Title :
A temporal memory network with state-dependent thresholds
Author :
Ghosh, Joydeep ; Wang, Shaoyun
Author_Institution :
Dept. of Electr. & Comput. Eng., Texas Univ., Austin, TX, USA
Abstract :
A fully connected recurrent network that is capable of storing, recalling, and generating a pattern sequence, is presented. This network reproduces a memorized sequence by synchronous updating, and can independently adjust the duration of occurrence of each pattern in the sequence. Such a capability is obtained by using a state dependent threshold for each cell (which reflects the characteristics of the neuron refractory period), and by the use of the hyperbolic tangent activation function rather than a hard limit. Computer simulations highlight the capabilities of the proposed architecture
Keywords :
pattern recognition; recurrent neural nets; temporal logic; fully connected recurrent network; hyperbolic tangent activation function; neuron refractory period; pattern sequence; state-dependent thresholds; synchronous updating; temporal memory network; Associative memory; Computer simulation; Contracts; Delay; Government; Limit-cycles; Neurons; Stochastic processes; Symmetric matrices; Very large scale integration;
Conference_Titel :
Neural Networks, 1993., IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0999-5
DOI :
10.1109/ICNN.1993.298583
