 13: 344–352. doi: 10.1038/nn.2479 20098420
73. Reimann MW, Anastassiou CA, Perin R, Hill SL, Markram H, et al. (2013) A Biophysically Detailed Model of Neocortical Local Field Potentials Predicts the Critical Role of Active Membrane Currents. Neuron 79: 375–390. doi: 10.1016/j.neuron.2013.05.023 23889937
74. Batagelj V, Brandes U, (2005) Efficient generation of large random networks. Phys Rev E 71: 036113. doi: 10.1103/PhysRevE.71.036113