Frontier of Artificial Network
A Series of Invited Talks @ FAN Group, CityU

Biography: Adeel Razi is a Professor of Computational Neuroscience at the School of Psychological Sciences, Monash University Australia, and affiliated with the Turner Institute for Brain and Mental Health and Monash Data Futures Institute. He leads a highly cross-disciplinary laboratory performing research combining engineering, physics, and machine-learning approaches to answer questions that are motivated by and grounded in neurobiology. He develops statistical methods for analysing neuroimaging time series data with a special focus on state-space modelling, Bayesian statistics, and dynamical systems theory. He has made fundamental contributions to the development and application of Dynamic Causal Modelling, especially for resting-state functional MRI, which is a framework for in-vivo investigating of the function of the human brain. His research has implications for building new neuroscience-inspired artificial intelligence systems, treatment of brain diseases and development of new neuro-technologies. His work has been published in journals such as Nature, Nature Human Behaviour, Nature Mental Health, Nature Reviews Neuroscience, Neuron, Nature Communications, and Proceedings of the National Academy of Sciences, and has been featured in The Guardians, BBC, CNN, The Age, The Australian etc. He joined Monash after finishing his postdoctoral studies (2012-2018) at the Wellcome Centre for Human Neuroimaging, UCL, UK. He received the B.E. degree in Electrical Engineering, with a University Medal, from the N.E.D. University of Engineering & Technology in Pakistan, the M.Sc. degree in Communications Engineering from the University of Technology Aachen (RWTH), Germany, and the Ph.D. degree in Electrical Engineering from the University of New South Wales, Australia in 2012.

Abstract: Synthetic biological intelligence (SBI) provides a new way to study learning and adaptive intelligence by embedding living neuronal networks in closed-loop interactive environments. Recent studies show that neuronal cultures can self-organize, learn task-relevant behaviours, and adapt to sparse sensory feedback in real time. These systems offer a valuable contrast to conventional reinforcement learning, especially in terms of sample efficiency and adaptive flexibility. In this talk, I will introduce SBI platforms such as DishBrain as experimental testbeds for understanding the computational principles of biological learning and for developing brain-inspired artificial intelligence. I will discuss recent work that combines active inference, dynamical systems modelling, and experiment-informed generative models to characterize how neuronal cultures interact with virtual environments. These studies provide insights into how adaptive behaviour can emerge from sparse feedback, prediction, and self-organization in living systems. More broadly, SBI points to new directions for AI beyond scale-driven deep learning, including efficient learning, embodied intelligence, continual adaptation, and neuromorphic computation.