Electrical stimulation of neural populations is a key tool for understanding neural dynamics and developing treatments. To investigate the effects of external stimulation on a basic cortical motif, we analyse the dynamical properties of an efficient mean-field neural mass model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons  and validate the results using detailed network simulations. The state space of the mean-field model and of the detailed networkare closely related. They reveal asynchronous up and down-states, bistable regions , and oscillatory regions corresponding to fast excitation-inhibition and slow excitation-adaptation feedback loops. Within this dynamical landscape, external stimuli can cause state transitions, such as turning on and off oscillations. Oscillatory input can frequency-entrain and phase-lock endogenous oscillations. The effects of  external stimulation are well-predicted by the mean-field model, further underpinning the utility of low-dimensional neural mass models.
Electrical stimulation of large neural networks is a key tool for understanding neural population dynamics and ultimately for developing clinical treatments. However, computational models of cortical networks of spiking neurons are inherently hard to simulate and analyze. We present an efficient mean-field model of excitatory and inhibitory adaptive exponential integrate-and-fire (AdEx) neurons of which we analyse the dynamical properties and validate the results using detailed network simulations. Bifurcation diagrams reflecting the network's state reveal asynchronous up and down-states, bistable regimes , and oscillatory regions corresponding to fast excitation-inhibition and slow excitation-adaptation feedback loops. In this dynamical landscape, electrical stimuli can cause state transitions, such as turning on and off oscillations. Oscillatory input can frequency-entrain and phase-lock endogenous oscillations. The effects of time-varying external stimulation are well-predicted by the mean-field model, further underpinning the utility of low-dimensional neural mass models.
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neural populations
large neural networks
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dynamics and developing treatments. To investigate the effects of external stimulation on a basic cortical motif, we analyse the dynamical properties of
population dynamics and ultimately for developing clinical treatments. However, computational models of cortical networks of spiking neurons are inherently hard to simulate and analyze. We present
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neural mass
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of which we analyse the dynamical properties
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The state space of the mean-field model and of the detailed networkare closely related. They
Bifurcation diagrams reflecting the network's state
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regions
regimes
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Within
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external
electrical
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time-varying