- Letter
- Open Access
Excitatory-inhibitory branching process: A parsimonious view of cortical asynchronous states, excitability, and criticality
Phys. Rev. Research 4, L042027 – Published 14 November, 2022
DOI: https://doi.org/10.1103/PhysRevResearch.4.L042027
Abstract
The branching process is the minimal model for propagation dynamics, avalanches, and criticality, broadly used in neuroscience. A simple extension of it, adding inhibitory nodes, induces a much-richer phenomenology, including an intermediate phase, between quiescence and saturation, that exhibits the key features of “asynchronous states” in cortical networks. Remarkably, in the inhibition-dominated case, it exhibits an extremely rich phase diagram that captures a wealth of nontrivial features of spontaneous brain activity, such as collective excitability, hysteresis, tilted avalanche shapes, and partial synchronization, allowing us to rationalize striking empirical findings within a common and parsimonious framework.
Physics Subject Headings (PhySH)
Article Text
Supplemental Material
References (77)
- T. Mora and W. Bialek, Are biological systems poised at criticality?, J. Stat. Phys. 144, 268 (2011).
- M. A. Muñoz, Colloquium: Criticality and dynamical scaling in living systems, Rev. Mod. Phys. 90, 031001 (2018).
- J. M. Beggs and D. Plenz, Neuronal avalanches in neocortical circuits, J. Neurosci. 23, 11167 (2003).
- T. Petermann, T. C. Thiagarajan, M. A. Lebedev, M. A. Nicolelis, D. R. Chialvo, and D. Plenz, Spontaneous cortical activity in awake monkeys composed of neuronal avalanches, Proc. Natl. Acad. Sci. USA 106, 15921 (2009).
- A. Haimovici, E. Tagliazucchi, P. Balenzuela, and D. R. Chialvo, Brain Organization into Resting State Networks Emerges at Criticality on a Model of the Human Connectome, Phys. Rev. Lett. 110, 178101 (2013).
- E. Tagliazucchi, P. Balenzuela, D. Fraiman, and D. R. Chialvo, Criticality in large-scale brain fmri dynamics unveiled by a novel point process analysis, Front. Physiol. 3, 15 (2012).
- O. Shriki, J. Alstott, F. Carver, T. Holroyd, R. N. Henson, M. L. Smith, R. Coppola, E. Bullmore, and D. Plenz, Neuronal avalanches in the resting MEG of the human brain, J. Neurosci. 33, 7079 (2013).
- T. Bellay, A. Klaus, S. Seshadri, and D. Plenz, Irregular spiking of pyramidal neurons organizes as scale-invariant neuronal avalanches in the awake state, Elife 4, e07224 (2015).
- S. Yu, T. L. Ribeiro, C. Meisel, S. Chou, A. Mitz, R. Saunders, and D. Plenz, Maintained avalanche dynamics during task-induced changes of neuronal activity in nonhuman primates, Elife 6, e27119 (2017).
- T. Liggett, Interacting Particle Systems, Classics in Mathematics (Springer, Berlin, 2004).
- T. E. Harris, The Theory of Branching Processes, Grundlehren der mathematischen Wissenschaften (Springer Berlin, Heidelberg, 1963).
- J. P. Sethna, K. A. Dahmen, and C. R. Myers, Crackling noise, Nature (London) 410, 242 (2001).
- N. Friedman, S. Ito, B. A. W. Brinkman, M. Shimono, R. E. L. DeVille, K. A. Dahmen, J. M. Beggs, and T. C. Butler, Universal Critical Dynamics in High Resolution Neuronal Avalanche Data, Phys. Rev. Lett. 108, 208102 (2012).
- S. R. Miller, S. Yu, S. Pajevic, and D. Plenz, Long-term stability of avalanche scaling and integrative network organization in prefrontal and premotor cortex, Network Neurosci. 5, 505 (2021).
- S. di Santo, P. Villegas, R. Burioni, and M. A. Muñoz, Simple unified view of branching process statistics: Random walks in balanced logarithmic potentials, Phys. Rev. E 95, 032115 (2017).
- V. Priesemann, M. H. Munk, and M. Wibral, Subsampling effects in neuronal avalanche distributions recorded in vivo, BMC Neurosci. 10, 40 (2009).
- M. Martinello, J. Hidalgo, A. Maritan, S. di Santo, D. Plenz, and M. A. Muñoz, Neutral Theory and Scale-Free Neural Dynamics, Phys. Rev. X 7, 041071 (2017).
- V. Priesemann, M. Valderrama, M. Wibral, and M. Le Van Quyen, Neuronal avalanches differ from wakefulness to deep sleep–evidence from intracranial depth recordings in humans, PLoS Comput. Biol. 9, e1002985 (2013).
- A. J. Fontenele, N. A. P. de Vasconcelos, T. Feliciano, L. A. A. Aguiar, C. Soares-Cunha, B. Coimbra, L. Dalla Porta, S. Ribeiro, A. J. Rodrigues, N. Sousa, P. V. Carelli, and M. Copelli, Criticality between Cortical States, Phys. Rev. Lett. 122, 208101 (2019).
