DocumentCode
288461
Title
Use of a neural field model to derive equilibrium values for the weights of recurrent synapses
Author
Roque-da-Silva-Filho, Antônio C.
Author_Institution
Dept. de Geol., Sao Paulo Univ., Brazil
Volume
2
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
996
Abstract
Amari´s field-theoretic model to neural networks is used in a situation in which the weights of the recurrent synapses vary with time according to a Hebbian law. Assuming equilibrium, an equation relating the weight of a recurrent synapse between two neurons to the area of the overlap between the receptive fields of the two neurons is derived. When all receptive fields are circular and have the same radius an equation is derived for the spatial variation of the weights of the recurrent synapses made by a neuron, showing a decay with distance
Keywords
Hebbian learning; recurrent neural nets; Hebbian law.; equilibrium values; field-theoretic model; neural field model; neural networks; receptive fields; recurrent synapses; spatial variation; Artificial neural networks; Biomembranes; Computational Intelligence Society; Electronic mail; Geology; Integral equations; Neural networks; Neurons; Probability distribution; Recurrent neural networks;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374318
Filename
374318
Link To Document