On-chip seizure detectors that learn on their own

Adelson Chua and Rikky Muller Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley Epilepsy affects about 50 million people worldwide and is characterized by recurring seizures that can lead to involuntary movements, loss of consciousness, and even death. Several technology-based solutions for monitoring and treating epilepsy have emerged in recent years involving wearable and implantable …

A new paradigm for probabilistic neuromorphic programming

P. Michael Furlong1* and Chris Eliasmith1 1Centre for Theoretical Neuroscience, University of Waterloo, 200 University Ave., Waterloo, N2L 3G1, Ontario, Canada.   *Corresponding author(s). E-mail(s): michael.furlong@uwaterloo.ca; Contributing authors: celiasmith@uwaterloo.ca;   Keywords: probability, Bayesian modelling, vector symbolic architecture, fractional binding, spatial semantic pointers   1     Introduction Since it was first introduced neuromorphic hardware has held the promise of capturing some of …

Direct-Digitization Neural Readouts for Fully-Integrated and High-Density Neural Recording

Monitoring large groups of neurons in various brain regions, including superficial and deep structures, is crucial for advancing neuroscience research on cognition, motor control, behavior, among other areas [1], [2]. Current extracellular CMOS high-density neural probes are becoming the new standard in electrophysiology, allowing for simultaneous recording with excellent spatial and temporal resolution [3]–[5]. However, there is still a demand for neural recording technologies that can access a significantly larger number of neurons, allowing for the decoding of more complex motor, sensory, and cognitive tasks. To achieve this, it is necessary to develop neural probes with much higher number of channels, which requires the design of readout circuits that meet several requirements, including: i) area- and power-efficiency, ii) low noise to capture weak neural signals, iii) capability to interface with large-impedance and high-DC-offset electrodes, and iv) tolerance to artifacts caused by movement or concurrent electrical stimulation.

An Interview with the 2022 SSCS-Brain Joint society best paper award winner

In 2022, Prof. Mahsa Shoaran together with Uisub Shin, Laxmeesha Somappa, Cong Ding, Yashwanth Vyza, Bingzhao Zhu, Alix Trouillet, Stéphanie P. Lacour from EPFL won the SSCS-Brain joint society best paper award. In an interview with the winners of the award, the IEEE Brain Magazine discussed more potential impact of their work.

NeuRRAM: RRAM Compute-In-Memory Chip for Efficient, Versatile, and Accurate AI Inference

December 2022


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.

Neuromorphic Model of Human Intelligence

December 2022


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.

Progress of Neuroimaging in Psychiatry

December 2022


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.

An affective computing aspect on similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French people

December 2022


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.

Self-stretchable Christmas-tree-shaped Ultraflexible Neural Probes

December 2022


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.

Towards the Design of BCI-based Accelerated Training System for Air Traffic Controllers

Communicated by Distinguished Professor Chin-Teng Lin 


May 2022

Chin-Teng Lin and Alka Rachel John

Humans are easily overwhelmed with tasks that push them beyond their capabilities. Despite their remarkable resilience to diverse working conditions, the work environment must be adapted to afford comfortable interactions with human operator abilities. Modern work environments position human operators at a supervisory level where they have extensive interactions with technology and must integrate multiple streams of information, demanding more cognitive resources and resulting in a higher workload in the human operators.