BrainInsight: 2022 - Issue 2

December 2022

RESEARCH

Weier Wan, Rajkumar Kubendran, Clemens Schaefer, S. Burc Eryilmaz, Wenqiang Zhang, Dabin Wu, Stephen Deiss, Priyanka Raina, He Qian, Bin Gao, Siddharth Joshi, Huaqiang Wu, H.-S. Philip Wong, Gert Cauwenberghs

AI-powered edge devices such as smart wearables, smart home appliances, and smart Internet-of-things (IoT) sensors are already pervasive in our lives. Yet, most of these devices are only smart when they are connected to the internet. Under limited battery capacity and cost budget, local chipsets inside these devices are only capable of relatively simple data processing, while the more computationally demanding AI tasks are offloaded to the remote cloud.

December 2022

RESEARCH

Anna W. Roe

Scientists and engineers have long drawn inspiration from the biological world to understand how architecture gives rise to function. To learn how to fly, study the architecture of bird and insect wings [1]. To build a master swimmer, study the architecture of fish and amphibian neuromuscular oscillators [2]. In the same vein, to understand intelligence, study the architecture of human and nonhuman primate brains. This last endeavor (Neuromorphics or Neuromorphic Computing), has generated ‘smart machines’ that can mimic perception and motor behavior, and have been modelled on the currency of brain function, neuronal spike firing [3,4]. Such approaches have driven the development of new computing architectures that overcome von Neumann bottlenecks, GPUs that accelerate via mass parallelism, in-memory processors, and implementation of attractor networks and finite state machines [5]. Year-by-year, we see accelerations in benchmark performance, expansion of hardware and software technology, and computational deep neural network sophistication [6]. However, despite these breathtaking advances, many of the basic functions of intelligent systems–rapid and efficient memory access, behaviorally targeted resource allocation, on-the-fly response to ever-changing contexts, and energy efficient computation–remain fundamentally out of reach.

December 2022

OPINION

Biqiu Tang, Hui Sun, Naici Liu, Youjin Zhao, Chengming Yang, Senhao Liu, Qiyong Gong, Wenjing Zhang, Su Lui

With the increase of the pace of life, work pressure, work-family conflict, social changes and emergencies, the prevalence of mental disorders are increasing, which contributed to a large proportion of the global disease burden. At present, the diagnosis and classifications of mental disorders in psychiatry has been relying on psychological and behavioral observations, while heterogeneity within psychiatric syndromes such as depression and psychosis in genetics, neurobiology and treatment outcomes was widely demonstrated in such way. When diagnostic labels do not map precisely onto either biology or treatment outcome, it is challenging to conduct translational neuroscience research to extend the understanding of pathogenesis and develop treatments that will target alterations in specific patients for personalized treatment. In addition, since current diagnosis requires that the defining behavioral features are already present, it is difficult to develop targeted prevention-based interventions.

December 2022

RESEARCH

Wei Liu, Bao-Liang Lu

Emotions, especially facial expressions, used to be thought of as universal all around the world: we would cry when we are sad, and we would smile when we are happy. However, you might have experienced that you do not laugh after hearing a foreign joke realizing that the joke has distinct cultural backgrounds. Emotions, therefore, seem to have both universal and culturally variable components. Understanding the relationship between cultures and emotions can help us know whether emotions affect physical health in the same way across various cultures and inform us about the effectiveness of mental health interventions for patients with different cultural backgrounds. In addition, from the aspect of affective computing, a deep comprehension of cultural influences on emotions can help us build emotion recognition models for generalizing to people around the world.

December 2022

RESEARCH

Ye Tian, Cunkai Zhou, Kuikui Zhang, Huiran Yang, Zhaohan Chen, Zhitao Zhou, Xiaoling Wei, Tiger H. Tao, Liuyang Sun

Implantable flexible neural probes have been demonstrated bridging the mechanical mismatch between invasive probes and brain tissues, minimizing footprint in brain, and chronic biocompatibility [1]. However, conventional needle-shaped flexible neural probes reported before have recording sites distributed vertically along a relatively narrow shank [2], which limits the lateral range in which the probes may record neural signals. Although designs with more probe shanks expand the lateral detectable range, the high implantation density reflects in increased tissue damage and surgery complexity. In this work, we developed a flexible neural probe by novel Christmas-tree structure, which has branches that are foldable along the shank by temporary encapsulation before implantation and self-stretchable after the encapsulation dissolves after implantation. The probe we developed affords increased lateral sensing range without causing extra brain tissue damage.