Discrete Synaptic Events Induce Global Oscillations in Balanced Neural Networks.

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Despite the fact that neural dynamics is triggered by discrete synaptic events, the neural response is usually obtained within the diffusion approximation representing the synaptic inputs as Gaussian noise. We derive a mean-field formalism encompassing synaptic shot noise for sparse balanced neural networks. For low (high) excitatory drive (inhibitory feedback) global oscillations emerge via continuous or hysteretic transitions, correctly predicted by our approach, but not from the diffusion approximation. At sufficiently low in-degrees the nature of these global oscillations changes from drift driven to cluster activation.