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dc.contributor.author
Bel, Andrea Liliana
dc.contributor.author
Rotstein, Horacio
dc.date.available
2020-05-25T23:26:51Z
dc.date.issued
2019-03-20
dc.identifier.citation
Bel, Andrea Liliana; Rotstein, Horacio; Membrane potential resonance in non-oscillatory neurons interacts with synaptic connectivity to produce network oscillations; Springer; Journal of Computational Neuroscience; 46; 2; 20-3-2019; 169-195
dc.identifier.issn
0929-5313
dc.identifier.uri
http://hdl.handle.net/11336/105864
dc.description.abstract
Several neuron types have been shown to exhibit (subthreshold) membrane potential resonance (MPR), defined as the occurrence of a peak in their voltage amplitude response to oscillatory input currents at a preferred (resonant) frequency. MPR has been investigated both experimentally and theoretically. However, whether MPR is simply an epiphenomenon or it plays a functional role for the generation of neuronal network oscillations and how the latent time scales present in individual, non-oscillatory cells affect the properties of the oscillatory networks in which they are embedded are open questions. We address these issues by investigating a minimal network model consisting of (i) a non-oscillatory linear resonator (band-pass filter) with 2D dynamics, (ii) a passive cell (low-pass filter) with 1D linear dynamics, and (iii) nonlinear graded synaptic connections (excitatory or inhibitory) with instantaneous dynamics. We demonstrate that (i) the network oscillations crucially depend on the presence of MPR in the resonator, (ii) they are amplified by the network connectivity, (iii) they develop relaxation oscillations for high enough levels of mutual inhibition/excitation, and (iv) the network frequency monotonically depends on the resonators resonant frequency. We explain these phenomena using a reduced adapted version of the classical phase-plane analysis that helps uncovering the type of effective network nonlinearities that contribute to the generation of network oscillations. We extend our results to networks having cells with 2D dynamics. Our results have direct implications for network models of firing rate type and other biological oscillatory networks (e.g, biochemical, genetic).
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Springer
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
INHIBITORY NETWORKS
dc.subject
LATENT TIME SCALES
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NEURONAL FILTERS
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PREFERRED FREQUENCY RESPONSE
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Matemática Aplicada
dc.subject.classification
Matemáticas
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CIENCIAS NATURALES Y EXACTAS
dc.title
Membrane potential resonance in non-oscillatory neurons interacts with synaptic connectivity to produce network oscillations
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-05-04T13:30:47Z
dc.journal.volume
46
dc.journal.number
2
dc.journal.pagination
169-195
dc.journal.pais
Alemania
dc.journal.ciudad
Berlin
dc.description.fil
Fil: Bel, Andrea Liliana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
dc.description.fil
Fil: Rotstein, Horacio. New Jersey Institute of Technology; Estados Unidos. Rutgers University; Estados Unidos
dc.journal.title
Journal of Computational Neuroscience
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1007/s10827-019-00710-y
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs10827-019-00710-y
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