A diversity of localized timescales in network activity

eLife

Volumn 2014, Issue 3, 2014, Pages

  Chaudhuri, Rishidev ^a,b   Bernacchia, Alberto ^c   Wang, Xiao Jing ^b,d  

^a YALE UNIVERSITY   (United States)

^b YALE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c JACOBS UNIVERSITY BREMEN   (Germany)

^d NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIODIVERSITY; CELL ACTIVITY; CELL HETEROGENEITY; CELL POPULATION; CONNECTOME; CYTOARCHITECTURE; MATHEMATICAL MODEL; NERVE CELL; NERVE CELL NETWORK; NERVE CONDUCTION; TEMPORAL LOBE; TIME;

EID: 84898725845     PISSN: None     EISSN: 2050084X     Source Type: Journal    
DOI: 10.7554/eLife.01239.001     Document Type: Article

References (49)

1. Abou-Chacra R, Thouless DJ, Anderson PW. 1973. A selfconsistent theory of localization. Journal of Physics C Solid State Physics 6:1734-1752. doi: 10.1088/0022-3719/6/10/009.
2. Ahmed B, Anderson JC, Douglas RJ, Martin KA, Whitteridge D. 1998. Estimates of the net excitatory currents evoked by visual stimulation of identified neurons in cat visual cortex. Cerebral Cortex 8:462-476. doi: 10.1093/cercor/8.5.462.
3. Anderson PW. 1958. Absence of diffusion in certain random lattices. Phys Rev 109:1492-1505. doi: 10.1103/PhysRev.109.1492.
4. Ashcroft NW, Mermin DN. 1976. Solid state physics. New York: Rinehart and Winston.
5. Barbour B, Brunel N, Hakim V, Nadal JP. 2007. What can we learn from synaptic weight distributions? Trends in Neurosciences 30:622-629. doi: 10.1016/j.tins.2007.09.005.
6. Benucci A, Ringach DL, Carandini M. 2009. Coding of stimulus sequences by population responses in visual cortex. Nature Neuroscience 12:1317-1324. doi: 10.1038/nn.2398.
7. Bernacchia A, Seo H, Lee D, Wang X-J. 2011. A reservoir of time constants for memory traces in cortical neurons. Nature Neuroscience 14:366-372. doi: 10.1038/nn.2752.
8. Chance FS, Abbott LF, Reyes AD. 2002. Gain modulation from background synaptic input. Neuron 35:773-782. doi: 10.1016/S0896-6273(02)00820-6.
9. Constantinidis C, Goldman-Rakic PS. 2002. Correlated discharges among putative pyramidal neurons and interneurons in the primate prefrontal cortex. Journal of Neurophysiology 88:3487-3497. doi: 10.1152/jn.00188.2002.
10. Dayan P, Abbott LF. 2001. Theoretical neuroscience. Cambridge: The MIT Press.
11. Elston GN. 2007. Specialization of the neocortical pyramidal cell during primate evolution. In: Kass JH, Preuss TM, editors. Evolution of nervous systems: a comprehensive reference. New York: Elsevier. p. 191-242.
12. Ercsey-Ravasz M, Markov NT, Lamy C, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H. 2013. A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron 80:184-197. doi: 10.1016/j.neuron.2013.07.036.
13. Ermentrout B. 1998. Linearization of F-I curves by adaptation. Neural Computation 10:1721-1729. doi: 10.1162/089976698300017106.
14. Ganguli S, Bisley JW, Roitman JD, Shadlen MN, Goldberg ME, Miller KD. 2008. One-dimensional dynamics of attention and decision making in LIP. Neuron 58:15-25. doi: 10.1016/j.neuron.2008.01.038.
15. Ganguli S, Huh D, Sompolinsky H. 2008. Memory traces in dynamical systems. Proceedings of the National Academy of Sciences of the United States of America 105:18970-18975. doi: 10.1073/pnas.0804451105.
16. Goldman MS. 2009. Memory without feedback in a neural network. Neuron 61:621-634. doi: 10.1016/j.neuron.2008.12.012.
17. Holmgren C, Harkany T, Svennenfors B, Zilberter Y. 2003. Pyramidal cell communication within local networks in layer 2/3 of rat neocortex. Journal of Physiology (London) 551:139-153. doi: 10.1113/jphysiol.2003.044784.
18. Honey CJ, Thivierge JP, Sporns O. 2010. Can structure predict function in the human brain? NeuroImage 52:766-776. doi: 10.1016/j.neuroimage.2010.01.071.
19. Joshua M, Medina JF, Lisberger SG. 2013. Diversity of neural responses in the brainstem during smooth pursuit eye movements constrains the circuit mechanisms of neural integration. Journal of Neuroscience 33:6633-6647. doi: 10.1523/JNEUROSCI.3732-12.2013.
20. Komiyama T, Sato TR, O'Connor DH, Zhang YX, Huber D, Hooks BM, Gabitto M, Svoboda K. 2010. Learning-related fine-scale specificity imaged in motor cortex circuits of behaving mice. Nature 464:1182-1186. doi: 10.1038/nature08897.
21. Lee PA. 1985. Disordered electronic systems. Reviews of Modern Physics 57:287-337. doi: 10.1103/RevModPhys.57.287.
