This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Yamauchi, B. M. Articles by Beer, R. D. Search for Related Content Social Bookmarking                   What's this?

Sequential Behavior and Learning in Evolved Dynamical Neural Networks

Case Western Reserve University

Randall D. Beer

Case Western Reserve University

This article explores the use of a real-valued modular genetic algorithm to evolve continuous-time recurrent neural networks capable of sequential behavior and learning. We evolve networks that can generate a fixed sequence of outputs in response to an external trigger occurring at varying intervals of time. We also evolve networks that can learn to generate one of a set of possible sequences based on reinforcement from the environment. Finally, we utilize concepts from dynamical systems theory to understand the operation of some of these evolved networks. A novel feature of our approach is that we assume neither an a priori discretization of states or time nor an a priori learning algorithm that explicitly modifies network parameters during learning. Rather, we merely expose dynamical neural networks to tasks that require sequential behavior and learning and allow the genetic algorithm to evolve network dynamics capable of accomplishing these tasks.

Key Words: neural networks • genetic algorithms • sequential behavior; reinforcement learning

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?

This article has been cited by other articles:

				P. Phattanasri, H. J. Chiel, and R. D. Beer The Dynamics of Associative Learning in Evolved Model Circuits Adaptive Behavior, December 1, 2007; 15(4): 377 - 396. [Abstract] [PDF]

				J. Schmidhuber, D. Wierstra, M. Gagliolo, and F. Gomez Training recurrent networks by evolino. Neural Comput., March 1, 2007; 19(3): 757 - 779. [Abstract] [Full Text] [PDF]

				R. W. Paine and J. Tani How Hierarchical Control Self-organizes in Artificial Adaptive Systems Adaptive Behavior, September 1, 2005; 13(3): 211 - 225. [Abstract] [PDF]

				P. M. Todd and H. A. Yanco Environmental Effects on Minimal Behaviors in the Minimat World Adaptive Behavior, March 1, 1996; 4(3-4): 365 - 413. [Abstract] [PDF]

				R. D. Beer On the Dynamics of Small Continuous-Time Recurrent Neural Networks Adaptive Behavior, March 1, 1995; 3(4): 469 - 509. [Abstract] [PDF]