Shiwei Wang

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What is your research focus? 

There are over 86 billion neurons in the brain, and it would be impossible to fully understand the fundamental mechanisms of human neurophysiology and the way we think, and decode cognitive functions without accessing a large number of them. Unfortunately, the number of neurons that can be accessed today is still limited to only a few thousand, representing fewer than 1 out of 10 million of the total neuron population. A key limiting factor to this is the footprints of implantable devices. While today’s brain electrodes can be made even smaller than a neuron (down to nanometres), the interface electronics required to read out and process neural signals are significantly bigger and consume much higher energy.  

What is your innovation idea? 

My vision is to address these issues through innovations in chip technologies that interface with the brain. A short-term target is to enable technologies for accessing one million neurons in an implantable device which will unleash the true ‘internet of neurons’ – imagine a device like the Neuralink but with the capability to link a thousand times more neurons to a mobile phone. 

What impact will it have on the world?  

These innovations will tackle unmet medical needs in brain healthcare with more reliable brain reading and deeper insights into the basis of neurological and psychiatric conditions. 

What is the future of your research?

A fundamental limit of existing neural interface electronics is that they do not speak the same language as the natural neural systems. A new method to process and encode neural signals has been developed by the Centre for Electronics Frontiers (University of Edinburgh School of Engineering) that uses a novel nanoelectronics device (‘memristor’) to interpret and to memorise useful signals. This new device can be integrated with conventional chip technologies making it possible to target neural interface chips at million-channel scale. From a systems perspective, our approach is creating a direct shortcut from the natural neural systems to modern computing systems that are driven by the need in accelerating machine learning and neural network computation.  

What motivated you to apply for the Bayes Innovation Fellows?

My goal is to spin out a start-up company focusing on ultra-large-scale neural interface electronics products. By unifying the ‘language’ at the very front-end of neural interfaces, we are creating a true 'internet of neurons' spanning across both the natural and artificial neural networks.