American Institute of Mathematical Sciences

Advanced
  • Previous Article
    Computer-assisted estimates for Birkhoff normal forms
  • JCD Home
  • This Issue
  • Next Article
    A tale of two vortices: How numerical ergodic theory and transfer operators reveal fundamental changes to coherent structures in non-autonomous dynamical systems
December  2020, 7(2): 401-423. doi: 10.3934/jcd.2020016

Degree assortativity in networks of spiking neurons

Christian Bläsche , Shawn Means and  Carlo R. Laing ,

School of Natural and Computational Sciences, Massey University, Private Bag 102-904 NSMC, Auckland, New Zealand

* Corresponding author: c.r.laing@massey.ac.nz

Received  October 2019 Published  July 2020

Degree assortativity refers to the increased or decreased probability of connecting two neurons based on their in- or out-degrees, relative to what would be expected by chance. We investigate the effects of such assortativity in a network of theta neurons. The Ott/Antonsen ansatz is used to derive equations for the expected state of each neuron, and these equations are then coarse-grained in degree space. We generate families of effective connectivity matrices parametrised by assortativity coefficient and use SVD decompositions of these to efficiently perform numerical bifurcation analysis of the coarse-grained equations. We find that of the four possible types of degree assortativity, two have no effect on the networks' dynamics, while the other two can have a significant effect.

Keywords: Neuronal networks, degree assortativity, theta neurons, Ott/Antonsen, bifurcation.
Mathematics Subject Classification: Primary: 92C20; Secondary: 92B25, 34B45, 34C15.
Citation: Christian Bläsche, Shawn Means, Carlo R. Laing. Degree assortativity in networks of spiking neurons. Journal of Computational Dynamics, 2020, 7 (2) : 401-423. doi: 10.3934/jcd.2020016
References:
[1]

V. Avalos-Gaytán, J. A. Almendral, D. Papo, S. E. Schaeffer and S. Boccaletti, Assortative and modular networks are shaped by adaptive synchronization processes, Phys. Rev. E, 86 (2012), 015101(R).  doi: 10.1103/PhysRevE.86.015101.  Google Scholar

[2]

G. Barlev, T. M. Antonsen and E. Ott, The dynamics of network coupled phase oscillators: An ensemble approach, Chaos, 21 (2011), 025103.  doi: 10.1063/1.3596711.  Google Scholar

[3]

S. Chandra, D. Hathcock, K. Crain, T. M. Antonsen, M. Girvan and E. Ott, Modeling the network dynamics of pulse-coupled neurons, Chaos, 27 (2017), 033102.  doi: 10.1063/1.4977514.  Google Scholar

[4]

S.-N. Chow and X.-B. Lin, Bifurcation of a homoclinic orbit with a saddle-node equilibrium, Differential Integral Equations, 3 (1990), 435-466.   Google Scholar

[5]

F. Chung and L. Lu, Connected components in random graphs with given expected degree sequences, Ann. Comb., 6 (2002), 125-145.  doi: 10.1007/PL00012580.  Google Scholar

[6]

B. C. Coutinho, A. V. Goltsev, S. N. Dorogovtsev and J. F. F. Mendes, Kuramoto model with frequency-degree correlations on complex networks, Phys. Rev. E, 87 (2013), 032106.  doi: 10.1103/PhysRevE.87.032106.  Google Scholar

[7]

S. De Franciscis, S. Johnson and J. J. Torres, Enhancing neural-network performance via assortativity, Phys. Rev. E, 83 (2011), 036114.  doi: 10.1103/PhysRevE.83.036114.  Google Scholar

[8]

D. de Santos-Sierra, I. Sendiña-Nadal, I. Leyva, J. A. Almendral and S. Anava, et al., Emergence of small-world anatomical networks in self-organizing clustered neuronal cultures, PLoS one, 9 (2014), e85828. doi: 10.1371/journal.pone.0085828.  Google Scholar

[9]

V. M. Eguíluz, D. R. Chialvo, G. A. Cecchi, M. Baliki and A. V. Apkarian, Scale-free brain functional networks, Phys. Rev. Lett., 94 (2005), 018102.  doi: 10.1103/PhysRevLett.94.018102.  Google Scholar

[10]

B. Ermentrout, Type I membranes, phase resetting curves, and synchrony, Neural Comput., 8 (1996), 979-1001.  doi: 10.1162/neco.1996.8.5.979.  Google Scholar

