| Summary: | Many experimental and theoretical studies have suggested that the reliable propagation of synchronous neural activity is crucial for neural information processing. The propagation of synchronous firing activity in so-called synfirechains has been studied extensively in feed-forward networks ofspiking neurons. However, it remains unclear how such neural activity could emerge in recurrentneuronal networks through synaptic plasticity. In this study, we investigate whether local excitation, i.e., neurons that fire at a higherfrequency than the other, spontaneously active neurons in the network, can shapea network to allow for synchronous activity propagation. We use two-dimensional,locally connected and heterogeneous neuronal networks with spike-timingdependent plasticity (STDP). We find that, in our model, local excitation drives profound network changes within seconds. In the emergent network, neural activity propagates synchronously through the network. Thisactivity originates from the site of the local excitation and propagates throughthe network.The synchronous activity propagation persists, even when the local excitation is removed, since it derives from the synaptic weight matrix. Importantly, once this connectivity is established it remains stable even inthe presence of spontaneous activity. Our results suggest that synfire-chain-like activity can emerge in a relativelysimple way in realistic neural networks by locally exciting the desired originof the neuronal sequence.
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