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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  
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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