[11]

G. B. Ermentrout and N. Kopell, Parabolic bursting in an excitable system coupled with a slow oscillation, SIAM J. Appl. Math., 46 (1986), 233-253.  doi: 10.1137/0146017.  Google Scholar

[12]

J. G. Foster, D. V. Foster, P. Grassberger and M. Paczuski, Edge direction and the structure of networks, PNAS, 107 (2010), 10815-10820.  doi: 10.1073/pnas.0912671107.  Google Scholar

[13]

W. J. F. Govaerts, Numerical Methods for Bifurcations of Dynamical Equilibria, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2000.  Google Scholar

[14]

P. Hagmann, L. Cammoun, X. Gigandet, R. Meuli and C. J. Honey, et al., Mapping the structural core of human cerebral cortex, PLoS Biology, 6 (2008), 1479-1493. doi: 10.1371/journal.pbio.0060159.  Google Scholar

[15]

T. Ichinomiya, Frequency synchronization in a random oscillator network, Phys. Rev. E, 70 (2004), 026116.  doi: 10.1103/PhysRevE.70.026116.  Google Scholar

[16]

M. Kähne, I. M. Sokolov and S. Rüdiger, Population equations for degree-heterogenous neural networks, Phys. Rev. E, 96 (2017), 052306.  doi: 10.1103/PhysRevE.96.052306.  Google Scholar

[17]

C. R. Laing, Derivation of a neural field model from a network of theta neurons, Phys. Rev. E, 90 (2014), 010901(R).  doi: 10.1103/PhysRevE.90.010901.  Google Scholar

[18]

C. R. Laing, Numerical bifurcation theory for high-dimensional neural models, J. Math. Neurosci., 4 (2014), 13.  doi: 10.1186/2190-8567-4-13.  Google Scholar

[19]

C. R. Laing, Exact neural fields incorporating gap junctions, SIAM J. Appl. Dyn. Syst., 14 (2015), 1899-1929.  doi: 10.1137/15M1011287.  Google Scholar

[20]

M. D. LaMar and G. D. Smith, Effect of node-degree correlation on synchronization of identical pulse-coupled oscillators, Phys. Rev. E, 81 (2010), 046206.  doi: 10.1103/PhysRevE.81.046206.  Google Scholar

[21]

P. E. Latham, B. J. Richmond, P. G. Nelson and S. Nirenberg, Intrinsic dynamics in neuronal networks. I. theory, J. Neurophysiology, 83 (2000), 808-827.  doi: 10.1152/jn.2000.83.2.808.  Google Scholar

[22]

T. B. Luke, E. Barreto and P. So, Complete classification of the macroscopic behavior of a heterogeneous network of theta neurons, Neural Comput., 25 (2013), 3207-3234.  doi: 10.1162/NECO_a_00525.  Google Scholar

[23]

M. B. Martens, A. R. Houweling and P. H. E. Tiesinga, Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks, J. Comput. Neuroscience, 42 (2017), 87-106.  doi: 10.1007/s10827-016-0629-1.  Google Scholar

[24]

W. S. McCulloch and W. Pitts, The statistical organization of nervous activity, Biometrics, 4 (1948), 91-99.  doi: 10.2307/3001453.  Google Scholar

[25]

E. Montbrió, D. Pazó and A. Roxin, Macroscopic description for networks of spiking neurons, Phys. Rev. X, 5 (2015), 021028.  doi: 10.1103/PhysRevX.5.021028.  Google Scholar

[26]

M. E. J. Newman, Assortative mixing in networks, Phys. Rev. Lett., 89 (2002), 208701.  doi: 10.1103/PhysRevLett.89.208701.  Google Scholar

[27]

M. E. J. Newman, The structure and function of complex networks, SIAM Rev., 45 (2003), 167-256.  doi: 10.1137/S003614450342480.  Google Scholar

[28]

D. Q. Nykamp, D. Friedman, S. Shaker, M. Shinn and M. Vella, et al., Mean-field equations for neuronal networks with arbitrary degree distributions, Phys. Rev. E, 95 (2017), 042323. doi: 10.1103/PhysRevE.95.042323.  Google Scholar

[29]

E. Ott and T. M. Antonsen, Low dimensional behavior of large systems of globally coupled oscillators, Chaos, 18 (2008), 037113.  doi: 10.1063/1.2930766.  Google Scholar

