Title :
A neural network-based associative memory for storing complex-valued patterns
Author :
Chakravarthy, Srinivasa V. ; Ghosh, Joydeep
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA
Abstract :
A neural network-based associative memory for storing complex patterns is proposed. Two variations of the model are proposed: 1) a discrete model, and 2) a continuous model. The latter approaches the former as a limit. A crude capacity estimate for the discrete model is made. Network weights can be calculated in one step using a complex outer-product rule or can be adjusted adaptively using a Hebbian learning rule. Possible biological significance of the complex neuron state is briefly discussed
Keywords :
Hebbian learning; adaptive systems; associative processing; content-addressable storage; neural nets; Hebbian learning rule; associative memory; complex-valued patterns storage; continuous model; discrete model; network weights; neural network; Artificial neural networks; Associative memory; Biological system modeling; Biology computing; Computer networks; Hebbian theory; Neural networks; Neurons; Stability criteria;
Conference_Titel :
Systems, Man, and Cybernetics, 1994. Humans, Information and Technology., 1994 IEEE International Conference on
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-2129-4
DOI :
10.1109/ICSMC.1994.400193
