Paper Info
Title
Spike transmission on diverging/converging neural network and its implementation on a multilevel modeling platform
Abstract
A multiple layers neural network characterized by diverging/converging projections between successive layers activated by an external spatio-temporal input pattern in presence of stochastic background activities was considered. In the previous studies we reported the properties and performance of spike information transmission in the network depending on neuron model type, inputed information type and background activity level. The models were rather simple and can be more detailed and bigger in size for further investigation. Based on a technology developed in the integrated physiology, we have implemented the network model on PhysioDesigner, a platform for multilevel mathematical modeling of physiological systems. This article instructs a use case of PhysioDesigner and the assistive function of PhysioDesigner especially for large size neuronal network modeling is demonstrated.
Year
DOI
Venue
2012
10.1007/978-3-642-33269-2_35
ICANN (1)
Keywords
Field
DocType
multilevel mathematical modeling,spike transmission,inputed information type,network model,neural network,background activity level,neuron model type,large size neuronal network,stochastic background activity,spike information transmission,multilevel modeling platform,assistive function,multilevel modeling
Transmission (mechanics),Biological neuron model,Computer science,Multilevel model,Information transmission,Artificial intelligence,Artificial neural network,Biological neural network,Synfire chain,Machine learning,Network model
Conference
Citations 
PageRank 
References 
0
0.34
11
Authors
2
Name
Order
Citations
PageRank
Yoshiyuki Asai1307.56
Alessandro E . P. Villa234853.26