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