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Linear Stability of Spontaneously Active Local Cortical Circuits: Is There Criticality on Long Time Scales?

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The Functional Role of Critical Dynamics in Neural Systems

Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 11))

Abstract

Self-organizing systems acquire their structures and functions without patterned input from the outside world. In the interconnected architectures of the neocortex, spontaneous activity—that is, activity that arises without external sensory or electrical stimulus—predominates over sensory-evoked activity. Thus, spontaneous neuronal activity provides a means to characterizing the structure, function and dynamics of neocortical networks. We have recorded spontaneous, asynchronous network activity from hundreds of neurons constituting local cortical circuits in mice with high-density microelectrode arrays (MEAs) in vitro. The spontaneous activity in the network displayed features of a system at criticality and scale-free structures, such as fluctuation scaling and multiple frequency bands. To investigate dynamical parameters, we have investigated the linear and nonlinear components of the network dynamics. The former allows us not only to define a linear measure of functional connectivity, but also to determine the linear stability of the system through its eigenvalues. Similarly, the latter allows us to define a measure of nonlinear functional connectivity. An important feature revealed by this approach is the large number of eigenvalues with positive real parts and the high density of eigenvalues near the imaginary axis, which demonstrate respectively that this high-dimensional system is linearly unstable and critical on long time scales (>1s). The function of critical dynamics in these networks is discussed with respect to exploratory behavior in rodents.

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References

  1. Ahmed, B., Anderson, J.C., Douglas, R.J., et al.: Polyneuronal innervation of spiny stellate neurons in cat visual cortex. J. Comput. Neurol. 341, 39–49 (1994). https://doi.org/10.1002/cne.903410105

    Article  CAS  Google Scholar 

  2. Ahrens, K.F., Kleinfeld, D.: Current flow in vibrissa motor cortex can phase-lock with exploratory rhythmic whisking in rat. J. Neurophysiol. 92, 1700–1707 (2004). https://doi.org/10.1152/jn.00404.2004

    Article  PubMed  Google Scholar 

  3. Alstrom, P.: Mean-field exponents for self-organized critical phenomena. Phys. Rev. A 38, 4905–4906 (1988). https://doi.org/10.1103/PhysRevA.38.4905

    Article  CAS  Google Scholar 

  4. Bak, P., Stassinopoulos, D.: Democratic reinforcement 51 (1995)

    Google Scholar 

  5. Bak, P., Tang, C., Wiesenfeld, K.: Self-organized criticality: An explanation of the 1/ f noise. Phys. Rev. Lett. 59, 381–384 (1987). https://doi.org/10.1103/physrevlett.59.381

    Article  CAS  Google Scholar 

  6. Bédard, C., Kröger, H., Destexhe, A.: Does the 1/f frequency scaling of brain signals reflect self-organized critical states? Phys. Rev. Lett. 97, 1–4 (2006). https://doi.org/10.1103/physrevlett.97.118102

  7. Beggs, J.M., Plenz, D.: Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167–11177 (2003). doi:23/35/11167 [pii]

    Google Scholar 

  8. Berg, R.W., Kleinfeld, D.: Rhythmic whisking by rat: retraction as well as protraction of the vibrissae is under active muscular control. J. Neurophysiol. 89, 104–117 (2003). https://doi.org/10.1152/jn.00600.2002

    Article  PubMed  Google Scholar 

  9. Buice, M.A., Cowan, J.D.: Statistical mechanics of the neocortex. Prog. Biophys. Mol. Biol. 99, 53–86 (2009). https://doi.org/10.1016/j.pbiomolbio.2009.07.003

    Article  PubMed  Google Scholar 

  10. Cabral, J., Kringelbach, M., Deco, G.: Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: models and mechanisms. Neuroimage, 0–1 (2017). https://doi.org/10.1016/j.neuroimage.2017.03.045

    Article  Google Scholar 

  11. Chialvo, D., Bak, P.: Commentary: learning from mistakes. Neuroscience 90, 1137–1148 (1999)

    Article  CAS  Google Scholar 

  12. Chiel, H.J., Beer, R.D.: The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment. Trends Neurosci. 20, 553–557 (1997)

