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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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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.
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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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