Modeling Spontaneous Episodic Activity in Developing Neuronal Networks

Abstract

Neuronal networks are extraordinarily complex systems, structurally and dynamically, given the number of elements that compose them, their functional architecture, their plasticity, and their nonlinear mechanisms for signaling over vast ranges of time scales. One approach to understanding how neuronal circuits generate activity is to study developing networks that are relatively simpler, before any experienced-based specialization has occurred. Here, we present a model for the generation of spontaneous, episodic activity by developing spinal cord networks. This model only represents the averaged activity and excitability in the network, assumed purely excitatory. In the 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 leads to prediction/comprehension of experimental observations. Although the complexity of the system has been restricted to interactions between fast positive and slow negative feedback, the emergent feature of the network rhythm was captured, and it applies to many developing/excitatory networks. An open question is whether this mechanism can help us explain the activity of more complex/mature networks including inhibitory connections.