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2012, Número 1

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Rev Mex Ing Biomed 2012; 33 (1)


Red neural artificial del sistema mesolímbico-cortical que simula el aprendizaje discriminativo y de inversión

Guevara MA, Hernández GM, Olvera CME, Robles AFA

Idioma: Español
Referencias bibliográficas: 33
Paginas: 8-16
Archivo PDF: 182.29 Kb.


RESUMEN

En el presente estudio se desarrolló un modelo conexionista de aprendizaje no supervisado para simular una tarea de discriminación en un número reducido de iteraciones sin entrenamiento previo a la tarea. La red neural artificial consideró características neurofisiológicas de algunas de las estructuras del sistema dopaminérgico mesolímbico amígdala (AMG), corteza orbitofrontal (COF), área tegmental ventral (ATV) y núcleo accumbens (ACC). El modelo generó respuestas similares a las emitidas por ratas macho durante una tarea de discriminación e inversión en un laberinto T, usando como recompensa un incentivo sexual. En la actividad de las estructuras simuladas, se hallaron los fenómenos de preferencia de reforzador e inversión de la preferencia durante la inversión del incentivo en ACC y ATV; una codificación temprana en AMG; además de una codificación retardada y aumento en el reclutamiento de nodos neurales en la COF ante la inversión. Por último, las estructuras de salida mostraron una actividad de expectativa anterior a la entrega del reforzador.


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