Neural networks and evolutionary computation. Part II: hybrid approaches in the neurosciences

Author

Weiß, Gerhard

Author_Institution

Inst. fur Inf., Tech. Univ. Munchen, Germany

fYear

1994

fDate

27-29 Jun 1994

Firstpage

273

Abstract

For pt. I, see ibid., p. 268. This paper focuses on the intersection of neural networks and evolutionary computation. It is addressed to researchers from artificial intelligence as well as the neurosciences. It provides an overview of hybrid work done in the neurosciences, and surveys neuroscientific theories that are bridging the gap between neural and evolutionary computation. According to these theories, evolutionary mechanisms like mutation and selection act in real brains in somatic time and are fundamental to learning and developmental processes in biological neural networks

Keywords

brain; brain models; genetic algorithms; learning (artificial intelligence); neural nets; neurophysiology; physiological models; stability; artificial intelligence; biological neural networks; brains; developmental processes; evolutionary computation; evolutionary learning circuits; evolutionary mechanisms; hybrid approaches; learning processes; mutation; neuronal group selection; neurosciences; pre-representations; selection; selective stabilization; somatic time; synapses; Artificial neural networks; Biological neural networks; Biology computing; Chemicals; Circuits; Evolutionary computation; Intelligent networks; Neural networks; Neurons; Pattern recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1899-4

Type

conf

DOI

10.1109/ICEC.1994.349940

Filename

349940