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Recurrent Neural Networks (RNNs) are frequently used to model aspects ofbrain function and structure. In this work, we trained small fully-connectedRNNs to perform temporal and flow control tasks with time-varying stimuli. Ourresults show that different RNNs can solve the same task by converging todifferent underlying dynamics and also how the performance gracefully degradesas either network size is decreased, interval duration is increased, orconnectivity damage is increased. For the considered tasks, we explored howrobust the network obtained after training can be according to taskparameterization. In the process, we developed a framework that can be usefulto parameterize other tasks of interest in computational neuroscience. Ourresults are useful to quantify different aspects of the models, which arenormally used as black boxes and need to be understood in order to model thebiological response of cerebral cortex areas.

Exploring weight initialization, diversity of solutions, and degradation in recurrent neural networks trained for temporal and decision-making tasks