    Article  CAS  Google Scholar 

  13. Cowan, J.D., Neuman, J., Kiewiet, B., Van Drongelen, W.: Self-organized criticality in a network of interacting neurons. J. Stat. Mech. Theory Exp. 2013:. https://doi.org/10.1088/1742-5468/2013/04/p04030

    Article  Google Scholar 

  14. Cowan, J.D., Neuman, J., Van Drongelen, W.: Self-organized criticality and near-criticality in neural networks. In: Criticality in Neural Systems, pp 465–484 (2014)

    Chapter  Google Scholar 

  15. De Arcangelis, L., Perrone-Capano, C., Herrmann, H.J.: Self-organized criticality model for brain plasticity. Phys. Rev. Lett. 96, 1–4 (2006). https://doi.org/10.1103/physrevlett.96.028107

  16. Douglas, R.J., Koch, C., Mahowald, M., et al.: Recurrent excitation in neocortical circuits. Science 269, 981–985 (1995)

    Article  CAS  Google Scholar 

  17. Eguíluz, V.M., Chialvo, D.R., Cecchi, G.A., et al.: Scale-free brain functional networks. Phys. Rev. Lett. 94, 1–4 (2005). https://doi.org/10.1103/physrevlett.94.018102

  18. Friedman, N., Ito, S., Brinkman, B.A.W., et al.: Universal critical dynamics in high resolution neuronal avalanche data. Phys. Rev. Lett. 108, 1–5 (2012). https://doi.org/10.1103/physrevlett.108.208102

  19. Galán, R.F.: On how network architecture determines the dominant patterns of spontaneous neural activity. PLoS One 3, (2008). https://doi.org/10.1371/journal.pone.0002148

    Article  Google Scholar 

  20. Gireesh, E.D., Plenz, D.: Neuronal avalanches organize as nested theta- and beta/gamma-oscillations during development of cortical layer 2/3. Proc. Natl. Acad. Sci. 105, 7576–7581 (2008). https://doi.org/10.1073/pnas.0800537105

    Article  Google Scholar 

  21. Gutenberg B, Richter C (1954) Seismicity of the earth: Princeton, NJ

    Google Scholar 

  22. Hájos, N., Ellender, T.J., Zemankovics, R., et al.: Maintaining network activity in submerged hippocampal slices: importance of oxygen supply. Eur. J. Neurosci. 29, 319–327 (2009). https://doi.org/10.1111/j.1460-9568.2008.06577.x

    Article  Google Scholar 

  23. Hájos, N., Mody, I.: Establishing a physiological environment for visualized in vitro brain slice recordings by increasing oxygen supply and modifying aCSF content. J. Neurosci. Methods 183, 107–113 (2009). https://doi.org/10.1016/j.jneumeth.2009.06.005

    Article  Google Scholar 

  24. Haken, H.: Synergetics: An Introduction. Springer, Berlin (1978)

    Chapter  Google Scholar 

  25. Haken, H.: Advanced Synergetics: Instability Hierarchies of Self-organizing Systems. Springer, Berlin (1983)

    Book  Google Scholar 

  26. Herz, A.V.M., Hopfield, J.J.: Earthquake cycles and neural reverberations: collective oscillations in systems with pulse-coupled threshold elements andreas. Phys. Rev. Lett. 75, 4–7 (1995)

    Article  Google Scholar 

  27. Hoffman, K.L., Battaglia, F.P., Harris, K., et al.: The upshot of up states in the neocortex: from slow oscillations to memory formation. J. Neurosci. 27, 11838–11841 (2007). https://doi.org/10.1523/JNEUROSCI.3501-07.2007

    Article  CAS  PubMed  Google Scholar 

  28. Kodama, N.X., Feng, T., Ullett, J.J., et al.: Anti-correlated cortical networks arise from spontaneous neuronal dynamics at slow timescales. Sci. Rep. (2017)

    Google Scholar 

  29. Koyama, S.: On the spike train variability characterized by variance-to-mean power relationship. Neural Comput. 27, 1530–1548 (2015). https://doi.org/10.1162/NECO_a_00748

