Cell assembly signatures defined by short-term synaptic plasticity in cortical networks

International Journal of Neural Systems

Volumn 25, Issue 7, 2015, Pages

  Carrillo Reid, Luis ^a   Lopez Huerta, Violeta G ^b   Garcia Munoz, Marianela ^b   Theiss, Stephan ^c   Arbuthnott, Gordon W ^b  

^a Department of Biological Sciences ^*  (United States)

^b OKINAWA INSTITUTE OF SCIENCE AND TECHNOLOGY GRADUATE UNIVERSITY   (Japan)

^c HEINRICH HEINE UNIVERSITY   (Germany)

Author keywords

Calcium imaging; Cell assemblies; Microelectrode arrays; Short term synaptic plasticity; Whole cell recordings

Indexed keywords

CELLS; CYTOLOGY; ELECTROPHYSIOLOGY; GRAPH THEORY; MICROELECTRODES; RECURRENT NEURAL NETWORKS;

CALCIUM IMAGING; CELL ASSEMBLY; MICROELECTRODE ARRAY; SHORT-TERM SYNAPTIC PLASTICITY; WHOLE CELL;

NEURONS;

CALCIUM;

ACTION POTENTIAL; ANIMAL; BIOLOGICAL MODEL; BRAIN; CELL CULTURE; FLUORESCENCE IMAGING; METABOLISM; MOUSE; NERVE CELL; NERVE CELL PLASTICITY; NERVE TRACT; PATCH CLAMP TECHNIQUE; PHYSIOLOGY; SIGNAL PROCESSING;

ACTION POTENTIALS; ANIMALS; BRAIN; CALCIUM; CELLS, CULTURED; MICE; MODELS, NEUROLOGICAL; NEURAL PATHWAYS; NEURONAL PLASTICITY; NEURONS; OPTICAL IMAGING; PATCH-CLAMP TECHNIQUES; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84940395028     PISSN: 01290657     EISSN: 17936462     Source Type: Journal    
DOI: 10.1142/S0129065715500264     Document Type: Article

References (78)

