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Main Title: How adaptation shapes spike rate oscillations in recurrent neuronal networks
Author(s): Augustin, Moritz
Ladenbauer, Josef
Obermayer, Klaus
Type: Article
Language Code: en
Is Part Of: 10.14279/depositonce-6178
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.
Issue Date: 2013
Date Available: 25-Oct-2017
DDC Class: 500 Naturwissenschaften und Mathematik
Subject(s): spike frequency adaptation
rate models
network dynamics
recurrent network
Journal Title: Frontiers in computational neuroscience
Publisher: Frontiers
Publisher Place: Lausanne
Volume: 7
Issue: 9
Publisher DOI: 10.3389/fncom.2013.00009
EISSN: 1662-5188
Appears in Collections:FG Neuronale Informationsverarbeitung » Publications

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