Title :
A spiking recurrent neural network
Author :
Li, Yuan ; Harris, John G.
Author_Institution :
Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL, USA
Abstract :
A spiking recurrent neural network implementing an associative memory is proposed. The circuit including four integrate-and-fire (IF) and Willshaw-type binary synapses is designed with the AMI 0.5μm CMOS process. A large-scale network is simulated with Matlab and its storage capacity is calculated and analyzed.
Keywords :
CMOS integrated circuits; associative processing; content-addressable storage; recurrent neural nets; 0.5 microns; AMI; CMOS; IF; Matlab; Willshaw-type binary synapses; associative memory; integrate-and-fire; large-scale network; neural network; spiking recurrent; storage capacity; Associative memory; Circuit simulation; Mathematical model; Neural networks; Neurons; Noise robustness; Recurrent neural networks; Semiconductor device modeling; Timing; Very large scale integration;
Conference_Titel :
VLSI, 2004. Proceedings. IEEE Computer society Annual Symposium on
Print_ISBN :
0-7695-2097-9
DOI :
10.1109/ISVLSI.2004.1339571
