Title
Synchrony and asynchrony in neural networks
Abstract
The dynamics of large networks is an important and fascinating problem. Key examples are the Internet, social networks, and the human brain. In this paper we consider a model introduced by DeVille and Peskin [6] for a stochastic pulse-coupled neural network. The key feature and novelty in their approach is that they describe the interactions of a neuronal system as a discrete-state stochastic dynamical network. This idealization has two benefits: it captures essential features of neuronal behavior, and it allows the study of spontaneous synchronization, an important phenomenon in neuronal networks that is well-studied but unfortunately far from being well-understood. In synchronous behavior the firing of one neuron leads to the firing of other neurons, which in turn may set off a chain reaction that often involves a substantial proportion of the neurons. In this paper we rigorously analyze their model. In particular, by applying methods and tools that are frequently used in theoretical computer science, we provide a very precise picture of the dynamics and the evolution of the given system. In particular, we obtain insights into the coexistence of synchronous and asynchronous behavior and the conditions that trigger a "spontaneous" transition from one state to another.
Year
DOI
Venue
2010
10.5555/1873601.1873678
SODA
Keywords
Field
DocType
important phenomenon,discrete-state stochastic dynamical network,neuronal network,neural network,asynchronous behavior,key feature,synchronous behavior,neuronal system,neuronal behavior,key example,large network
Asynchronous communication,Synchronization,Computer science,Stochastic neural network,Idealization,Artificial intelligence,Phenomenon,Novelty,Artificial neural network,The Internet
Conference
Volume
ISBN
Citations 
135
978-0-89871-698-6
1
PageRank 
References 
Authors
0.38
3
4
Name
Order
Citations
PageRank
Fabian Kuhn12709150.17
Konstantinos Panagiotou229027.80
Joel Spencer350.86
Angelika Steger4995111.50