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Use of recurrent infomax to improve the memory capability of input-driven recurrent neural networks

14 Feb 2018 • Hisashi Iwade • Kohei Nakajima • Takuma Tanaka • Toshio Aoyagi

The inherent transient dynamics of recurrent neural networks (RNNs) have been exploited as a computational resource in input-driven RNNs. However, the information processing capability varies from RNN to RNN, depending on their properties... (read more)

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Use of recurrent infomax to improve the memory capability of input-driven recurrent neural networks

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