A detailed study of recurrent neural networks used to model tasks in the cerebral cortex

We studied the properties of simple recurrent neural networks trained to perform temporal tasks and also flow control tasks with temporal stimulus. We studied mainly three aspects: inner configuration sets, memory capacity with the scale of the models and finally immunity to induced damage on a trained network. Our results allow us to quantify different aspects of these models which are normally used as black boxes to model the biological response of cerebral cortex.