[30]

J. G. Restrepo and E. Ott, Mean-field theory of assortative networks of phase oscillators, Europhys. Lett., 107 (2014), 60006.  doi: 10.1209/0295-5075/107/60006.  Google Scholar

[31]

C. Schmeltzer, A. H. Kihara, I. M. Sokolov and S. Rüdiger, Degree correlations optimize neuronal network sensitivity to sub-threshold stimuli, PLoS One, 10 (2015), e0121794.  doi: 10.1371/journal.pone.0121794.  Google Scholar

[32]

P. S. Skardal, J. G. Restrepo and E. Ott, Frequency assortativity can induce chaos in oscillator networks, Phys. Rev. E, 91 (2015), 060902(R).  doi: 10.1103/PhysRevE.91.060902.  Google Scholar

[33]

P. S. Skardal, J. Sun, D. Taylor and J. G. Restrepo, Effects of degree-frequency correlations on network synchronization: Universality and full phase-locking, Europhys. Lett., 101 (2013), 20001.  doi: 10.1209/0295-5075/101/20001.  Google Scholar

[34]

B. Sonnenschein, F. Sagués and L. Schimansky-Geier, Networks of noisy oscillators with correlated degree and frequency dispersion, Eur. Phys. J. B, 86 (2013), 12.  doi: 10.1140/epjb/e2012-31026-x.  Google Scholar

[35]

S. Teller, C. Granell, M. De Domenico, J. Soriano, S. Gómez and A. Arenas, Emergence of assortative mixing between clusters of cultured neurons, PLoS Comput. Biology, 10 (2014), e1003796.  doi: 10.1371/journal.pcbi.1003796.  Google Scholar

[36]

J. C. Vasquez, A. R. Houweling and P. Tiesinga, Simultaneous stability and sensitivity in model cortical networks is achieved through anti-correlations between the in- and out-degree of connectivity, Frontiers Comput. Neuroscience, 7 (2013), 156.  doi: 10.3389/fncom.2013.00156.  Google Scholar

[37]

M. Vegué and A. Roxin, Firing rate distributions in spiking networks with heterogeneous connectivity, Phys. Rev. E, 100 (2019), 022208.  doi: 10.1103/PhysRevE.100.022208.  Google Scholar

show all references

References:
[1]

V. Avalos-Gaytán, J. A. Almendral, D. Papo, S. E. Schaeffer and S. Boccaletti, Assortative and modular networks are shaped by adaptive synchronization processes, Phys. Rev. E, 86 (2012), 015101(R).  doi: 10.1103/PhysRevE.86.015101.  Google Scholar

[2]

G. Barlev, T. M. Antonsen and E. Ott, The dynamics of network coupled phase oscillators: An ensemble approach, Chaos, 21 (2011), 025103.  doi: 10.1063/1.3596711.  Google Scholar

[3]

S. Chandra, D. Hathcock, K. Crain, T. M. Antonsen, M. Girvan and E. Ott, Modeling the network dynamics of pulse-coupled neurons, Chaos, 27 (2017), 033102.  doi: 10.1063/1.4977514.  Google Scholar

[4]

S.-N. Chow and X.-B. Lin, Bifurcation of a homoclinic orbit with a saddle-node equilibrium, Differential Integral Equations, 3 (1990), 435-466.   Google Scholar

[5]

F. Chung and L. Lu, Connected components in random graphs with given expected degree sequences, Ann. Comb., 6 (2002), 125-145.  doi: 10.1007/PL00012580.  Google Scholar

[6]

B. C. Coutinho, A. V. Goltsev, S. N. Dorogovtsev and J. F. F. Mendes, Kuramoto model with frequency-degree correlations on complex networks, Phys. Rev. E, 87 (2013), 032106.  doi: 10.1103/PhysRevE.87.032106.  Google Scholar

[7]

S. De Franciscis, S. Johnson and J. J. Torres, Enhancing neural-network performance via assortativity, Phys. Rev. E, 83 (2011), 036114.  doi: 10.1103/PhysRevE.83.036114.  Google Scholar

[8]

D. de Santos-Sierra, I. Sendiña-Nadal, I. Leyva, J. A. Almendral and S. Anava, et al., Emergence of small-world anatomical networks in self-organizing clustered neuronal cultures, PLoS one, 9 (2014), e85828. doi: 10.1371/journal.pone.0085828.  Google Scholar

