NFR Project

  DeepCA - Hybrid Deep Learning Cellular Automata Reservoir

DeepCA is a long-term time horizon project seeking the integration of biological and artificial intelligence.



About
The ambitious research goal of the DeepCA project is to create a theoretical and experimental foundation for a novel hybrid deep learning paradigm based on cellular automata and biological neural networks, in order to bridge the gap between neuroscience and deep learning towards self-learning devices that are significantly more efficient than the state-of-the-art. The desired results have the potential for breakthroughs in novel substrates for machine learning (transferrable to hardware implementations), as well as direct medical applications.

Current deep learning implementations are not easily transferrable to hardware devices for widespread adoption, e.g., to sensor devices and Internet of Things, due to the required computing power and complexity of the underlying architectures. Therefore, a different computing paradigm is needed. Investigating biological neural cultures information processing and dynamics could lead to better, more powerful, energy efficient implementations of deep learning systems.

The main hypothesis in DeepCA is that deep architectures of cellular automata reservoir can significantly facilitate the implementation of recurrent neural networks and reservoir dynamics, in order to efficiently interface biological neural networks and implement learning through local interactions.

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Contact
Stefano Nichele, Project Manager

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DeepCa is partially funded by the Norwegian Research Council (NFR) through their Young Research Talent program under the project agreement 286558.