    Article  PubMed  Google Scholar 

  30. Landau, I.D., Sompolinsky, H.: Coherent chaos in a recurrent neural network with structured connectivity, 1–29 (2018). https://doi.org/10.1101/350801

  31. Langton, C.G.: Computation at the edge of chaos: phase transitions and emergent computation. Phys. D Nonlinear Phenom. 42, 12–37 (1990). https://doi.org/10.1016/0167-2789(90)90064-V

    Article  Google Scholar 

  32. Levina, A., Priesemann, V.: Subsampling scaling. Nat. Commun. 8, 1–9 (2017). https://doi.org/10.1038/ncomms15140

    Article  Google Scholar 

  33. Luczak, A., Maclean, J.N.: Default activity patterns at the neocortical microcircuit level. Front. Integr. Neurosci. 6, 30 (2012). https://doi.org/10.3389/fnint.2012.00030

    Article  PubMed  PubMed Central  Google Scholar 

  34. MacLean, J.N., Watson, B.O., Aaron, G.B., Yuste, R.: Internal dynamics determine the cortical response to thalamic stimulation. Neuron 48, 811–823 (2005). https://doi.org/10.1016/j.neuron.2005.09.035

    Article  CAS  PubMed  Google Scholar 

  35. Malamud, B.D.: Forest fires: an example of self-organized critical behavior. Science 1840, 1998–2001 (2008). https://doi.org/10.1126/science.281.5384.1840

    Article  Google Scholar 

  36. Mehta, M.L.: Random Matrices (2005)

    Google Scholar 

  37. Millman, D., Mihalas, S., Kirkwood, A., Niebur, E.: Self-organized criticality occurs in non-conservative neuronal networks during “up” states. Nat. Phys. 6, 801–805 (2010). https://doi.org/10.1038/nphys1757

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mitra, P., Pesaran, B.: Analysis of dynamic brain imaging data. Biophys. J. 76, 691–708 (1999)

    Article  CAS  Google Scholar 

  39. Nelder, J., Wedderburn, R. Generalized linear models. J. R. Stat. Soc. (1972). https://doi.org/10.2307/2344614

    Article  Google Scholar 

  40. Neske, G.T., Patrick, S.L., Connors, B.W.: Contributions of diverse excitatory and inhibitory neurons to recurrent network activity in cerebral cortex. J. Neurosci. 35, 1089–1105 (2015). https://doi.org/10.1523/JNEUROSCI.2279-14.2015

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nicolelis, M.A.L., Baccala, L.A., Lin, R.C.S., Chapin, J.K.: Sensorimotor encoding by synchronous neural ensemble activity at multiple levels of the somatosensory system. Science 268, 1353–1358 (1995)

    Article  CAS  Google Scholar 

  42. Nicolelis, M.A.L., Fanselow, E.E.: Thalamocortical optimization of tactile processing according to behavioral state. Nat. Neurosci. 5, 517–523 (2002). https://doi.org/10.1038/nn0602-517

    Article  CAS  Google Scholar 

  43. Pais-Vieira, M., Kunicki, C., Tseng, P.-H., et al.: Cortical and thalamic contributions to response dynamics across layers of the primary somatosensory cortex during tactile discrimination. J. Neurophysiol. 114, 1652–1676 (2015). https://doi.org/10.1152/jn.00108.2015

    Article  PubMed  PubMed Central  Google Scholar 

  44. Petermann, T., Thiagarajan, T.C., Lebedev, M.A., et al.: Spontaneous cortical activity in awake monkeys composed of neuronal avalanches. Proc. Natl. Acad. Sci. 106, 15921–15926 (2009). https://doi.org/10.1073/pnas.0904089106

    Article  PubMed  Google Scholar 

  45. Pikovsky, A.: Reconstruction of a neural network from a time series of firing rates. Phys. Rev. E 93, 062313 (2016)

    Google Scholar 

  46. Priesemann, V.: Spike avalanches in vivo suggest a driven, slightly subcritical brain state 8, 1–17 (2014). https://doi.org/10.3389/fnsys.2014.00108

