In the fast-evolving landscape of medical science and technology, the intersection of neuroscience, engineering, and clinical medicine has given rise to unprecedented opportunities for understanding and treating neurological diseases. From August 6 to 8, 2023, the West China Hospital of Sichuan University, in collaboration with the National Clinical Research Center for Geriatric Diseases and the State Key Laboratory of Mechanical …
Geometric Deep Learning-Based Classifiers for Motor Imagery Classification using Electroencephalograms
Ce Ju and Cuntai Guan School of Computer Science and Engineering at Nanyang Technological University Since changes in sensorimotor rhythms (SMR) occurring within the sensorimotor areas of the brain during motor imagery (MI) can effectively function as control signals for electroencephalography (EEG)-based brain-computer interfaces (BCIs), MI-EEG classification has been a primary area of research in BCIs [1, 2]. During the …
A new paradigm for probabilistic neuromorphic programming
Since it was first introduced neuromorphic hardware has held the promise of capturing some of the efficiency of biological neural computation, which is 3- 6 orders of magnitude more efficient than engineering solutions. To attempt to capture this efficiency, one key feature of biological neural computation that has been replicated in current neuromorphic hardware is its event-based nature. Neural ‘spikes’ are used to transmit information both in neuromorphic hardware and many neurobiological systems to minimize long distance communication energy costs.
Restoring naturalistic speech communication using multimodal speech neuroprosthetics
Speech communication accounts for a significant amount of the social interactions in daily life. Neurological conditions like amyotrophic lateral sclerosis, stroke, brain tumor and traumatic brain injury can lead to the paralysis or impairment of the vocal structures responsible for speech production. For these patients, restoring speech functioning is one of the crucial components for their rehabilitation.
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
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.
Neuromorphic Model of Human Intelligence
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.
