Title :
Stability and discriminative properties of the AMI model
Author :
Hoang, D.B. ; James, M.
Author_Institution :
Dept. of Comput. Sci., Sydney Univ., NSW, Australia
Abstract :
We consider a basic biologically plausible neural circuit that employs supragranular self-gain, negative feedback via inhibitory infragranular neuron. Such circuitry has been used as fundamental building blocks in the AMI (a model of intelligence) modular neural network. We derive the conditions for stability of an adaptive model of such a circuit with nonlinear self-gain and nonlinear adaptation characteristics. We also present the simulation results which demonstrate the discriminative property of the discriminative compartment of an AMI module
Keywords :
adaptive systems; associative processing; circuit feedback; circuit stability; learning (artificial intelligence); neural nets; AMI model; adaptive model; associative compartment; atomic intelligent module; discriminative compartment; inhibitory infragranular neuron; learning algorithm; modular neural networks; negative feedback; nonlinear adaptation; nonlinear self-gain; stability; supragranular self-gain; Ambient intelligence; Biological system modeling; Circuit simulation; Circuit stability; Feedback circuits; Intelligent networks; Intelligent structures; Negative feedback; Neural networks; Neurons;
Conference_Titel :
Neural Networks,1997., International Conference on
Conference_Location :
Houston, TX
Print_ISBN :
0-7803-4122-8
DOI :
10.1109/ICNN.1997.611677
