 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.
Recurrent Neural Networks or RNN are frequently used to model different aspects of brain regions. We studied the properties of RNN trained to perform temporal and flow control tasks with temporal stimuli. We present the results regarding three aspects: inner configuration sets, memory capacity with the scale and immunity to induced damage on trained networks . Our results allow us to quantify different aspects of these physical models, which are normally used as black boxes and must be understood previous to modeling the biological response of cerebral cortex.
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Recurrent Neural Networks or RNN are frequently used to model different aspects of brain regions.
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simple recurrent neural networks
RNN
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tasks and also
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stimulus. We studied mainly
stimuli. We present the results regarding
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of the models and finally
and
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a trained network
trained networks
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models
physical models,
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to model
and must be understood previous to modeling