Rebuilding the brain with neuromorphic computing: an interview with Oliver Rhodes

What can we learn from the brain about building better computers? Oliver Rhodes discusses how neuromorphic computing draws on the brain's architecture to develop new approaches to more efficient information processing. Can you give me an overview of your background and the research that you're involved with? Neuromorphic computing is quite a broad subject, which essentially looks to biology as inspiration to develop next-generation computing systems. We work off this principle: we know the brain is this really amazing computer, and it's able to do things that a lot of modern computing systems aren't able to do, and it also does them in an incredibly energy efficient way. We'd like to try to replicate that with some of the systems we build. In the context of the current climate around AI, while we've seen that it's made really big steps forward and is able to do really impressive things, there are certain things that it still can't do that the brain is able to do. So we look to the brain for inspiration to try and solve some of those next generation challenges. Neuromorphic computing covers all aspects of this: from looking at algorithms that might be solving a particular problem, to the systems and subsystems that would be running those algorithms, right down to a chip or devices level. We often use neural networks, which are also employed in artificial intelligence models, such as in deep learning and transformer models.