How adaptation shapes spike rate oscillations in recurrent neuronal networks
| dc.contributor.author | Augustin, Moritz | |
| dc.contributor.author | Ladenbauer, Josef | |
| dc.contributor.author | Obermayer, Klaus | |
| dc.date.accessioned | 2017-10-25T11:17:49Z | |
| dc.date.available | 2017-10-25T11:17:49Z | |
| dc.date.issued | 2013 | |
| dc.description.abstract | Neural mass signals from in-vivo recordings often show oscillations with frequencies ranging from <1 to 100 Hz. Fast rhythmic activity in the beta and gamma range can be generated by network-based mechanisms such as recurrent synaptic excitation-inhibition loops. Slower oscillations might instead depend on neuronal adaptation currents whose timescales range from tens of milliseconds to seconds. Here we investigate how the dynamics of such adaptation currents contribute to spike rate oscillations and resonance properties in recurrent networks of excitatory and inhibitory neurons. Based on a network of sparsely coupled spiking model neurons with two types of adaptation current and conductance-based synapses with heterogeneous strengths and delays we use a mean-field approach to analyze oscillatory network activity. For constant external input, we find that spike-triggered adaptation currents provide a mechanism to generate slow oscillations over a wide range of adaptation timescales as long as recurrent synaptic excitation is sufficiently strong. Faster rhythms occur when recurrent inhibition is slower than excitation and oscillation frequency increases with the strength of inhibition. Adaptation facilitates such network-based oscillations for fast synaptic inhibition and leads to decreased frequencies. For oscillatory external input, adaptation currents amplify a narrow band of frequencies and cause phase advances for low frequencies in addition to phase delays at higher frequencies. Our results therefore identify the different key roles of neuronal adaptation dynamics for rhythmogenesis and selective signal propagation in recurrent networks. | en |
| dc.identifier.eissn | 1662-5188 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/6986 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-6322 | |
| dc.language.iso | en | en |
| dc.relation.ispartof | 10.14279/depositonce-6178 | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 500 Naturwissenschaften und Mathematik | de |
| dc.subject.other | spike frequency adaptation | en |
| dc.subject.other | adaptation | en |
| dc.subject.other | oscillations | en |
| dc.subject.other | rate models | en |
| dc.subject.other | network dynamics | en |
| dc.subject.other | Fokker–Planck | en |
| dc.subject.other | mean-field | en |
| dc.subject.other | recurrent network | en |
| dc.title | How adaptation shapes spike rate oscillations in recurrent neuronal networks | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.doi | 10.3389/fncom.2013.00009 | en |
| dcterms.bibliographicCitation.issue | 9 | en |
| dcterms.bibliographicCitation.journaltitle | Frontiers in computational neuroscience | en |
| dcterms.bibliographicCitation.originalpublishername | Frontiers | en |
| dcterms.bibliographicCitation.originalpublisherplace | Lausanne | en |
| dcterms.bibliographicCitation.volume | 7 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Neuronale Informationsverarbeitung | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Neuronale Informationsverarbeitung | de |
| tub.affiliation.institute | Inst. Softwaretechnik und Theoretische Informatik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |