Synchrony-induced modes of oscillation of a neural field model

Abstract

We investigate the modes of oscillation of heterogeneous ring-networks of quadratic integrate-and-fire neurons with non-local, space-dependent coupling. Perturbations of the equilibrium state with a particular wave number produce transient standing waves with a specific frequency, analogous to those in a tense string. In the neuronal network, the equilibrium corresponds to a spatially homogeneous, asynchronous state. Perturbations of this state excite the network's oscillatory modes, which reflect the interplay of episodes of synchronous spiking with the excitatory-inhibitory spatial interactions. In the thermodynamic limit, an exact low-dimensional neural field model describing the macroscopic dynamics of the network is derived. This allows us to obtain formulas for the Turing eigenvalues of the spatially-homogeneous state, and hence to obtain its stability boundary. We find that the frequency of each Turing mode depends on the corresponding Fourier coefficient of the synaptic pattern of connectivity. The decay rate instead, is identical for all oscillation modes as a consequence of the heterogeneity-induced desynchronization of the neurons. Finally, we numerically compute the spectrum of spatially-inhomogeneous solutions branching from the Turing bifurcation, showing that similar oscillatory modes operate in neural bump states, and are maintained away from onset.

References in corpus (4)

• Low Dimensional Behavior of Large Systems of Globally Coupled Oscillators
• Macroscopic description for networks of spiking neurons
• Temporal decorrelation of collective oscillations in neural networks with local inhibition and long-range excitation
• Synchronization scenarios in the Winfree model of coupled oscillators

Cited by in corpus (5)

• Exact mean-field theory explains the dual role of electrical synapses in collective synchronization
• Mean-field equations for neural populations with $q$-Gaussian heterogeneities
• Mathematical framework for breathing chimera states
• Low-dimensional model for adaptive networks of spiking neurons
• Bumps, chimera states, and Turing patterns in systems of coupled active rotators