1. C. M. Brooks and J. C. Eccles, An electrical hypothesis of central inhibition, Nature 159 (1947) 760-764.
2. D. O. Hebb, The Organization of Behaviour (Wiley, NY, 1949).
3. K. D. Harris, Neural signatures of cell assembly organization, Nat. Rev. Neurosci. 6 (2005) 399-407, doi:10.1038/nrn1669.
4. D. J. Wallace and J. N. Kerr, Chasing the cell assembly, Curr. Opin. Neurobiol. 20 (2010) 296-305.
5. N. C. Bentzen, A. M. Zhabotinsky and J. L. Laugesen, Modeling of glutamate-induced dynamical patterns, Int. J. Neural Syst. 19 (2009) 395-407, doi:10.1142/S0129065709002105.
6. F. Cona and M. Ursino, A multi-layer neural-mass model for learning sequences using theta/gamma oscillations, Int. J. Neural Syst. 23 (2013) 1250036, doi:10.1142/S0129065712500360.
7. J. Iglesias and A. E. Villa, Emergence of preferred firing sequences in large spiking neural networks during simulated neuronal development, Int. J. Neural Syst. 18 (2008) 267-277, doi:10.1142/S0129065708001580.
8. A. Mohemmed, S. Schliebs, S. Matsuda and N. Kasabov, Span: Spike pattern association neuron for learning spatio-temporal spike patterns, Int. J. Neural Syst. 22 (2012) 1250012, doi:10.1142/ S0129065712500128.
9. C. R. Huyck, in Emerging Neural Architectures Based on Neuroscience, eds. S. Wermter, J. Austin and O. Willshaw (Springer, Heidelberg, 2001), pp. 383-397.
10. A. Lansner, Associative memory models: From the cell-assembly theory to biophysically detailed cortex simulations, Trends Neurosci. 32 (2009) 178-186, doi:S0166-2236(09)00017-4 [pii] 10.1016/j.tins. 2008.12.002.
11. A. M. Graybiel, Building action repertoires: Memory and learning functions of the basal ganglia, Curr. Opin. Neurobiol. 5 (1995) 733-741, doi:0959-4388(95)80100-6 [pii].
12. H. Ko, et al. Functional specificity of local synaptic connections in neocortical networks, Nature 473 (2011) 87-91, doi:10.1038/nature09880.
13. S. Song, P. J. Sjostrom, M. Reigl, S. Nelson and D. B. Chklovskii, Highly nonrandom features of synaptic connectivity in local cortical circuits, PLoS Biol. 3 (2005) e68, doi:10.1371/journal.pbio.0030068.
14. Y. Yoshimura and E. M. Callaway, Fine-scale specificity of cortical networks depends on inhibitory cell type and connectivity, Nat. Neurosci. 8 (2005) 1552-1559, doi:10.1038/nn1565.
15. C. D. Harvey, P. Coen and D. W. Tank, Choicespecific sequences in parietal cortex during a virtual-navigation decision task, Nature 484 (2012) 62-68, doi:10.1038/nature10918.
16. E. Seidemann, I. Meilijson, M. Abeles, H. Bergman and E. Vaadia, Simultaneously recorded single units in the frontal cortex go through sequences of discrete and stable states in monkeys performing a delayed localization task, J. Neurosci. 16 (1996) 752-768.
17. E. H. Baeg et al., Dynamics of population code for working memory in the prefrontal cortex, Neuron 40 (2003) 177-188.
18. S. Fujisawa, A. Amarasingham, M. T. Harrison and G. Buzsaki, Behavior-dependent short-term assembly dynamics in the medial prefrontal cortex, Nat. Neurosci. 11 (2008) 823-833, doi:10.1038/nn.2134.
19. D. A. Crowe, B. B. Averbeck and M. V. Chafee, Rapid sequences of population activity patterns dynamically encode task-critical spatial information in parietal cortex, J. Neurosci. 30 (2010) 11640-11653, doi:10.1523/JNEUROSCI.0954-10.2010.
20. G. Dragoi and G. Buzsaki, Temporal encoding of place sequences by hippocampal cell assemblies. Neuron 50 (2006) 145-157, doi:S0896-6273(06)00165-6 [pii] 10.1016/j.neuron.2006.02.023.
21. G. Dragoi and S. Tonegawa, Preplay of future place cell sequences by hippocampal cellular assemblies, Nature 469 (2011) 397-401, doi:nature09633 [pii] 10.1038/nature09633.
