A
Neural Architecture for Rule-guided Behavior: A Simulation of Physiological
Experiments
T. Minami & T. Inui |
The
ability to use abstract rules is very important in applying our experience
to new situations. To examine the system governing such rule-guided
behavior, we developed a recurrent neural network model of rule-guided
behavior and simulated a physiological experiment involving a rule-guided
delayed task (Wallis, Anderson, & Miller, 2001). Our model was
constructed using neural system identification (Zipser, 1992), and
a fully recurrent neural network model was optimized to perform a
rule-guided delayed task. The model's hidden layer contained rule-selective
units, and an examination of connection weights substantiated the
postulate that rule-selective neurons maintain encoded rule information
and contribute to rule-guided responses indirectly. The simulation
results predicted functional interactions among neurons involved
in various task-related activities. The similarity between the behavior
of the model units and biological neurons shows that the brain uses
mechanisms like those of the model, and that ample mutual connections
in the prefrontal cortex are the basis for promoting flexible learning. Key words: rule-guided behavior, recurrent network, prefrontal cortex, working memory |