Homeostatic activity-dependent tuning of recurrent networks for robust propagation of activity

BioRxiv : the Preprint Server for Biology
Julijana GjorgjievaStephen J Eglen

Abstract

Developing neuronal networks display spontaneous rhythmic bursts of action potentials that are necessary for circuit organization and tuning. While spontaneous activity has been shown to instruct map formation in sensory circuits, it is unknown whether it plays a role in the organization of motor networks that produce rhythmic output. Using computational modeling we investigate how recurrent networks of excitatory and inhibitory neuronal populations assemble to produce robust patterns of unidirectional and precisely-timed propagating activity during organism locomotion. One example is provided by the motor network in Drosophila larvae, which generates propagating peristaltic waves of muscle contractions during crawling. We examine two activity-dependent models which tune weak network connectivity based on spontaneous activity patterns: a Hebbian model, where coincident activity in neighboring populations strengthens connections between them; and a homeostatic model, where connections are homeostatically regulated to maintain a constant level of excitatory activity based on spontaneous input. The homeostatic model tunes network connectivity to generate robust activity patterns with the appropriate timing relationships between ne...Continue Reading

Related Concepts

Biological Neural Networks
Patterns
Neurons
Action Potentials
Drosophila
Locomotion
Larva
Neuronal
Organism
Cellular Component Organization

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