Modeling pavlovian conditioning with multiple neuronal populations

Artificial Neural Networks are often used as black boxes to implement behavioral functions, developed by trials and errors, fed with sensory inputs and controlled by some criteria of performance. This is the case for pavlovian conditioning where important sensory information is non ambiguous and where the error of prediction is to be minimized. These past years, taking into account critical conditioning behaviors entailed complexifying the neuronal functioning and learning rules. This resulted in networks still simple at the architectural level but with a dynamics difficult to master. Instead, we propose a new neuronal model using uniform and classical neuronal dynamics, with a more complex architecture based on recent findings in neuroscience. Results reported in this paper confirm the good behavior of the model and justify the complex architecture by the greater robustness and flexibility of the model.