Abstract Previous papers have studied a leaky Integrate-and-Fire (IF) model whose connectivity was designed in such a manner as to favor the spontaneous emergence of collective oscillatory spatiotemporal patterns of spikes. In this model the alternation of up and down states does not depend on a kind of neuron bistability, nor on synaptic depression, but is rather a network effect. In order to check if the transition region with bimodal distribution of the firing rate survive even after changes in the topology of the network, in this work we shuffle all the connections. For each chosen connection we change the presynaptic neuron, choosing as the new presynaptic one a random neuron of the network. After shuffling the connections, not only the number of excitatory and inhibitory connections is the same as before, but also the strengths of the connections are the same, and only the topology is changed. We observe that shuffling the connections changes the features of the dynamics dramatically. Before shuffling the system has a transition from a regime of Poissonian noisy activity to a regime of spontaneous persistent collective replay, and at the transition point the network dynamics shows an intermittent reactivation of the stored patterns, with alternation of up and down state, and bimodal distribution of spiking rate. Shuffling all the connections we observe that the transition region with bimodal distribution disappears, and the dynamics is Poissonian with unimodal rate distribution for all the investigated parameters. These results show the role of topology in dictating the emerging collective dynamics of neural circuits.
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