The DOI matches its Crossref record; 54 of the 55 references it cites were found in a registry or an index.
Dynamics of Spiking Neurons Connected by Both Inhibitory and Electrical Coupling
Open accessAbstract
We study the dynamics of a pair of intrinsically oscillating leaky integrate-and-fire neurons (identical and noise-free) connected by combinations of electrical and inhibitory coupling. We use the theory of weakly coupled oscillators to examine how synchronization patterns are influenced by cellular properties (intrinsic frequency and the strength of spikes) and coupling parameters (speed of synapses and coupling strengths). We find that, when inhibitory synapses are fast and the electrotonic effect of the suprathreshold portion of the spike is large, increasing the strength of weak electrical coupling promotes synchrony. Conversely, when inhibitory synapses are slow and the electrotonic effect of the suprathreshold portion of the spike is small, increasing the strength of weak electrical coupling promotes antisynchrony (see Fig. 10). Furthermore, our results indicate that, given a fixed total coupling strength, either electrical coupling alone or inhibition alone is better at enhancing neural synchrony than a combination of electrical and inhibitory coupling. We also show that these results extend to moderate coupling strengths.
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“Events such as the action potential AHP, which are faithfully transmitted across electrical synapses, serve to deinactivate intrinsic voltage-gated conductances across the many connected cells. This allows for simultaneous cell activation once the AHP relaxes, further enhancing synchronous activity (107, 397, 671).”
From Hippocampal GABAergic Inhibitory Interneurons (2017) · cites this paper as [671]
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Cites
Chemical Oscillations, Waves, and Turbulence
Kuramoto, Y. (1984) · Springer series in synergetics · doi:10.1007/978-3-642-69689-3 · Cited by 7,242
Whittington, M. A., Traub, R. D., & Jefferys, J. G. R. (1995) · Nature · doi:10.1038/373612a0 · Cited by 1,613
Gamma Oscillation by Synaptic Inhibition in a Hippocampal Interneuronal Network Model
Wang, X., & Buzsáki, G. (1996) · Journal of Neuroscience · doi:10.1523/jneurosci.16-20-06402.1996 · Cited by 1,570
Two networks of electrically coupled inhibitory neurons in neocortex
Gibson, J. R., Beierlein, M., & Connors, B. W. (1999) · Nature · doi:10.1038/47035 · Cited by 1,404
Ylinen, A., Bragin, A., Nadasdy, Z., Jando, G., Szabo, I., Sik, A., & Buzsaki, G. (1995) · The Journal of Neuroscience · doi:10.1523/jneurosci.15-01-00030.1995 · Cited by 1,092
Organizing principles for a diversity of GABAergic interneurons and synapses in the neocortex
Gupta, A., Wang, Y., & Markram, H. (2000) · Science · doi:10.1126/science.287.5451.273 · Cited by 1,036
Type I membranes, phase resetting curves, and synchrony
Ermentrout, B. (1996) · Neural Computation · doi:10.1162/neco.1996.8.5.979 · Cited by 1,001
A network of fast-spiking cells in the neocortex connected by electrical synapses
Galarreta, M., & Hestrin, S. (1999) · Nature · doi:10.1038/47029 · Cited by 960
Cholinergic induction of network oscillations at 40 Hz in the hippocampus in vitro
Fisahn, A., Pike, F. G., Buhl, E. H., & Paulsen, O. (1998) · Nature · doi:10.1038/28179 · Cited by 884
Temporal structure in spatially organized neuronal ensembles: a role for interneuronal networks
Buzsáki, G., & Chrobak, J. J. (1995) · Current Opinion in Neurobiology · doi:10.1016/0959-4388(95)80012-3 · Cited by 878
Research works printed but not linked: 1
- Lewis TJ, Gibson JR, Connors BW, Rinzel J (2001) Dynamics of neurons connected by inhibitory and electrical synapses. Society for Neuroscience Abstract 504: 16.
Cited by
Hippocampal GABAergic Inhibitory Interneurons
Pelkey, K. A., Chittajallu, R., Craig, M. T., Tricoire, L., Wester, J. C., & McBain, C. J. (2017) · Physiological Reviews · doi:10.1152/physrev.00007.2017 · Cited by 889
Dynamical principles in neuroscience
Rabinovich, M. I., Varona, P., Selverston, A. I., & Abarbanel, H. D. I. (2006) · Reviews of Modern Physics · doi:10.1103/revmodphys.78.1213 · Cited by 840
Electrical Coupling and Neuronal Synchronization in the Mammalian Brain
Bennett, M. V. L., & Zukin, R. S. (2004) · Neuron · doi:10.1016/s0896-6273(04)00043-1 · Cited by 833
ELECTRICAL SYNAPSES IN THE MAMMALIAN BRAIN
Connors, B. W., & Long, M. A. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.26.041002.131128 · Cited by 784
The Dynamics of Legged Locomotion: Models, Analyses, and Challenges
Holmes, P. J., Full, R. J., Koditschek, D., & Guckenheimer, J. M. (2006) · SIAM Review · doi:10.1137/s0036144504445133 · Cited by 728
Metadata
- Authors
- Lewis, T. J., & Rinzel, J.
- Affiliations
- Courant Institute of Mathematical Sciences (US)
- Corresponding author
- Lewis, Timothy J.
- Venue
- Journal of Computational Neuroscience
- Published
- 2003-05
- Volume
- 14
- Issue
- 3
- Pages
- 283-309
- ISSN
- 0929-5313 (ISSN-L)
- 1573-6873
- OpenAlex
- W2131913615
- Cited by
- 259
- References
- 55
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 4 · 2023: 7 · 2022: 5 · 2021: 9 · 2020: 7 · 2019: 12 · 2018: 9 · 2017: 10 · 2016: 14 · 2015: 14 · 2014: 11 · 2013: 22 · 2012: 11
- FWCI
- 7.46
- Type
- journal article
- Language
- en
- Open access
- green
- escholarship.org
- Licence
- Locations
- Journal of Computational Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (5f32mh012873-03)
- National Institute of Mental Health (MH12873)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Nonlinear Dynamics and Pattern Formation
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
- Keywords
- leaky integrate-and-fire neurons, electrical coupling, inhibitory coupling, neuronal synchronization, anti-synchronization, weakly coupled oscillators, intrinsic frequency, coupling strength
- MeSH terms
- Action Potentials, Animals, Biological Clocks, Cell Membrane, Central Nervous System, Cortical Synchronization, Humans, Interneurons, Models, Neurological, Nerve Net, Neural Inhibition, Neural Pathways, Synaptic Transmission, Synapses, Gap Junctions, Nonlinear Dynamics
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