[9]

V. M. Eguíluz, D. R. Chialvo, G. A. Cecchi, M. Baliki and A. V. Apkarian, Scale-free brain functional networks, Phys. Rev. Lett., 94 (2005), 018102.  doi: 10.1103/PhysRevLett.94.018102.  Google Scholar

[10]

B. Ermentrout, Type I membranes, phase resetting curves, and synchrony, Neural Comput., 8 (1996), 979-1001.  doi: 10.1162/neco.1996.8.5.979.  Google Scholar

[11]

G. B. Ermentrout and N. Kopell, Parabolic bursting in an excitable system coupled with a slow oscillation, SIAM J. Appl. Math., 46 (1986), 233-253.  doi: 10.1137/0146017.  Google Scholar

[12]

J. G. Foster, D. V. Foster, P. Grassberger and M. Paczuski, Edge direction and the structure of networks, PNAS, 107 (2010), 10815-10820.  doi: 10.1073/pnas.0912671107.  Google Scholar

[13]

W. J. F. Govaerts, Numerical Methods for Bifurcations of Dynamical Equilibria, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 2000.  Google Scholar

[14]

P. Hagmann, L. Cammoun, X. Gigandet, R. Meuli and C. J. Honey, et al., Mapping the structural core of human cerebral cortex, PLoS Biology, 6 (2008), 1479-1493. doi: 10.1371/journal.pbio.0060159.  Google Scholar

[15]

T. Ichinomiya, Frequency synchronization in a random oscillator network, Phys. Rev. E, 70 (2004), 026116.  doi: 10.1103/PhysRevE.70.026116.  Google Scholar

[16]

M. Kähne, I. M. Sokolov and S. Rüdiger, Population equations for degree-heterogenous neural networks, Phys. Rev. E, 96 (2017), 052306.  doi: 10.1103/PhysRevE.96.052306.  Google Scholar

[17]

C. R. Laing, Derivation of a neural field model from a network of theta neurons, Phys. Rev. E, 90 (2014), 010901(R).  doi: 10.1103/PhysRevE.90.010901.  Google Scholar

[18]

C. R. Laing, Numerical bifurcation theory for high-dimensional neural models, J. Math. Neurosci., 4 (2014), 13.  doi: 10.1186/2190-8567-4-13.  Google Scholar

[19]

C. R. Laing, Exact neural fields incorporating gap junctions, SIAM J. Appl. Dyn. Syst., 14 (2015), 1899-1929.  doi: 10.1137/15M1011287.  Google Scholar

[20]

M. D. LaMar and G. D. Smith, Effect of node-degree correlation on synchronization of identical pulse-coupled oscillators, Phys. Rev. E, 81 (2010), 046206.  doi: 10.1103/PhysRevE.81.046206.  Google Scholar

[21]

P. E. Latham, B. J. Richmond, P. G. Nelson and S. Nirenberg, Intrinsic dynamics in neuronal networks. I. theory, J. Neurophysiology, 83 (2000), 808-827.  doi: 10.1152/jn.2000.83.2.808.  Google Scholar

[22]

T. B. Luke, E. Barreto and P. So, Complete classification of the macroscopic behavior of a heterogeneous network of theta neurons, Neural Comput., 25 (2013), 3207-3234.  doi: 10.1162/NECO_a_00525.  Google Scholar

[23]

M. B. Martens, A. R. Houweling and P. H. E. Tiesinga, Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks, J. Comput. Neuroscience, 42 (2017), 87-106.  doi: 10.1007/s10827-016-0629-1.  Google Scholar

[24]

W. S. McCulloch and W. Pitts, The statistical organization of nervous activity, Biometrics, 4 (1948), 91-99.  doi: 10.2307/3001453.  Google Scholar

[25]

E. Montbrió, D. Pazó and A. Roxin, Macroscopic description for networks of spiking neurons, Phys. Rev. X, 5 (2015), 021028.  doi: 10.1103/PhysRevX.5.021028.  Google Scholar

[26]

M. E. J. Newman, Assortative mixing in networks, Phys. Rev. Lett., 89 (2002), 208701.  doi: 10.1103/PhysRevLett.89.208701.  Google Scholar

[27]

M. E. J. Newman, The structure and function of complex networks, SIAM Rev., 45 (2003), 167-256.  doi: 10.1137/S003614450342480.  Google Scholar

