Biomedical Engineering Reference
In-Depth Information
5.2
MODELING SPONTANEOUS EPISODIC
ACTIVITY IN DEVELOPING
NEURONAL NETWORKS
Joël Tabak
Laboratory of Neural Control, Section on Developmental Neurobiology,
NINDS, National Institutes of Health, Bethesda, Maryland
John Rinzel
Courant Institute of Mathematical Science and Center for Neural Science,
New York University, New York
Neuronal networks are extraordinarily complex systems, structurally and dynamically,
given the number of elements that compose them, their functional architecture, their plas-
ticity, 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 oc-
curred. 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 complex-
ity of the system has been restricted to interactions between fast positive and slow nega-
tive 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.
Address correspondence to: Joël Tabak, Department of Biological Science, BRF/206, Florida State
University, Tallahassee, Florida 32306 (joel@neuro.fsu.edu).
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