DocumentCode
1346749
Title
Logic operations based on single neuron rational model
Author
Zhang, Chang Nian ; Zhao, Ming ; Wang, Meng
Author_Institution
Dept. of Comput. Sci., Regina Univ., Sask., Canada
Volume
11
Issue
3
fYear
2000
fDate
5/1/2000 12:00:00 AM
Firstpage
739
Lastpage
747
Abstract
This paper focuses on phase analysis to explore the single neuron local arithmetic and logic operations on their input conductances. Based on the analysis of the rational function model of local spatial summation with the equivalent circuits for steady-state membrane potentials, the prototypes spatial summation with the equivalent circuits for steady-state membrane potentials, the prototypes of logic operations are constructed. A mapping from a partition of input conductance space into functionally distinct phases is described and the multiple mode models for logic operations are established. The transitions from output voltage to input conductance in logic operations are also discussed for the connections between neurons in different layers. Our theoretical studies and software simulations indicate that the single neuron local rational logic is programmable and the selection of these functional phases can be effectively instructed by presynaptic activities. This programmability makes the single neuron more flexible in processing the input information
Keywords
digital arithmetic; multilayer perceptrons; neural chips; rational functions; functional phases; functionally distinct phases; input conductance space partition; input conductances; local rational logic; local spatial summation; logic operations; phase analysis; presynaptic activities; programmability; programmable logic; prototype spatial summation; rational function model; single neuron local arithmetic operations; single neuron rational model; software simulations; steady-state membrane potentials; Artificial neural networks; Biological neural networks; Biological system modeling; Biology computing; Biomembranes; Computational modeling; Equivalent circuits; Logic; Neurons; Prototypes;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.846745
Filename
846745
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