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Updated: Oct 11, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Towards the next generation of recurrent network models for cognitive neuroscience
Guangyu Robert Yang1, Manuel Molano-Mazón2
1Center for Theoretical Neuroscience, Columbia University, USA; Department of Brain and Cognitive Sciences, MIT, USA; Department of Electrical Engineering and Computer Science, MIT, USA.
Abstract:
Recurrent neural networks (RNNs) trained with machine learning techniques on cognitive tasks have become a widely accepted tool for neuroscientists. In this short opinion piece, we discuss fundamental challenges faced by the early work of this approach and recent steps to overcome such challenges and build next-generation RNN models for cognition. We propose several essential questions that practitioners of this approach should address to continue to build future generations of RNN models.
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