- A. Ponce-Alvarez, A. Jouary, M. Privat, G. Deco, and G. Sumbre, Whole-brain neuronal activity displays crackling noise dynamics, Neuron 100, 1446 (2018).
- S. di Santo, P. Villegas, R. Burioni, and M. A. Muñoz, Landau–ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization, Proc. Natl. Acad. Sci. USA 115, E1356 (2018).
- V. Buendía, P. Villegas, R. Burioni, and M. A. Muñoz, Hybrid-type synchronization transitions: Where incipient oscillations, scale-free avalanches, and bistability live together, Phys. Rev. Res. 3, 023224 (2021).
- B. Mariani, G. Nicoletti, M. Bisio, M. Maschietto, S. Vassanelli, and S. Suweis, Disentangling the critical signatures of neural activity, Sci. Rep. 12, 10770 (2022).
- D. Plenz and E. Niebur, Criticality in Neural Systems (John Wiley & Sons, New York, 2014).
- D. R. Chialvo, Emergent complex neural dynamics, Nat. Phys. 6, 744 (2010).
- A. Levina, J. M. Herrmann, and T. Geisel, Dynamical synapses causing self-organized criticality in neural networks, Nat. Phys. 3, 857 (2007).
- L. J. Fosque, R. V. Williams-García, J. M. Beggs, and G. Ortiz, Evidence for Quasicritical Brain Dynamics, Phys. Rev. Lett. 126, 098101 (2021).
- O. Kinouchi and M. Copelli, Optimal dynamical range of excitable networks at criticality, Nat. Phys. 2, 348 (2006).
- W. L. Shew, H. Yang, T. Petermann, R. Roy, and D. Plenz, Neuronal avalanches imply maximum dynamic range in cortical networks at criticality, J. Neurosci. 29, 15595 (2009).
- W. L. Shew and D. Plenz, The functional benefits of criticality in the cortex, Neuroscientist 19, 88 (2013).
- Z. Ma, G. G. Turrigiano, R. Wessel, and K. B. Hengen, Cortical circuit dynamics are homeostatically tuned to criticality in vivo, Neuron 104, 655 (2019).
- D. Plenz, T. L. Ribeiro, S. R. Miller, P. A. Kells, A. Vakili, and E. L. Capek, Self-organized criticality in the brain, Front. Phys. 9, 639389 (2021).
- L. Cocchi, L. L. Gollo, A. Zalesky, and M. Breakspear, Criticality in the brain: A synthesis of neurobiology, models and cognition, Prog. Neurobiol. 158, 132 (2017).
- J. Wilting and V. Priesemann, 25 years of criticality in neuroscience–established results, open controversies, novel concepts, Curr. Opin. Neurobiol. 58, 105 (2019).
- P. Dayan and L. F. Abbott, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (MIT Press, Cambridge, MA, 2006).
- J. S. Isaacson and M. Scanziani, How inhibition shapes cortical activity, Neuron 72, 231 (2011).
- B. Doiron, A. Litwin-Kumar, R. Rosenbaum, G. K. Ocker, and K. Josić, The mechanics of state-dependent neural correlations, Nat. Neurosci. 19, 383 (2016).
- S. Denéve and C. K. Machens, Efficient codes and balanced networks, Nat. Neurosci. 19, 375 (2016).
- C. van Vreeswijk and H. Sompolinsky, Chaos in neuronal networks with balanced excitatory and inhibitory activity, Science 274, 1724 (1996).
- N. Brunel, Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons, J. Comput. Neurosci. 8, 183 (2000).
- N. Brunel and X.-J. Wang, What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance, J. Neurophysiol. 90, 415 (2003).
- I. Ginzburg and H. Sompolinsky, Theory of correlations in stochastic neural networks, Phys. Rev. E 50, 3171 (1994).
- A. Renart, J. De La Rocha, P. Bartho, L. Hollender, N. Parga, A. Reyes, and K. D. Harris, The asynchronous state in cortical circuits, Science 327, 587 (2010).
- T. Sippy and R. Yuste, Decorrelating action of inhibition in neocortical networks, J. Neurosci. 33, 9813 (2013).
- D. Dahmen, S. Grün, M. Diesmann, and M. Helias, Second type of criticality in the brain uncovers rich multiple-neuron dynamics, Proc. Natl. Acad. Sci. USA 116, 13051 (2019).
- M. Benayoun, J. D. Cowan, W. van Drongelen, and E. Wallace, Avalanches in a stochastic model of spiking neurons, PLoS Comput. Biol. 6, e1000846 (2010).
- J. Wilting, J. Dehning, J. Pinheiro Neto, L. Rudelt, M. Wibral, J. Zierenberg, and V. Priesemann, Operating in a reverberating regime enables rapid tuning of network states to task requirements, Front. Syst. Neurosci. 12, 55 (2018).
- J. Liang, T. Zhou, and C. Zhou, Hopf bifurcation in mean field explains critical avalanches in excitation-inhibition balanced neuronal networks: a mechanism for multiscale variability, Front. Syst. Neurosci. 14, 580011 (2020).
