The Super-Turing Computational Power of plastic Recurrent Neural Networks
By Jérémie Cabessa, Hava T. Siegelmann. 2014
58 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Kenneth O. Stanley
- Jeff Clune
- Sebastian Risi
- Andrea Soltoggio
- Joel Lehman
- Jean-Baptiste Mouret
- Risto Miikkulainen
- J. Urgen Schmidhuber
In Cognitive Neuroscience
- Sebastian Risi
- Kenneth O. Stanley
- Jeff Clune
- Andrea Soltoggio
- J. Krichmar
- Risto Miikkulainen
- Gido M. van de Ven
- Maxim Bazhenov
In Electrical and Electronic Engineering
- Kenneth O. Stanley
- Jeff Clune
- Sebastian Risi
- Andrea Soltoggio
- Risto Miikkulainen
- Nick Cheney
- Jean-Baptiste Mouret
- Joel Lehman
Citations in the corpus
- Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Plastic networks, in particular recurrent plastic networks, are known for their computational power, which has also been hypothesized to go beyond that of Turing Machines (Cabessa and Siegelmann, 2014): evolution can be a valuable tool to explore the power of those computational structures.”
- Biological underpinnings for lifelong learning machines (2022, background): “Because of their greater abilities and richer range of behaviours when deployed in the real world 221 , L2 systems have the potential to revolutionize many applications, including fully autonomous vehicles, smart cities and healthcare.”