ISSN:
1614-7456
Keywords:
Hippocampal neural network
;
Hebbian learning
;
Phase coding
;
Self-orientation
;
Home-navigation
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract We propose an artificial neural network model for autonomous agents, i.e., mobile robots, to learn maps of environments and acquire the ability to perform home-navigation autonomously. The networks consists of two subnetworks, each of which has a similar structure with hippocampal lamellar neuronal circuits. Hebbian learning procedures self-organize the first subnetwork to output the distributed sinusoidal activity of the cells by accumulating motor information generated during movement, and the second subnetwork to output localized activity by prototyping sensory information. These patterns represent a homing vector providing the relative coordinates of the agent from a starting point, and a place code corresponding uniquely to a point of the environment. By attaching homing vectors to the sensor map, the homing vector is associated with the sensory stimuli. Then the agents can perform home-navigation autonomously by this association.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF02471169