22. K. D. Harris, J. Csicsvari, H. Hirase, G. Dragoi and G. Buzsaki, Organization of cell assemblies in the hippocampus, Nature 424 (2003) 552-556, doi:10.1038/nature01834 nature01834 [pii].
23. E. Pastalkova, V. Itskov, A. Amarasingham and G. Buzsaki, Internally generated cell assembly sequences in the rat hippocampus, Science 321 (2008) 1322-1327, doi:321/5894/1322 [pii] 10.1126/science.1159775.
24. R. Perin, T. K. Berger and H. Markram, A synaptic organizing principle for cortical neuronal groups, Proc. Nat. Acad. Sci. USA 108 (2011) 5419-5424, doi:10.1073/pnas.1016051108.
25. J. K. Jun et al., Heterogenous population coding of a short-term memory and decision task, J. Neurosci. 30 (2010) 916-929, doi:10.1523/JNEUROSCI.2062-09.2010.
26. T. Sasaki, N. Matsuki and Y. Ikegaya, Metastability of active CA3 networks, J. Neurosci. 27 (2007) 517-528, doi:27/3/517 [pii] 10.1523/JNEUROSCI.4514-06.2007.
27. R. Singh and C. Eliasmith, Higher-dimensional neurons explain the tuning and dynamics of working memory cells, J. Neurosci. 26 (2006) 3667-3678, doi:10.1523/JNEUROSCI.4864-05.2006.
28. Y. Wang et al., Heterogeneity in the pyramidal network of the medial prefrontal cortex, Nat. Neurosci. 9 (2006) 534-542, doi:10.1038/nn1670.
29. W. Chen, J. P. Hobbs, A. Tang and J. M. Beggs, A few strong connections: Optimizing information retention in neuronal avalanches, BMC Neurosci 11 (2010) 3, doi:1471-2202-11-3 [pii] 10.1186/1471-2202-11-3.
30. C. Clopath, L. Busing, E. Vasilaki and W. Gerstner, Connectivity reflects coding: A model of voltagebased STDP with homeostasis, Nat. Neurosci. 13 (2010) 344-352, doi:10.1038/nn.2479.
31. R. Fernandez Galan, On how network architecture determines the dominant patterns of spontaneous neural activity, PLoS One 3 (2008) e2148, doi:10.1371/journal.pone.0002148.
32. C. J. Honey, R. Kotter, M. Breakspear and O. Sporns, Network structure of cerebral cortex shapes functional connectivity on multiple time scales, Proc. Natl. Acad. Sci. USA 104 (2007) 10240-10245, doi:10.1073/pnas.0701519104.
33. A. Luczak and J. N. Maclean, Default activity patterns at the neocortical microcircuit level, Front Integr. Neurosci. 6 (2012) 30, doi:10.3389/fnint. 2012.00030.
34. L. Carrillo-Reid et al., Encoding network states by striatal cell assemblies, J. Neurophysiol. 99 (2008) 1435-1450, doi:01131.2007 [pii] 10.1152/jn. 01131.2007.
35. R. Cossart, D. Aronov and R. Yuste, Attractor dynamics of network UP states in the neocortex, Nature 423 (2003) 283-288, doi:10.1038/ nature01614 nature01614 [pii].
36. B. Q. Mao, F. Hamzei-Sichani, D. Aronov, R. C. Froemke and R. Yuste, Dynamics of spontaneous activity in neocortical slices, Neuron 32 (2001) 883-898, doi:S0896-6273(01)00518-9 [pii].
37. L. Carrillo-Reid, S. Hernandez-Lopez, D. Tapia, E. Galarraga and J. Bargas, Dopaminergic modulation of the striatal microcircuit: Receptorspecific configuration of cell assemblies, J. Neurosci. 31 (2011) 14972-14983, doi:10.1523/JNEUROSCI. 3226-11.2011.
38. Y. Ikegaya et al., Synfire chains and cortical songs: Temporal modules of cortical activity, Science 304 (2004) 559-564, doi:10.1126/science.1093173 304/5670/559 [pii].
39. T. Shmiel et al., Neurons of the cerebral cortex exhibit precise interspike timing in correspondence to behavior, Proc. Nat. Acad. Sci. USA 102 (2005) 18655-18657, doi:0509346102 [pii] 10.1073/pnas.0509346102.
40. S. L. Brown, J. Joseph and M. Stopfer, Encoding a temporally structured stimulus with a temporally structured neural representation, Nat. Neurosci. 8 (2005) 1568-1576, doi:nn1559 [pii] 10.1038/nn 1559.
