Title :
Fractal neural nets with sparse coding and low firing rates
Author :
Kim, Eung-Soo ; Sano, Masaki ; Sawada, Yasuji
Author_Institution :
Res. Inst. of Electr. Commun., Tohoku Univ., Sendai, Japan
Abstract :
An associative memory with fractal connections was studied. The performance of fractal neural network and randomly connected network were compared for the random patterns and fractally localized patterns, respectively. Since fractal patterns have very low activity level, the encoding scheme is sparse. We show numerically that the fractal neural network has a higher capability than randomly connected neural network for fractal pattern, but not for random patterns.
Keywords :
associative processing; content-addressable storage; encoding; fractals; neural nets; associative memory; encoding; firing rates; fractal neural nets; fractal patterns; randomly connected network; sparse coding; Artificial neural networks; Associative memory; Biological information theory; Biological neural networks; Encoding; Fractals; Humans; Neural networks; Neurons; Recurrent neural networks;
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
DOI :
10.1109/IJCNN.1993.716967