[28]

D. Q. Nykamp, D. Friedman, S. Shaker, M. Shinn and M. Vella, et al., Mean-field equations for neuronal networks with arbitrary degree distributions, Phys. Rev. E, 95 (2017), 042323. doi: 10.1103/PhysRevE.95.042323.  Google Scholar

[29]

E. Ott and T. M. Antonsen, Low dimensional behavior of large systems of globally coupled oscillators, Chaos, 18 (2008), 037113.  doi: 10.1063/1.2930766.  Google Scholar

[30]

J. G. Restrepo and E. Ott, Mean-field theory of assortative networks of phase oscillators, Europhys. Lett., 107 (2014), 60006.  doi: 10.1209/0295-5075/107/60006.  Google Scholar

[31]

C. Schmeltzer, A. H. Kihara, I. M. Sokolov and S. Rüdiger, Degree correlations optimize neuronal network sensitivity to sub-threshold stimuli, PLoS One, 10 (2015), e0121794.  doi: 10.1371/journal.pone.0121794.  Google Scholar

[32]

P. S. Skardal, J. G. Restrepo and E. Ott, Frequency assortativity can induce chaos in oscillator networks, Phys. Rev. E, 91 (2015), 060902(R).  doi: 10.1103/PhysRevE.91.060902.  Google Scholar

[33]

P. S. Skardal, J. Sun, D. Taylor and J. G. Restrepo, Effects of degree-frequency correlations on network synchronization: Universality and full phase-locking, Europhys. Lett., 101 (2013), 20001.  doi: 10.1209/0295-5075/101/20001.  Google Scholar

[34]

B. Sonnenschein, F. Sagués and L. Schimansky-Geier, Networks of noisy oscillators with correlated degree and frequency dispersion, Eur. Phys. J. B, 86 (2013), 12.  doi: 10.1140/epjb/e2012-31026-x.  Google Scholar

[35]

S. Teller, C. Granell, M. De Domenico, J. Soriano, S. Gómez and A. Arenas, Emergence of assortative mixing between clusters of cultured neurons, PLoS Comput. Biology, 10 (2014), e1003796.  doi: 10.1371/journal.pcbi.1003796.  Google Scholar

[36]

J. C. Vasquez, A. R. Houweling and P. Tiesinga, Simultaneous stability and sensitivity in model cortical networks is achieved through anti-correlations between the in- and out-degree of connectivity, Frontiers Comput. Neuroscience, 7 (2013), 156.  doi: 10.3389/fncom.2013.00156.  Google Scholar

[37]

M. Vegué and A. Roxin, Firing rate distributions in spiking networks with heterogeneous connectivity, Phys. Rev. E, 100 (2019), 022208.  doi: 10.1103/PhysRevE.100.022208.  Google Scholar

Figure 1.  Assortativity in undirected and directed networks. An undirected network (left column) is assortative if high degree nodes are more likely to be connected to high degree nodes, and low to low, than by chance (top left). Such a network is disassortative if the opposite occurs (bottom left). In directed networks (right column) there are four possible kinds of assortativity. The probability of a connection (red) is thus influenced by the number of red shaded links of the sending (left) and receiving (right) node
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  Orange circles: steady state of (19)-(20) for 20 different default networks. Blue circles: results from 50 different realisations of the $ \eta_i $ for (1)-(2), for each network. Parameters: $ \eta_0 = -2,\Delta = 0.1,K = 3 $. The orange line marks the ensemble mean value
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 3.  Comparison of steady-state values of the order parameter (top: magnitude of $ R $; bottom: $ \mbox{Re}(R) $) over a suite of adjacency matrices with varied densities of multi-edge connections ranging from none to 97%. Higher densities of multi-edges were obtained, but assortativities exceeded the target neutral values of $ \pm 0.005 $. Values shown are from simulations of (1)-(2) after initial transients decay (i.e., time t $ \geq $ 40). Each of these 10 curves correspond to a unique realisation of default $ \eta $s from the distribution $ g(\eta) $. Parameters: $ N = 5000,\eta_0 = -2,\Delta = 0.1,K = 3,q = 2 $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 4.  Real part of $ z $ at steady state for 20 different default adjacency matrices (indicated by different colors), as the number of clusters in degree space is varied. Parameters: $ \eta_0 = -2,\Delta = 0.1,K = 3 $
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 5.  Six largest singular values of the SVD decomposition of $ E $ as a function of assortativity coefficent, for 4 types of assortativity
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 6.  Average firing rate at fixed points of (30)-(31) as a function of $ \eta_0 $, for the 4 types of assortativity. For each type of assortativity curves are plotted for $ r = 0 $ (black), $ r = -0.2 $ (blue) and $ r = 0.2 $ (green). Solid lines indicate stable and dashed lines unstable fixed points. Parameters: $ K = 3,\Delta = 0.1 $
Figure Options

