Spiral Waves in Disinhibited Mammalian Neocortex

Huang, Xing; Troy, William C.; Yang, Qian; Ma, Hongtao; Laing, Carlo R.; Schiff, Steven J.; Wu, Jian

doi:10.1523/jneurosci.2705-04.2004

Cited by 374 publications

(308 citation statements)

References 35 publications

(41 reference statements)

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“…Most of these rhythms (at least in vertebrates) are generated by the coherent oscillations of many coupled neurons. Sometimes they are synchronous and in other cases they are organized into patterns such as waves (for example in the swimming pattern of the lamprey; Cohen et al 1992) or even rotating waves (in certain brain slice preparations; Huang et al 2004. ) Thus, one of the outstanding theoretical questions in our understanding of neural rhythms is how the coupling interacts with the intrinsic dynamics to generate these patterns of oscillations.…”

Section: Introductionmentioning

confidence: 99%

Delays and weakly coupled neuronal oscillators

Ermentrout

2009

Phil. Trans. R. Soc. A.

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We use weakly coupled oscillator theory to study the effects of delays on coupled systems of neuronal oscillators. We explore, first, simple pairs with constant delays and then examine the role of distributed delays as would occur in systems with dendritic branches or in networks where there is a distance-dependent conductance delay. In the latter, we use mean field theory to show the emergence of travelling waves and the loss of synchronization. Next, we consider phase models with stronger coupling and delays in the state variables. We show that they have a richer dynamics but one that is still similar to the weakly coupled case.

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Section: Introductionmentioning

confidence: 99%

Delays and weakly coupled neuronal oscillators

Ermentrout

2009

Phil. Trans. R. Soc. A.

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“…Travelling waves of activity are also of interest. Experimentally, these can be induced by stimulating pharmacologically treated neural tissue slices (Pinto & Ermentrout 2001a;Huang et al 2004); they have also been observed in several different areas of the cortex of awake animals, often when no stimulation 152…”

Section: Introductionmentioning

confidence: 99%

The importance of different timings of excitatory and inhibitory pathways in neural field models

Laing

Coombes

2006

Network: Computation in Neural Systems

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In this paper, we consider a neural field model comprised of two distinct populations of neurons, excitatory and inhibitory, for which both the velocities of action potential propagation and the time courses of synaptic processing are different. Using recently-developed techniques, we construct the Evans function characterising the stability of both stationary and travelling wave solutions, under the assumption that the firing rate function is the Heaviside step. We find that these differences in timing for the two populations can cause instabilities of these solutions, leading to, for example, stationary breathers. We also analyse "anti-pulses", a novel type of pattern for which all but a small interval of the domain (in moving coordinates) is active. These results extend previous work on neural fields with space-dependent delays, and demonstrate the importance of considering the effects of the different time-courses of excitatory and inhibitory neural activity.

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“…Stable rotating spiral waves in rat neocortical slices visualized by voltagesensitive dye imaging were found in experiments [6,7]. As noted in [6,7], spiral waves might serve as pacemakers for the emergent population to generate periodic activity in a nonoscillatory network without the need for individual cellular pacemakers. It is interesting to simulate and investigate the formation and breakup of spiral waves in networks of neurons with different topologies.…”

mentioning

confidence: 75%

“…breakup, channel noise, factor of synchronization, probability of long-range connection Collective electrical behaviors of neurons and oscillators in networks often have spatiotemporal patterns [1][2][3][4][5][6][7][8][9][10][11]. A spiral wave is one such spatial pattern and is often observed in excitable and oscillatory media.…”

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confidence: 99%

Channel noise-induced phase transition of spiral wave in networks of Hodgkin-Huxley neurons

Huang

et al. 2011

Chin. Sci. Bull.

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The phase transition of spiral waves in networks of Hodgkin-Huxley neurons induced by channel noise is investigated in detail. All neurons in the networks are coupled with small-world connections, and the results are compared with the case for regular networks, in which all neurons are completely coupled with nearest-neighbor connections. A statistical variable is defined to study the collective behavior and phase transition of the spiral wave due to the channel noise and topology of the network. The effect of small-world connection networks is described by local regular networks and long-range connection with certain probability p. The numerical results confirm that (1) a stable rotating spiral wave can be developed and maintain robust with low p, where the breakup of the spiral wave and turbulence result from increasing the probability p to a certain threshold; (2) appropriate intensity of the optimized channel noise can develop a spiral wave among turbulent states in small-world connection networks of H-H neurons; and (3) regular connection networks are more robust to channel noise than small-world connection networks. A spiral wave in a small-world network encounters instability more easily as the membrane temperature is increased to a certain high threshold.breakup, channel noise, factor of synchronization, probability of long-range connection Citation:Ma J, Wu Y, Ying H P, et al. Channel noise-induced phase transition of spiral wave in networks of Hodgkin-Huxley neurons.

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Spiral Waves in Disinhibited Mammalian Neocortex

Cited by 374 publications

References 35 publications

Delays and weakly coupled neuronal oscillators

Delays and weakly coupled neuronal oscillators

The importance of different timings of excitatory and inhibitory pathways in neural field models

Channel noise-induced phase transition of spiral wave in networks of Hodgkin-Huxley neurons

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