LI Xiaojian

LI Xiaojian

Ph.D Professor of Engineering
The key technology for brain research and brain-like intelligence
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Development of new instruments for neurophysiology research.
Development of neuromorphic neural interface for neural signal sensing, recording and neural modulation.
Exploration of the neural circuits of intelligence for inspiring brain-like intelligence.
Exploration of the algorithms and emulation devices for neuromorphic engineering.

Reverse-engineer the brain to advance artificial intelligence
For decades, engineers built many algorithms for machine intelligence, yet those algorithms each fell far short of human capabilities. In parallel, cognitive scientists and neuroscientists accumulated myriad measurements describing how the brain processes information. They measured how neurons inside a neural network respond to the surroundings. They proposed mathematical models of how neural networks might learn from experience. Yet, these approaches alone failed to uncover the brain’s algorithms for intelligence.
The key breakthrough came when researchers used a combination of science and engineering. Using algorithms inspired by the brain, engineers have completely reshaped technologies from the recognition of faces and objects and speech, to automated language translation, to autonomous driving, and many others. But this is just the beginning. Deep learning algorithms resulted from new understanding of just one layer of human intelligence—visual perception. There is no limit to what can be achieved from a deeper understanding of other layers of intelligence.
The approach of working backwards from measurements of the functioning system to engineer models of how that system works is called reverse engineering. Discovering how the human brain works in the language of engineers will not only lead to transformative A.I. It will also illuminate new approaches to helping those who are blind, deaf, autistic, schizophrenic, or who have learning disabilities or age-related memory loss. Armed with an engineering description of the brain, we will see new ways to repair, educate, and augment our own minds.


2018-present, Principal Investigator, SIAT, CAS.
2013-2018, Research Associate, Northwestern University, USA.
2010-2013, Postdoc Fellow, Medical College of Georgia, USA.
2010, Ph.D, Institute of Biophysics, CAS 2001, B.E, Northwest University.

Selected publications

1.Ledesma H, Li X, Carvalho-de-Souza J, Wei W, Bezanilla F, Tian B. An atlas of nano-enabled neural interfaces. Nature Nanotechnology (in minor revision)

2.Jiang Y*, Parameswaran R*,  Li X* , et al. Non-genetic optical neuromodulation with silicon-based materials.  Nature Protocols  (* co-first author, in press) 

3.Yamawaki N,  Li X , Lambot L, Ren L, Radulovic J, Shepherd GMG. Long-range inhibitory intersection of a retrosplenial thalamocortical circuit by apical tuft-targeting CA1 neurons. Nature Neuroscience  (in press) 

4. Li X , Yamawaki N, Barrett JM, Körding KP and Shepherd GMG. Scaling of Optogenetically Evoked Signaling in a Higher-Order Corticocortical Pathway in the Anesthetized Mouse.  Frontiers in Systems Neuroscience  (2018) 12:16.

5.Jiang Y*,  Li X* , Liu B*, et al. Rational design of silicon structures for optically-controlled multiscale biointerfaces.  Nature Biomedical Engineering  (2018) 2, 508-521 (* co-first author)