Category Archive

Communicated by Dr. Jun Wang

RESEARCH

December 2021

Linran Zhao, Wen Li, Maysam Ghovanloo, Yaoyao Jia

There is an increasing realization that the majority of brain functions relate to a large distributed network of neurons that are spread over different interconnected regions of the brain. Thus, neural recording and modulation of the future will require the ability to simultaneously interface with multiple neural sites distributed over a large brain area. Traditional methods for modulating neuronal function have relied on direct stimulation by tiny electrodes, which effectiveness is undermined by the limited spatial and temporal precision with which individual cells can be selectively targeted. The emergence of optogenetic stimulation provides distinct advantages over electrical stimulation, such as cell-type specificity, sub-millisecond temporal precision, and rapid reversibility. Optogenetic neuromodulation has the potential to revolutionize the study of how neurons operate as members of larger networks and may ultimately help patients suffering from neurological disorders. Hence, we aspire to design a distributed wireless neural interface framework to stimulate large-scale neuronal ensembles over large brain areas. The distributed framework includes an array of tiny, wireless, and highly efficient implants, each of which operates autonomously to stimulate neural activities.

Communicated by Dr. Yiwen Wang

RESEARCH

December 2021

Leilei Gu, Yucheng Ding, Zhiyong Fan

“To see is to believe”. High-performance imaging devices are essential in society, particularly in the current age of Artificial Intelligence (AI) +. The biological eyes have been polished by natural selection for millions of years and their function has been verified by the diverse environment. Learning from the masterpiece of nature is therefore a shortcut to improve our manmade systems. As one of the wisest creatures in nature, human eyes are advanced image sensing systems with superiorities such as high resolution, wide field-of-view (FoV), high energy efficiency, and strong accommodations. Their high performance originates from the combined effect of a vastly flexible optical system, high-density and sensitive photoreceptor arrays, and powerful neural networks from both retina and cortex. The human eyes have a spherical shape with a hemispherical retina. A hemispherical shape matches well with the Petzval surface, which is the theoretical focal plane of the spherical lens, leading to clear and sharp imaging. In regular cameras, to mitigate the mismatching in planar structure, a delicate lens array has to be inserted to gradually bend the focal plane into quasi-flat. In our cell phones, there are 10-16 lens. With a well-designed hemispherical image sensor, high-quality imaging with a simple structure can be achieved.

Communicated by Dr. Yuxiao Yang

RESEARCH

December 2021

Shixian Wen, Laurent Itti

Lifelong learning challenges

The human brain can quickly learn and adapt its behavior in a wide range of environments throughout its lifetime. In contrast, deep neural networks only learn one sophisticated but fixed mapping from inputs to outputs.  In more complex and dynamic scenarios where the inputs to outputs mapping may change with different contexts, the deployment of these deep neural network systems would be constrained. One of the failed salient scenarios is lifelong learning—learning new independent tasks sequentially without forgetting previous tasks. More specifically, agents should incrementally learn and evolve based on multiple tasks from various data distributions across time while remembering previously learned knowledge. In general, current neural networks are not capable of lifelong learning and usually suffer from “catastrophic forgetting”—learning the knowledge of the new task would overwrite the fixed learned mapping of an old task. This effect typically leads to a significant decrease of the network performances on previous tasks or, in the worst case, leads to the network completely forgetting all previous tasks.

Communicated by Dr. Sung-Phil Kim

RESEARCH

December 2021

Shitao Chen, Songyi Zhang, Badong Chen and Nanning Zheng

As a typical artificial intelligence system, self-driving cars, unlike normal artificial intelligence systems, usually concern the safety of people’s lives and property, and have little tolerance of mistakes. With the furthering of research on self-driving technology, the existing computing framework based on the “perception-planning-decision-control” information processing method has increasingly manifested the problems of low computing efficiency, poor environmental adaptability, and insufficient self-learning ability. Our research work mainly refers to the psychological level of human cognition to construct a new type of self-driving method.

EVENT

December 2021

Cynthia Weber, PhD, on behalf of IEEE Brain

Guidelines that consider societal and cultural impacts of neurotechnology are crucial for ensuring responsible innovation in the field.

Ethical considerations have not always been of primary concern in the development of technology. However, the need for ethical standards and guidelines for neurotechnology has received significant support with multiple efforts underway that aim to sidestep past mistakes by preparing for future development and use cases. The challenge lies in identifying the complex social, legal, and cultural issues tied to how neurotechnologies will be accessed and implemented once released into the world, and the associated safety, privacy, and long-term consequences of its use. For many people, the brain is intimately connected to one’s sense of self and personal identity—our thoughts and emotions, for example. Consequently, neurotechnology devices that intervene with the brain, whether for medical treatment, wellness applications, or entertainment, may pose unique perceived risks for the user. This is also the case when neurotechnology has the potential to be implemented in employment, legal, or educational contexts. In all these scenarios, ethical considerations are interwoven within layers of consent, data access and control, and possible manipulation.