Spintronic Nanodevices for Neuromorphic Sensing Chips

Recent developments in spintronics materials and physics are promising to develop a new type of magnetic sensors which can be embedded into the silicon chips. These neuromorphic sensing chips will be designed to capture the biomagnetic signals from active biological tissue exploited as brain-machine interface. They lead to machines that are able to sense and interact with the world in humanlike ways and able to accelerate years of fitful advance in artificial intelligence. To detect the weak biomagnetic signals, this work aims to develop a CMOS-compatible spintronic sensor based on the magnetoresistive (MR) effect. As an alternative to bulky superconducting quantum interference device (SQUID) systems, the miniaturised spintronic devices can be integrated with standard CMOS technologies makes it possible to detect weak biomagnetic signals with micron-sized, non-cooled and low-cost. Fig. 1 shows the finite element method (FEM)-based simulation results of a Tunnelling-Magnetoresistive (TMR) sensor with an optimal structure in COMSOL Multiphysics. The finest geometry and material are demonstrated and compared with the state-of-the-art. The proposed TMR sensor achieves a linear response with a high TMR ratio of 172% and sensitivity of 223 μV/Oe. The results are promising for utilizing the TMR sensors in future miniaturized brain-machine interface, such as Magnetoencephalography (MEG) systems for neuromorphic sensing

Poker as a Skill Game: Rational vs Irrational Behaviors

In many countries poker is one of the most popular card games. Although each variant of poker has its own rules, all involve the use of money to make the challenge meaningful. Nowadays, in the collective consciousness, some variants of poker are referred to as games of skill, others as gambling. A poker table can be viewed as a psychology lab, where human behavior can be observed and quantified. This work provides a preliminary analysis of the role of rationality in poker games, using a stylized version of Texas Hold'em. In particular, we compare the performance of two different kinds of players, i.e., rational vs irrational players, during a poker tournament. Results show that these behaviors (i.e., rationality and irrationality) affect both the outcomes of challenges and the way poker should be classified.Comment: 15 pages, 5 figure

Advanced Computing and Related Applications Leveraging Brain-inspired Spiking Neural Networks

In the rapid evolution of next-generation brain-inspired artificial intelligence and increasingly sophisticated electromagnetic environment, the most bionic characteristics and anti-interference performance of spiking neural networks show great potential in terms of computational speed, real-time information processing, and spatio-temporal information processing. Data processing. Spiking neural network is one of the cores of brain-like artificial intelligence, which realizes brain-like computing by simulating the structure and information transfer mode of biological neural networks. This paper summarizes the strengths, weaknesses and applicability of five neuronal models and analyzes the characteristics of five network topologies; then reviews the spiking neural network algorithms and summarizes the unsupervised learning algorithms based on synaptic plasticity rules and four types of supervised learning algorithms from the perspectives of unsupervised learning and supervised learning; finally focuses on the review of brain-like neuromorphic chips under research at home and abroad. This paper is intended to provide learning concepts and research orientations for the peers who are new to the research field of spiking neural networks through systematic summaries