41. S. Schreiber, J. M. Fellous, D. Whitmer, P. Tiesinga and Sejnowski, T. J. A new correlation-based measure of spike timing reliability, Neurocomputing 52-4 (2003) 925-931, doi: 10.1016/S0925-2312(02) 00838-X.
42. E. Garbarine, J. DePasquale, V. Gadia, R. Polikar and G. Rosen, Information-theoretic approaches to SVM feature selection for metagenome read classification, Comput. Biol. Chem. 35 (2011) 199-209, doi:10.1016/j.compbiolchem.2011.04.007.
43. R. Islamaj Dogan and Z. Lu, Click-words: Learning to predict document keywords from a user perspective, Bioinformatics 26 (2010) 2767-2775, doi:10.1093/bioinformatics/btq459.
44. M. Lan, C. L. Tan, J. Su and Y. Lu, Supervised and traditional term weighting methods for automatic text categorization, IEEE Trans. Pattern Anal. Mach. Intell. 31 (2009) 721-735, doi:10.1109/TPAMI.2008.110.
45. L. Carrillo-Reid et al., Activation of the cholinergic system endows compositional properties to striatal cell assemblies, J. Neurophysiol. 101 (2009) 737-749, doi:90975.2008 [pii] 10.1152/jn.90975.2008.
46. A. K. Lee and M. A. Wilson, Memory of sequential experience in the hippocampus during slow wave sleep, Neuron 36 (2002) 1183-1194, doi:S0896627302010966 [pii].
47. R. Diestel, Graph Theory, 3rd edn. (Springer, Heidelberg, 2005).
48. M. Abeles, G. Hayon and D. Lehmann, Modeling compositionality by dynamic binding of synfire chains, J. Comput. Neurosci. 17 (2004) 179-201, doi:10.1023/B:JCNS.0000037682.18051.5f 5277002 [pii].
49. E. Bienenstock and S. Geman, in The Handbook of Brain Theory and Neural Networks, ed. M. A. Arbib (The MIT Press, Cambridge, MA, 1995), pp. 223-226.
50. B. Hammer, in The Handbook of Brain Theory and Neural Networks, ed. M. A. Arbib, Ch. 244-248 (The MIT Press, Cambridge, MA, 2003).
51. S. Bandyopadhyay, S. A. Shamma and P. O. Kanold, Dichotomy of functional organization in the mouse auditory cortex, Nat. Neurosci. 13 (2010) 361-368, doi:nn.2490 [pii] 10.1038/nn.2490.
52. J. M. Beggs and D. Plenz, Neuronal avalanches are diverse and precise activity patterns that are stable for many hours in cortical slice cultures, J. Neurosci. 24 (2004) 5216-5229, doi:10.1523/JNEUROSCI. 0540-04.200424/22/5216 [pii].
53. D. A. Dombeck, A. N. Khabbaz, F. Collman, T. L. Adelman and D. W. Tank, Imaging largescale neural activity with cellular resolution in awake, mobile mice, Neuron 56 (2007) 43-57, doi:10.1016/j.neuron.2007.08.003.
54. L. L. Bologna et al., Low-frequency stimulation enhances burst activity in cortical cultures during development, Neuroscience 165 (2010) 692-704, doi:S0306-4522(09)01864-8 [pii] 10.1016/j.neuroscience. 2009.11.018.
55. V. Pasquale, P. Massobrio, L. L. Bologna, M. Chiappalone and S. Martinoia, Self-organization and neuronal avalanches in networks of dissociated cortical neurons, Neuroscience 153 (2008) 1354-1369, doi:S0306-4522(08)00402-8 [pii] 10.1016/j.neuroscience. 2008.03.050.
56. D. A. Wagenaar, R. Madhavan, J. Pine and S. M. Potter, Controlling bursting in cortical cultures with closed-loop multi-electrode stimulation, J. Neurosci. 25 (2005) 680-688, doi:25/3/680 [pii] 10.1523/JNEUROSCI.4209-04.2005.
57. M. Chalk et al., Attention reduces stimulusdriven gamma frequency oscillations and spike field coherence in V1, Neuron 66 (2010) 114-125, doi:10.1016/j.neuron.2010.03.013.
58. P. Fries, J. H. Reynolds, A. E. Rorie and R. Desimone, Modulation of oscillatory neuronal synchronization by selective visual attention, Science 291 (2001) 1560-1563, doi:10.1126/science. 291.5508.1560.
