Multi-input silicon neuron with weighting adaptation

Author

Li, Ming-Ze ; Ping-Wang, Po ; Tang, Kea-Tiong ; Fang, Wai-Chi

Author_Institution

Dept. of Electr. Eng., Nat. Tsing Hua Univ., Hsinchu

fYear

2009

fDate

9-10 April 2009

Firstpage

194

Lastpage

197

Abstract

This paper presents a biologically inspired ldquointegrate-and-fire (I&F) neuronrdquo which has multiple input dendrites for adaptive weight storage. By using a capacitor-free integrator, longer time constant and smaller chip area can be achieved. A low-power Schmitt Trigger is used to implement the feedback loop to achieve smaller power consumption. Weights are stored by using floating gate MOS transistors as nonvolatile analog memory. Simulation results show that this I&F neuron can be utilized in an analog VLSI neural network system.

Keywords

MOSFET; VLSI; neural nets; neurophysiology; silicon; trigger circuits; adaptive weight storage; analog VLSI neural network system; biologically inspired integrate-and-fire neuron; capacitor-free integrator; feedback loop; floating gate MOS transistor; low-power Schmitt trigger; multi-input silicon neuron; multiple input dendrites; nonvolatile analog memory; Analog memory; Energy consumption; Feedback loop; MOSFETs; Neural networks; Neurons; Nonvolatile memory; Silicon; Trigger circuits; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Life Science Systems and Applications Workshop, 2009. LiSSA 2009. IEEE/NIH

Conference_Location

Bethesda, MD

Print_ISBN

978-1-4244-4292-8

Electronic_ISBN

978-1-4244-4293-5

Type

conf

DOI

10.1109/LISSA.2009.4906744

Filename

4906744