Title :
A spike-based adaptive filter
Author :
Gong, Xiaoxiang ; Harris, John G.
Author_Institution :
Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL, USA
Abstract :
We propose a spike-based adaptive filter with supervised learning. Unlike standard adaptive filters, here the optimal MSE solution is not unique for the spike-based system identification problem. The simplex method is introduced to select one of the many possible optimal solutions. In simulations, an LMS-based learning procedure is designed and, for faster convergence, we introduce a credit assignment method which penalizes all the weights contributing to the current error signal. Finally, we discuss issues regarding the implementation of the spike-based adaptive filter in an analog VLSI circuit.
Keywords :
VLSI; adaptive filters; analogue integrated circuits; convergence; integrating circuits; learning (artificial intelligence); least mean squares methods; linear programming; pulse circuits; LMS-based learning procedure; MSE; analog VLSI implementation; convergence; credit assignment method; integrate-and-fire mechanism; linear programming; simplex method; spike timing; spike-based adaptive filter; supervised learning; system identification; Adaptive filters; Biological information theory; Circuit simulation; Laboratories; Neural engineering; Signal design; Supervised learning; System identification; Timing; Very large scale integration;
Conference_Titel :
Electronics, Circuits and Systems, 2004. ICECS 2004. Proceedings of the 2004 11th IEEE International Conference on
Print_ISBN :
0-7803-8715-5
DOI :
10.1109/ICECS.2004.1399683