Download full-size image

Download as PowerPoint slide

Fig. 6 as $ r $ is varied. Curves in Figure 6 correspond to vertical slices at $ r = 0,\pm 0.2 $. The network is bistable in region $ B $ and has a single stable fixed point in regions A and C">Figure 7.  Continuation of the saddle-node bifurcations seen in the upper two panels of Fig. 6 as $ r $ is varied. Curves in Figure 6 correspond to vertical slices at $ r = 0,\pm 0.2 $. The network is bistable in region $ B $ and has a single stable fixed point in regions A and C
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 8.  Average firing rate at fixed points of (30)-(31) as a function of $ \eta_0 $, for the 4 types of assortativity. For each type of assortativity curves are plotted for $ r = 0 $ (black), $ r = -0.2 $ (blue) and $ r = 0.2 $ (green). In addition there are oscillations in certain regions and dash-dotted lines outline the minimal and maximal firing rate over one period of oscillation. The (in, in)-plot in the top left corner contains a zoom of rest of the panel, and the (out, in)-plot contains a subplot with the oscillation's period for $ r = 0 $ and which is aligned with the outer $ \eta_0 $ axis
Figure Options

Download full-size image

Download as PowerPoint slide

Fig. 8. Solid black lines indicate saddle-node bifurcations, dashed blue is a Hopf bifurcation and dashed red a homoclinic bifurcation. Curves in Figure 8 can be understood as vertical slices through the respective plot at $ r = 0,\pm 0.2 $. See text for explanation of labels">Figure 9.  Continuation of bifurcations seen in upper panels of Fig. 8. Solid black lines indicate saddle-node bifurcations, dashed blue is a Hopf bifurcation and dashed red a homoclinic bifurcation. Curves in Figure 8 can be understood as vertical slices through the respective plot at $ r = 0,\pm 0.2 $. See text for explanation of labels
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 10.  Permutation method initial matrices illustration. Top: $ A^{(0)} $ showing arrangement of edge entries (all solo connections: $ \rho_{m}^{+} = 0 $) for each row aligned left where row sums add up to $ k_{in} $. If multi-edges are desired, we simply distribute them in the rows of $ A^{(0)} $ satisfying the proportion, $ \rho_{m}^{+} = 0 $ and the row sum. Bottom: permutation of rows in $ A^{(0)} $ into this example $ A^{(1)} $. Note row sums still add up to $ k_{in} $, with column sums adding to a current $ k_{out}^{(1)} $ — likely violating the designated $ k_{out} $
Figure Options

Download full-size image

Download as PowerPoint slide

[1]

T. Jäger. Neuronal coding of pacemaker neurons -- A random dynamical systems approach. Communications on Pure & Applied Analysis, 2011, 10 (3) : 995-1009. doi: 10.3934/cpaa.2011.10.995
[2]

Andrey Olypher, Jean Vaillant. On the properties of input-to-output transformations in neuronal networks. Mathematical Biosciences & Engineering, 2016, 13 (3) : 579-596. doi: 10.3934/mbe.2016009
[3]

Runxia Wang, Haihong Liu, Fang Yan, Xiaohui Wang. Hopf-pitchfork bifurcation analysis in a coupled FHN neurons model with delay. Discrete & Continuous Dynamical Systems - S, 2017, 10 (3) : 523-542. doi: 10.3934/dcdss.2017026
[4]

Qixiang Wen, Shenquan Liu, Bo Lu. Firing patterns and bifurcation analysis of neurons under electromagnetic induction. Electronic Research Archive, , () : -. doi: 10.3934/era.2021034
[5]

Raoul-Martin Memmesheimer, Marc Timme. Stable and unstable periodic orbits in complex networks of spiking neurons with delays. Discrete & Continuous Dynamical Systems, 2010, 28 (4) : 1555-1588. doi: 10.3934/dcds.2010.28.1555
[6]

