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A D Thieme

Publications and source records attributed to A D Thieme.

2 recordsLinked to original sources

Maps, routes, and the hippocampus: a neural network approach.

This study describes hippocampal participation in maze navigation in terms of a real-time, biologically plausible neural network. The system is composed of (1) a goal-seeking mechanism, (2) a cognitive map system, and (3) a route system. The goal-seeking mechanism displays exploratory behavior until either the goal is found or a sufficiently strong prediction of the goal is generated. The cognitive map is a topological map that stores associations between places and views of accessible places, and between places and reward. The route system establishes associations between cues and reward. Both systems compete with each other to establish associations with the reward, with the cognitive system generally overshadowing the route system. In agreement with previous models, it is assumed that the hippocampus modulates the storage of cognitive maps in cortical areas and mediates the competition between cognitive maps and route systems. After hippocampal lesions, animals navigate through mazes making use of the route system. Computer simulations show that the network effectively describes latent learning, detour behavior, and place learning in normal and hippocampal- and cortical-lesioned animals.

Animals↗

Purposive behavior and cognitive mapping: a neural network model.

This study presents a real-time, biologically plausible neural network approach to purposive behavior and cognitive mapping. The system is composed of (a) an action system, consisting of a goal-seeking neural mechanism controlled by a motivational system; and (b) a cognitive system, involving a neural cognitive map. The goal-seeking mechanism displays exploratory behavior until either (a) the goal is found or (b) an adequate prediction of the goal is generated. The cognitive map built by the network is a topological map, i.e., it represents only the adjacency, but not distances or directions, between places. The network has recurrent and non-recurrent properties that allow the reading of the cognitive map without modifying it. Two types of predictions are introduced: fast-time and real-time predictions. Fast-time predictions are produced in advance of what occurs in real time, when the information stored in the cognitive map is used to predict the remote future. Real-time predictions are generated simultaneously with the occurrence of environmental events, when the information stored in the cognitive map is being updated. Computer simulations show that the network successfully describes latent learning and detour behavior in rats. In addition, simulations demonstrate that the network can be applied to problem-solving paradigms such as the Tower of Hanoi puzzle.

Animals↗