Bursting in excitatory neural networks

Rhythmic activities underly many crucial functions such as locomotion, respiration, secretion, etc. Models of network rhythmicity have generally included cellular oscillators or inhibitory connections between antagonistic centers. Here, we present a model of the spontaneous episodicactivity in developing spinal cord networks that include neither of these two components. This model only represents the averaged activity and excitability in the network, assumed purely excitatory. In our model, positive feedback through excitatory connections generates episodes of activity, which are terminated by a slow, activity-dependent depression of network activity (slow negative feedback). This idealized model allowed a qualitative understanding of the network dynamics which lead to prediction and comprehension of experimental observations. Although the complexity of the system has been restricted to the interactions between fast positive and slow negative feedback, the emergent feature of the network rhythm was captured, and it applies to many developinglexcitatory networks. Finally, we show that this model of network rhythmicity behaves similarly to models of repetitive bursting in single cells.