The Role of the Size Maze and Learning Parameters in the Prefrontal Cortex Modeling Based in Minicolumns

Learning pathways in spatial navigation has been a subject of the literature in the last decade, one must bear about decision making and situation management. Column models were characterized few years ago and current implementations of the prefrontal brain cortex (PFC) simulated the rat behavior in a 3x3 maze given a Goal-Driven task. In this work, the simulation was adapted to study learning variables and goal task processing. The model was adapted to study different situations such a (1) 'µ' parameter value (for learning enhancement or degeneration) and different limits between a half and the entire amplitude of the threshold parameter, and (2) size of the maze (3x3, 3x4, 3x6 and 3x8 in tabulated simulations) related with the initial position of the rat and the goal condition (reward position). The initially position did not increment the average number of step to learn the way, but the when vertical size was increased to more than 4/3 the horizontal maze size, the number of steps was increased to learn the optimal pathway to reach to reward. Then, the larger size maze the more difficult to the PFC model to learn the optimal pathway and this was discussed in the current view of the entorhinal cortex and how this model process a different number of goals for a Goal-Driven task, especially considering modelling of adquisition and learning variables in the minicolumn model. A short discussion is extended about studies of situation management.