Paper Info
Title
Synaptic model for spontaneous activity in developing networks.
Abstract
Spontaneous rhythmic activity occurs in many developing neural networks. The activity in these hyperexcitable networks is comprised of recurring ''episodes'' consisting of ''cycles'' of high activity that alternate with ''silent phases'' with little or no activity. We introduce a new model of synaptic dynamics that takes into account that only a fraction of the vesicles stored in a synaptic terminal is readily available for release. We show that our model can reproduce spontaneous rhythmic activity with the same general features as observed in experiments, including a positive correlation between episode length and length of the preceding silent phase.
Year
DOI
Venue
2005
10.1016/j.neucom.2004.10.074
Neurocomputing
Keywords
Field
DocType
neural network,silent phase,synaptic model,preceding silent phase,high activity,episode length,spontaneous activity,synaptic terminal,new model,spontaneous rhythmic activity,hyperexcitable network,synaptic vesicle pools,synapse model,synaptic dynamic,synaptic vesicle
Synapse,Synaptic terminal,Synaptic augmentation,Artificial intelligence,Positive correlation,Artificial neural network,Rhythm,Machine learning,Mathematics
Journal
Volume
ISSN
Citations 
65-66
Neurocomputing
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Alexander Lerchner1152.14
John Rinzel2459219.68