Title :
Neural networks and evolutionary computation. Part II: hybrid approaches in the neurosciences
Author_Institution :
Inst. fur Inf., Tech. Univ. Munchen, Germany
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;
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
DOI :
10.1109/ICEC.1994.349940
