Paper Info
Title
Results on a binding neuron model and their implications for modified hourglass model for neuronal network.
Abstract
The classical models of single neuron like Hodgkin-Huxley point neuron or leaky integrate and fire neuron assume the influence of postsynaptic potentials to last till the neuron fires. Vidybida (2008) in a refreshing departure has proposed models for binding neurons in which the trace of an input is remembered only for a finite fixed period of time after which it is forgotten. The binding neurons conform to the behaviour of real neurons and are applicable in constructing fast recurrent networks for computer modeling. This paper develops explicitly several useful results for a binding neuron like the firing time distribution and other statistical characteristics. We also discuss the applicability of the developed results in constructing a modified hourglass network model in which there are interconnected neurons with excitatory as well as inhibitory inputs. Limited simulation results of the hourglass network are presented.
Year
DOI
Venue
2013
10.1155/2013/374878
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Keywords
Field
DocType
algorithms,probability,poisson distribution,membrane potentials,computer simulation,synapses
Neuroscience,Synapse,Biological neuron model,Binding neuron,Biological system,Computer science,Artificial intelligence,Spiking neural network,Winner-take-all,Hourglass,Biological neural network,Machine learning,Network model
Journal
Volume
ISSN
Citations 
2013
1748-670X
0
PageRank 
References 
Authors
0.34
3
3
Name
Order
Citations
PageRank
Viswanathan Arunachalam1172.78
Raha Akhavan-Tabatabaei26611.78
Cristina Lopez300.68