TY - GEN
T1 - Synchrony and asynchrony in neural networks
AU - Kuhn, Fabian
AU - Panagiotou, Konstantinos
AU - Spencer, Joel
AU - Steger, Angelika
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
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U2 - 10.1137/1.9781611973075.77
DO - 10.1137/1.9781611973075.77
M3 - Conference contribution
AN - SCOPUS:77951673448
SN - 9780898717013
T3 - Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms
SP - 949
EP - 964
BT - Proceedings of the 21st Annual ACM-SIAM Symposium on Discrete Algorithms
PB - Association for Computing Machinery (ACM)
T2 - 21st Annual ACM-SIAM Symposium on Discrete Algorithms
Y2 - 17 January 2010 through 19 January 2010
ER -