Title :
A model of neural circuits for programmable VLSI implementation
Author :
Salam, Fathi M A
Author_Institution :
Dept. of Electr. Eng., Michigan State Univ., East Lansing, MI, USA
Abstract :
A new model for neural circuits is introduced which has qualitatively the same dynamic properties as gradient continuous-time feedback neural nets. This model (i) reduces the maximum number of connections to n(n+1)/2, (ii) does not suffer from the synaptic weight problem, i.e. the problem of implementing variable linear resistive elements in large scale, and (iii) is implementable via all MOS elements. Hence, it lends itself naturally to analog MOS VLSI implementation
Keywords :
MOS integrated circuits; VLSI; analogue computer circuits; circuit layout; linear integrated circuits; neural nets; MOS elements; analog MOS VLSI implementation; circuit architecture; dynamic properties; gradient continuous-time feedback neural nets; neural circuit model; programmable VLSI implementation; synaptic weight problem; variable linear resistive element implementation; Artificial neural networks; Biological system modeling; Feedback circuits; Integrated circuit interconnections; Large-scale systems; Neural engineering; Neural networks; Neurofeedback; Systems engineering and theory; Very large scale integration;
Conference_Titel :
Circuits and Systems, 1989., IEEE International Symposium on
Conference_Location :
Portland, OR
DOI :
10.1109/ISCAS.1989.100484
