Our lab is focused on mapping, modeling and manipulating the human brain.
We develop systems and devices for understanding the underlying neurophysiological processes and conditions that spawn consciousness, cognition and communications/language. Our research focus is to leverage these computational and mathematical insights to improve diagnostic accuracy and treatment efficacy for a wide range of neurological disorders and neurodegenerative diseases.
Our diagnostic research centers around multimodal analysis and machine learning. We have developed a prototype system called the Brain Code Collection System (BCCS) that is able to effectively identify (and objectively quantify the state and progression of) many formerly-undiagnosable conditions, including Alzheimer’s Disease, Parkinson’s Disease, PTSD, TBI, neuropathic pain, dystonia, etc.
Our research on treatment modalities is focused on various methods and applications of Deep Brain Stimulation (DBS). Electrical, optogenetic and native optical methods of DBS are studied at CNL, with various methods of reactive stimulation and pattern application being tested as alternatives to the traditional approach of continuous stimulation.
DBS may be applied to different regions of the brain to restore both brain and bodily function and applications for this are also being researched. DBS within particular regions have been shown to be able to alter bladder function, cardiac function and many other systems yet to be mapped.
Our past work with this technique has successfully restored sight in blind mice, abetted chronic pain in humans and will hopefully one day be used to restore memories and enhance cognitive performance. Future work is to build a “pacemaker for the brain”.
Our lab was the first to identify an endogenous optical communications network in the human brain consisting of mitochondrial free radical production reacting with neuropsin (OPN5) in neural pathways. The existence of such a signaling network strongly suggests that information processing in the brain is not exclusively electrochemical, in nature. We are developing more effective ways to read and write to this neurophotonic communications layer and generate meaning from scan information. In partnership with industry, we are researching methods for replicating the behavior of this neurophotonic switch in silico to build faster, smaller and self-powered computing architectures.
Although the Computational Neuroscience Laboratory is relatively new at Oxford and Nuffield (being established in early 2015), our work extends many years of prior research at MIT within the Mind Machine Project and Synthetic Intelligence Lab. The theoretical foundation behind all of this work originates from a doctoral thesis proposed by Dr. Newton Howard on the Theory of Intention Awareness (IA). This theory has been patented, published and widely cited and has been applied in practice to redesign Command and Control (C3I) systems within the US Military.
IA was further extended for specific application within the field of Artificial Intelligence as the Intent-Centric Paradigm (ICP) while at MIT and later at Oxford became more focused on discrete elements of IA transfer, for application within the field of neurology.
This work spawned the Brain Code (BC) and Fundamental Code Unit (FCU) theories, both of which guide our present research by mapping patterns of the most fundamental neurological processes to higher-order cognitive, behavioral and linguistic function.
Oxford CNL is a highly multidisciplinary lab, consisting of neurologists, mathematicians, engineers, physicists, psychologists, philosophers and surgeons from around the world.