DeepCA is a long-term time horizon project seeking the integration of biological and artificial intelligence. 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.
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.
By creating a theoretical and experimental foundation for a novel hybrid deep learning paradigm based on cellular automata and biological neural networks, the goal is 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.