  47. Prigogine, I., Lefever, R.: Symmetry breaking instabilities in dissipative systems. II. J Chem Phys 48, 1695–1700 (1968). https://doi.org/10.1063/1.1668896

    Article  Google Scholar 

  48. Prigogine, I., Nicolis, G.: On symmetry-breaking instabilities in dissipative systems. J. Chem. Phys. 46, 3542–3550 (1967). https://doi.org/10.1063/1.1841255

    Article  CAS  Google Scholar 

  49. Puzerey, P.A., Kodama, N.X., Galán, R.F.: Abnormal cell-intrinsic and network excitability in the neocortex of serotonin-deficient Pet-1 knockout mice. J. Neurophysiol. (2016). https://doi.org/10.1152/jn.00996.2014

    Article  CAS  Google Scholar 

  50. Quiroga, R.Q., Nadasdy, Z., Ben-Shaul, Y.: Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering. Neural Comput. 16, 1661–1687 (2004). https://doi.org/10.1162/089976604774201631

    Article  PubMed  Google Scholar 

  51. Rajan, K., Abbott, L.F.: Eigenvalue spectra of random matrices for neural networks. Phys. Rev. Lett. 97, 2–5 (2006). https://doi.org/10.1103/physrevlett.97.188104

  52. Renart, A., de la Rocha, J., Bartho, P., et al.: The asynchronous state in cortical circuits. Science 327, 587–590 (2010). https://doi.org/10.1126/science.1179850

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Sadovsky, A.J., MacLean, J.N.: Scaling of topologically similar functional modules defines mouse primary auditory and somatosensory microcircuitry. J. Neurosci. 33, 14048–14060 (2013). https://doi.org/10.1523/JNEUROSCI.1977-13.2013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Sederberg, A.J., Palmer, S.E., MacLean, J.N.: Decoding thalamic afferent input using microcircuit spiking activity. J. Neurophysiol. 113, 2921–2933 (2015). https://doi.org/10.1152/jn.00885.2014

    Article  PubMed  PubMed Central  Google Scholar 

  55. Sporns, O., Zwi, J.D.: The small world of the cerebral cortex. Neuroinformatics 2, 145–162 (2004). https://doi.org/10.1385/NI:2:2:145

    Article  PubMed  Google Scholar 

  56. Steinke, G.K., Galán, R.F.: Brain rhythms reveal a hierarchical network organization. PLoS Comput. Biol. 7, e1002207 (2011). https://doi.org/10.1371/journal.pcbi.1002207

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Tomen, N., Rotermund, D., Ernst, U.: Marginally subcritical dynamics explain enhanced stimulus discriminability under attention. Front. Syst. Neurosci. 8, 1–15 (2014). https://doi.org/10.3389/fnsys.2014.00151

    Article  Google Scholar 

  58. Touboul, J., Destexhe, A.: Power-law statistics and universal scaling in the absence of criticality. Phys. Rev. E 012413, 1–15 (2017). https://doi.org/10.1103/PhysRevE.95.012413

    Article  Google Scholar 

  59. Watson, B.O., MacLean, J.N., Yuste, R.: UP States protect ongoing cortical activity from thalamic inputs. PLoS One 3, (2008). https://doi.org/10.1371/journal.pone.0003971

    Article  Google Scholar 

  60. Wilting, J., Priesemann, V.: Inferring collective dynamical states from widely unobserved systems (2018)

    Google Scholar 

  61. Zapperi, S., Lauritsen, K.B., Stanley, H.E.: Self-organized branching processes: Mean-field theory for avalanches. Phys. Rev. Lett. 75, 4071–4074 (1995). https://doi.org/10.1103/physrevlett.75.4071

    Article  CAS  Google Scholar 

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Correspondence to Roberto F. Galán .

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Kodama, N.X., Galán, R.F. (2019). Linear Stability of Spontaneously Active Local Cortical Circuits: Is There Criticality on Long Time Scales?. In: Tomen, N., Herrmann, J., Ernst, U. (eds) The Functional Role of Critical Dynamics in Neural Systems . Springer Series on Bio- and Neurosystems, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-20965-0_8

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