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

fYear

1989

Firstpage

849

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1989., IEEE International Symposium on

Conference_Location

Portland, OR

Type

conf

DOI

10.1109/ISCAS.1989.100484

Filename

100484

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1638757