Lixia Duan, Zhuoqin Yang, Shenquan Liu, Dunwei Gong. Bursting and two-parameter bifurcation in the Chay neuronal model. Discrete & Continuous Dynamical Systems - B, 2011, 16 (2) : 445-456. doi: 10.3934/dcdsb.2011.16.445
[7]

Sungwoo Ahn, Winfried Just. Digraphs vs. dynamics in discrete models of neuronal networks. Discrete & Continuous Dynamical Systems - B, 2012, 17 (5) : 1365-1381. doi: 10.3934/dcdsb.2012.17.1365
[8]

Claudio Canuto, Anna Cattani. The derivation of continuum limits of neuronal networks with gap-junction couplings. Networks & Heterogeneous Media, 2014, 9 (1) : 111-133. doi: 10.3934/nhm.2014.9.111
[9]

Johanna D. García-Saldaña, Armengol Gasull, Hector Giacomini. Bifurcation values for a family of planar vector fields of degree five. Discrete & Continuous Dynamical Systems, 2015, 35 (2) : 669-701. doi: 10.3934/dcds.2015.35.669
[10]

Junbo Jia, Zhen Jin, Lili Chang, Xinchu Fu. Structural calculations and propagation modeling of growing networks based on continuous degree. Mathematical Biosciences & Engineering, 2017, 14 (5&6) : 1215-1232. doi: 10.3934/mbe.2017062
[11]

Feng Zhang, Wei Zhang, Pan Meng, Jianzhong Su. Bifurcation analysis of bursting solutions of two Hindmarsh-Rose neurons with joint electrical and synaptic coupling. Discrete & Continuous Dynamical Systems - B, 2011, 16 (2) : 637-651. doi: 10.3934/dcdsb.2011.16.637
[12]

Linghai Zhang. Wave speed analysis of traveling wave fronts in delayed synaptically coupled neuronal networks. Discrete & Continuous Dynamical Systems, 2014, 34 (5) : 2405-2450. doi: 10.3934/dcds.2014.34.2405
[13]

Qingyun Wang, Xia Shi, Guanrong Chen. Delay-induced synchronization transition in small-world Hodgkin-Huxley neuronal networks with channel blocking. Discrete & Continuous Dynamical Systems - B, 2011, 16 (2) : 607-621. doi: 10.3934/dcdsb.2011.16.607
[14]

Fernando Antoneli, Ana Paula S. Dias, Rui Paiva. Coupled cell networks: Hopf bifurcation and interior symmetry. Conference Publications, 2011, 2011 (Special) : 71-78. doi: 10.3934/proc.2011.2011.71
[15]

Bernold Fiedler. Global Hopf bifurcation in networks with fast feedback cycles. Discrete & Continuous Dynamical Systems - S, 2021, 14 (1) : 177-203. doi: 10.3934/dcdss.2020344
[16]

Lubomir Kostal, Shigeru Shinomoto. Efficient information transfer by Poisson neurons. Mathematical Biosciences & Engineering, 2016, 13 (3) : 509-520. doi: 10.3934/mbe.2016004
[17]

Udhayakumar Kandasamy, Rakkiyappan Rajan. Hopf bifurcation of a fractional-order octonion-valued neural networks with time delays. Discrete & Continuous Dynamical Systems - S, 2020, 13 (9) : 2537-2559. doi: 10.3934/dcdss.2020137
[18]

Martn P. Árciga Alejandre, Elena I. Kaikina. Mixed initial-boundary value problem for Ott-Sudan-Ostrovskiy equation. Discrete & Continuous Dynamical Systems, 2012, 32 (2) : 381-409. doi: 10.3934/dcds.2012.32.381
[19]

Jakub Cupera. Diffusion approximation of neuronal models revisited. Mathematical Biosciences & Engineering, 2014, 11 (1) : 11-25. doi: 10.3934/mbe.2014.11.11
[20]

Jianzhong Su, Humberto Perez-Gonzalez, Ming He. Regular bursting emerging from coupled chaotic neurons. Conference Publications, 2007, 2007 (Special) : 946-955. doi: 10.3934/proc.2007.2007.946

 Impact Factor: 

Tools

Metrics

  • PDF downloads (107)
  • HTML views (289)
  • Cited by (1)

Other articles
by authors

[Back to Top]

Copyright © 2021 American Institute of Mathematical Sciences