Human-like and neural computing
With human-like and neural computing we take inspiration from how humans solve information processing problems and try to make machines perform in a similar way.
Inspiration could come from human behaviour studies in psychology, or from neuroscience studies of how neurons process information at a lower level. For example, a human-like approach to video understanding can use interpretative models, where the computer can report why it recognised e.g. “handbag theft” in a given video. The computer would be able to identify the thief’s hand grabbing a bag, and who was the original owner of the bag, and would be able to explain why it is “theft” and not “giving” by pointing to the gaze direction of the actors.
In neuroscience-inspired studies, the behaviour of biological neural networks is simulated using computer models. Different methods exist to conduct such simulations, which also vary in their biological realism. For instance, spike-based neural networks have many benefits including energy-efficient and low-latency computation.
We are researching the question, how to create and develop functional spike-based neural networks, but the biological brain serves as an excellent inspiration. Along those lines, network cascades found in brain function (neuronal avalanches) provide a design paradigm for logic and learning under statistical learning models, such as the linear threshold model, which allow the study of spatial constraints and criticality to efficiency.
We are a founding member of the EPSRC Network+ on Human-Like Computing which is currently supporting the development of a UK-wide multi-disciplinary community of AI and cognitive science researchers.
Get in touch
Contact us at firstname.lastname@example.org if you'd like to find out more about our research in human-like and neural computing.