Title :
Self-trapping in an attractor neural network with nearest neighbor synapses mimics full connectivity
Author :
Pavloski, Raymond ; Karimi, Majid
Author_Institution :
Dept. of Psychol. & Phys., Indiana Univ., PA, USA
Abstract :
A means of providing the feedback necessary for an associative memory is suggested by self-trapping, the development of localization phenomena and order in coupled physical systems. Following the lead of Hopfield (1982, 1984) who exploited the formal analogy of a fully-connected ANN to an infinite ranged interaction Ising model, we have carried through a similar development to demonstrate that self-trapping networks (STNs) with only near-neighbor synapses develop attractor states through localization of a self-trapping input. The attractor states of the STN are the stored memories of this system, and are analogous to the magnetization developed in a self-trapping 1D Ising system. Post-synaptic potentials for each stored memory become trapped at non-zero valves and a sparsely-connected network evolves to the corresponding state. Both analytic and computational studies of the STN show that this model mimics a fully-connected ANN
Keywords :
Ising model; content-addressable storage; neural nets; probability; Ising system; associative memory; attractor neural network; attractor states; full connectivity; nearest neighbor synapses; probability; self-trapping networks; Artificial neural networks; Intelligent networks; Magnetic fields; Magnetization; Nearest neighbor searches; Neural networks; Neurons; Physics; Psychology; Temperature;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831586