22. Levy RB, Reyes AD. 2012. Spatial profile of excitatory and inhibitory synaptic connectivity in mouse primary auditory cortex. Journal of Neuroscience 32:5609-5619. doi: 10.1523/JNEUROSCI.5158-11.2012.
23. Markov NT, Ercsey-Ravasz MM, Ribeiro Gomes AR, Lamy C, Magrou L, Vezoli J, Misery P, Falchier A, Quilodran R, Gariel MA, Sallet J, Gamanut R, Huissoud C, Clavagnier S, Giroud P, Sappey-Marinier D, Barone P, Dehay C, Toroczkai Z, Knoblauch K, Van Essen DC, Kennedy H. 2014. A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cerebral Cortex 24:17-36. doi: 10.1093/cercor/bhs270.
24. Markov NT, Misery P, Falchier A, Lamy C, Vezoli J, Quilodran R, Gariel MA, Giroud P, Ercsey-Ravasz M, Pilaz LJ, Huissoud C, Barone P, Dehay C, Toroczkai Z, Van Essen DC, Kennedy H, Knoblauch K. 2011. Weight consistency specifies regularities of macaque cortical networks. Cerebral Cortex 21:1254-1272. doi: 10.1093/cercor/bhq201.
25. Miri A, Daie K, Arrenberg AB, Baier H, Aksay E, Tank DW. 2011. Spatial gradients and multidimensional dynamics in a neural integrator circuit. Nature Neuroscience 14:1150-1159. doi: 10.1038/nn.2888.
26. 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.
27. Newman MEJ. 2003. The structure and function of complex networks. SIAM Review 45:167-256. doi: 10.1137/S003614450342480.
28. Newman MEJ. 2010. Networks: an introduction. New York: Oxford University Press.
29. Olver FWJ. 2010. Chapter 9 airy and related functions. In: Olver FWJ, Lozier DW, Boisvert RF, Clark CW, editors. NIST handbook of mathematical functions. New York: Cambridge University Press. p. 193-214.
30. 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.
31. Rajan K, Abbott LF. 2006. Eigenvalue spectra of random matrices for neural networks. Physical Review Letters 97:188104. doi: 10.1103/PhysRevLett.97.188104.
32. Raser JM, O'Shea EK. 2005. Noise in gene expression: origins, consequences, and control. Science 309:2010-2013. doi: 10.1126/science.1105891.
33. Romo R, Brody CD, Hernandez A, Lemus L. 1999. Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399:470-473. doi: 10.1038/20939.
34. Rugh WJ. 1995. Linear system theory. New Jersey: Prentice Hall. 2nd edition.
35. Shriki O, Hansel D, Sompolinsky H. 2003. Rate models for conductance-based cortical neuronal networks. Neural Computation 15:1809-1841. doi: 10.1162/08997660360675053.
36. Smith MA, Kohn A. 2008. Spatial and temporal scales of neuronal correlation in primary visual cortex. Journal of Neuroscience 28:12591-12603. doi: 10.1523/JNEUROSCI.2929-08.2008.
37. Sporns O. 2011. The non-random brain: efficiency, economy, and complex dynamics. Frontiers in Computational Neuroscience 5:5. doi: 10.3389/fncom.2011.00005.
38. Strogatz SH. 1994. Nonlinear dynamics and chaos. New York: Perseus Books Publishing.
39. Strogatz SH. 2001. Exploring complex networks. Nature 410:268-276. doi: 10.1038/35065725.
40. Tononi G, Edelman GM. 1998. Consciousness and complexity. Science 282:1846-1851. doi: 10.1126/science.282.5395.1846.
41. Trefethen LN, Chapman SJ. 2004. Wave packet pseudomodes of twisted Toeplitz matrices. Communications on Pure and Applied Mathematics 57:1233-1264. doi: 10.1002/cpa.20034.
42. Trefethen LN, Embree M. 2005. Spectra and Pseudospectra: the behavior of Nonnormal matrices and Operators. Princeton: Princeton University Press.
43. Vogels TP, Rajan K, Abbott LF. 2005. Neural network dynamics. Annual Review of Neuroscience 28:357-376. doi: 10.1146/annurev.neuro.28.061604.135637.
44. Wang X-J. 1998. Calcium coding and adaptive temporal computation in cortical pyramidal neurons. Journal of Neurophysiology 79:1549-1566.
45. Wang X-J. 2001. Synaptic reverberation underlying mnemonic persistent activity. Trends in Neurosciences 24:455-463. doi: 10.1016/S0166-2236(00)01868-3.
46. Wang X-J. 2008. Decision making in recurrent neuronal circuits. Neuron 60:215-234. doi: 10.1016/j.neuron.2008.09.034.
47. Wang X-J. 2010. Prefrontal cortex. In: Shepherd GM, Grillner S, editors. Handbook of brain microcircuits. New York: Oxford University Press. p. 46-56.
48. Watts DJ, Strogatz SH. 1998. Collective dynamics of 'small-world' networks. Nature 393:440-442. doi: 10.1038/30918.
49. Wolpert L. 2011. Positional information and patterning revisited. Journal of Theoretical Biology 269:359-365. doi: 10.1016/j.jtbi.2010.10.034.