59. B. Pesaran, J. S. Pezaris, M. Sahani, P. P. Mitra and R. A. Andersen, Temporal structure in neuronal activity during working memory in macaque parietal cortex, Nat. Neurosci. 5 (2002) 805-811, doi:10.1038/nn890.
60. R. W. Hamming, Error detecting and error correcting codes, The Bell Syst. Tech. J. 29 (1950) 147-160.
61. N. Levy, D. Horn, I. Meilijson and E. Ruppin, Distributed synchrony in a cell assembly of spiking neurons, Neural. Netw. 14 (2001) 815-824, doi:S0893-6080(01)00044-2 [pii].
62. T. Shmiel et al., Temporally precise cortical firing patterns are associated with distinct action segments, J. Neurophysiol. 96 (2006) 2645-2652, doi:10.1152/jn.00798.2005.
63. D. Fisher, I. Olasagasti, D. W. Tank, E. R. Aksay and M. S. Goldman, A modeling framework for deriving the structural and functional architecture of a short-term memory microcircuit, Neuron 79 (2013) 987-1000, doi:10.1016/j.neuron.2013.06.041.
64. R. Madhavan, Z. C. Chao and S. M. Potter, Plasticity of recurring spatiotemporal activity patterns in cortical networks, Phys. Biol. 4 (2007) 181-193, doi:S1478-3975(07)52301-0 [pii] 10.1088/1478-3975/4/3/005.
65. M. Chiappalone, A. Vato, L. Berdondini, Koudelka-Hep, M. & S. Martinoia, Network dynamics and synchronous activity in cultured cortical neurons, Int. J. Neural Syst. 17 (2007) 87-103, doi:10.1142/S0129065707000968.
66. J. Friedrich, R. Urbanczik and W. Senn, Codespecific learning rules improve action selection by populations of spiking neurons, Int. J. Neural Syst. 24 (2014) 1450002, doi:10.1142/S0129065714500026.
67. S. Ghosh-Dastidar and H. Adeli, Spiking neural networks, Int. J. Neural Syst. 19 (2009) 295-308, doi:10.1142/S0129065709002002.
68. W. K. Wong, Z. Wang, B. Zhen and S. Leung, Relationship between applicability of currentbased synapses and uniformity of firing patterns, Int. J. Neural Syst. 22 (2012) 1250017, doi:10.1142/S0129065712500177.
69. I. Baruchi, V. Volman, N. Raichman, M. Shein and Ben-Jacob, E. The emergence and properties of mutual synchronization in vitro coupled cortical networks, Eur. J. Neurosci. 28 (2008) 1825-1835, doi:10.1111/j.1460-9568.2008.06487.x.
70. O. Jaidar et al., Dynamics of the Parkinsonian striatal microcircuit: Entrainment into a dominant network state, J. Neurosci. 30 (2010) 11326-11336, doi:10.1523/JNEUROSCI.1380-10.2010.
71. B. J. Frey and D. Dueck, Clustering by passing messages between data points, Science 315 (2007) 972-976, doi:10.1126/science.1136800.
72. R. Santana, L. M. McGarry, C. Bielza, P. Larranaga and R. Yuste, Classification of neocortical interneurons using affinity propagation, Front Neural Circuits 7 (2013) 185, doi:10.3389/fncir.2013.00185.
73. R. Albert and A. L. Barabasi, Dynamics of complex systems: Scaling laws for the period of boolean networks, Phys. Rev. Lett. 84 (2000) 5660-5663.
74. A. L. Barabasi and R. Albert, Emergence of scaling in random networks, Science 286 (1999) 509-512.
75. N. Spruston and J. Cang, Timing isn't everything, Nat. Neurosci. 13 (2010) 277-279, doi:nn0310-277 [pii] 10.1038/nn0310-277.
76. S. Grillner, Biological pattern generation: The cellular and computational logic of networks in motion, Neuron 52 (2006) 751-766, doi:10.1016/j.neuron. 2006.11.008.
77. I. Baruchi and E. Ben-Jacob, Towards neuromemory-chip: Imprinting multiple memories in cultured neural networks, Phys. Rev. E Stat. Nonlin. Soft Matter. Phys. 75 (2007) 050901.
78. J. E. Miller, I. Ayzenshtat, L. Carrillo-Reid and R. Yuste, Visual stimuli recruit intrinsically generated cortical ensembles, Proc. Nat. Acad. Sci. USA 111 (2014) E4053-4061, doi:10.1073/pnas.1406077111.