Red neural artificial del sistema mesolímbico-cortical que simula el aprendizaje discriminativo y de inversión
The present study develops a connectionist neural network with unsu pervised learning rules to simulate a discrimination task in a reduced number of time steps without previous training. The design of the network took into account some neurophysiological findings of dopaminergic mesolimbic system from structures like amygdala (AMG), orbitofrontal cortex (COF), ventral tegmental area (ATV) and nucleus accumbens (ACC). The proposed model generated similar responses to those from male rats during a discrimination and reversal learning tasks in a T maze, using sex as reward. In the activity of simulated structures different phenomena were found, like reinforcement preference and its reversal during reversal learning phase in ACC and ATV. It was also found an early encode in AMG, besides a retarded encoding and an increase in recruitment of neural nodes in COF during reversal learning. All output structures showed an expectancy activity before reinforcer delivery.
Copyright (c) 2012 Revista Mexicana de Ingenería Biomédica
This work is licensed under a Creative Commons Attribution 4.0 International License.
Upon acceptance of an article in the RMIB, corresponding authors will be asked to fulfill and sign the copyright and the journal publishing agreement, which will allow the RMIB authorization to publish this document in any media without limitations and without any cost. Authors may reuse parts of the paper in other documents and reproduce part or all of it for their personal use as long as a bibliographic reference is made to the RMIB and a copy of the reference is sent. However written permission of the Publisher is required for resale or distribution outside the corresponding author institution and for all other derivative works, including compilations and translations.