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).

Highlights

  • Neuronal network oscillations emerge from the cooperative activity of the participating neurons and the network connectivity and involve the interplay of the nonlinearities and time scales present in the ionic and synaptic currents

  • Understanding how the intrinsic oscillatory properties of the participating nodes interact with the network connectivity is key for the mechanistic description of biological network oscillations

  • We have identified a minimal network model consisting of a resonator, a low-pass filter and nonlinear connectivity with no dynamics

Read more

Summary

Introduction

Neuronal network oscillations emerge from the cooperative activity of the participating neurons and the network connectivity and involve the interplay of the nonlinearities and time scales present in the ionic and synaptic currents. The network time scales directly reflect the time scales of the individual neurons. This class includes the networks that synchronize the oscillatory activity of the individual neurons where the frequency of both (network and individual neurons) belong to the same (narrow) frequency band. The oscillatory time scales are latent (or hidden) at the individual neuron level and become apparent at the network level. The oscillatory networks of non-oscillatory neurons we investigate in this paper belong to this class. Because the individual cells are non-oscillatory, the resonant frequency is an oscillatory latent time scale

Methods
Results
Discussion
Conclusion
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call