- V. Buendía, P. Villegas, S. di Santo, A. Vezzani, R. Burioni, and M. A. Muñoz, Jensen's force and the statistical mechanics of cortical asynchronous states, Sci. Rep. 9, 15183 (2019).
- M. Girardi-Schappo, E. F. Galera, T. T. Carvalho, L. Brochini, N. L. Kamiji, A. C. Roque, and O. Kinouchi, A unified theory of e/i synaptic balance, quasicritical neuronal avalanches and asynchronous irregular spiking, J. Phys. Complex. 2, 045001 (2021).
- J. Li and W. L. Shew, Tuning network dynamics from criticality to an asynchronous state, PLoS Comput. Biol. 16, e1008268 (2020).
- J. Marro and R. Dickman, Nonequilibrium Phase Transition in Lattice Models (Cambridge University Press, Cambridge, UK, 1999).
- M. Henkel, H. Hinrichsen, and S. Lübeck, Non-equilibrium Phase Transitions: Absorbing Phase Transitions, Theoretical and Mathematical Physics (Springer, Berlin, 2008).
- G. Ódor, Universality in Nonequilibrium Lattice Systems: Theoretical Foundations (World Scientific, Singapore, 2008).
- See Supplemental Material at http://link.aps.org/supplemental/10.1103/PhysRevResearch.4.L042027 for a detailed derivation of the model, mean-field results, sparse networks analysis, and methods.
- D. T. Gillespie, Stochastic simulation of chemical kinetics, Annu. Rev. Phys. Chem. 58, 35 (2007).
- R. Pastor-Satorras, C. Castellano, P. Van Mieghem, and A. Vespignani, Epidemic processes in complex networks, Rev. Mod. Phys. 87, 925 (2015).
- J. D. Cowan, J. Neuman, and W. van Drongelen, Wilson–cowan equations for neocortical dynamics, J. Math. Neurosci. 6, 1 (2016).
- E. Negahbani, D. A. Steyn-Ross, M. L. Steyn-Ross, M. T. Wilson, and J. W. Sleigh, Noise-induced precursors of state transitions in the stochastic wilson–cowan model, J. Math. Neurosci. 5, 9 (2015).
- S. di Santo, P. Villegas, R. Burioni, and M. A. Muñoz, Non-normality, reactivity, and intrinsic stochasticity in neural dynamics: A non-equilibrium potential approach, J. Stat. Mech.: Theory Exp. (2018) 073402.
- A. de Candia, A. Sarracino, I. Apicella, and L. de Arcangelis, Critical behaviour of the stochastic wilson-cowan model, PLoS Comput. Biol. 17,e1008884 (2021).
- A. Sarracino, O. Arviv, O. Shriki, and L. de Arcangelis, Predicting brain evoked response to external stimuli from temporal correlations of spontaneous activity, Phys. Rev. Res. 2, 033355 (2020).
- M. Kunze, Non-smooth Dynamical Systems (Springer Science & Business Media, New York, 2000), Vol. 1744.
- E. M. Izhikevich, Dynamical Systems in Neuroscience (MIT Press, Cambridge, MA, 2007).
- B. K. Murphy and K. D. Miller, Balanced amplification: A new mechanism of selective amplification of neural activity patterns, Neuron 61, 635 (2009).
- G. Hennequin, T. P. Vogels, and W. Gerstner, Non-normal amplification in random balanced neuronal networks, Phys. Rev. E 86, 011909 (2012).
- M. Fruchart, R. Hanai, P. B. Littlewood, and V. Vitelli, Non-reciprocal phase transitions, Nature (London) 592, 363 (2021).
- H. Piuvezam et al. (unpublished).
- D. Hansel and G. Mato, Existence and Stability of Persistent States in Large Neuronal Networks, Phys. Rev. Lett. 86, 4175 (2001).
- B. Kriener, H. Enger, T. Tetzlaff, H. E. Plesser, M.-O. Gewaltig, and G. T. Einevoll, Dynamics of self-sustained asynchronous-irregular activity in random networks of spiking neurons with strong synapses, Front. Comput. Neurosci. 8, 136 (2014).
- M. Asllani, R. Lambiotte, and T. Carletti, Structure and dynamical behavior of non-normal networks, Sci. Adv. 4, eaau9403 (2018).
- C.-y. T. Li, M.-m. Poo, and Y. Dan, Burst spiking of a single cortical neuron modifies global brain state, Science 324, 643 (2009).
- D. Durstewitz, J. K. Seamans, and T. J. Sejnowski, Neurocomputational models of working memory, Nat. Neurosci. 3, 1184 (2000).
- E. Wallace, M. Benayoun, W. van Drongelen, and J. Cowan, Emergent oscillations in networks of stochastic spiking neurons, PLoS One 6, e14804 (2011).
- J. Hidalgo, L. Seoane, J. Cortés, and M. Muñoz, Stochastic amplification of fluctuations in cortical upstates, PLoS One 7, e40710 (2012).
- R. Kim and T. J. Sejnowski, Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks, Nat. Neurosci. 24, 129 (2021).
- J. P. Gleeson and R. Durrett, Temporal profiles of avalanches on networks, Nat. Commun. 8, 1227 (2017).