The continuous development of artificial intelligence has a profound impact on biomedical research and other fields.Brain-inspired computing is an important intersection of multimodal technology and biomedical field. This paper presents a comprehensive review of machine learning (ML) and deep learning (DL) models applied in human-computer interaction for brain-inspired computing, tracking their evolution, application value, challenges, and potential research trajectories. First, the basic concepts and development history are reviewed, and their evolution is divided into two stages: recent machine learning and current deep learning, emphasizing the importance of each stage in the research state of human-computer interaction for brain-inspired computing. In addition, the latest progress and key techniques of deep learning in different tasks of human-computer interaction for brain-inspired computing are introduced from six perspectives. Despite significant progress, challenges remain in making full use of its capabilities. This paper aims to provide a comprehensive review of human-computer interaction for brain-inspired computing models based on machine learning and deep learning, highlighting their potential in various applications and providing a valuable reference for future academic research. It can be accessed through the following url: https://github.com/ultracoolHub/brain-inspired-computing

Today is your EE Times Weekly Briefing for the week ending May 24. The biggest story in electronics this week affects almost everyone in the high-tech industry – from Huawei to Google to Infineon – to chip companies, circuit board suppliers – essentially the entire supply chain. The Trump Administration this week took steps that will certainly isolate Huawei and possibly cripple it. What was once delicately framed as a "trade tension" between the two nations has officially turned into an all-out-trade war, affecting not just the electronics industry, but nearly every other commercial segment around the world – from farming to aeronautics. This past week EE Times launched a Special Project that zeroed in on damage done during the trade conflict already.

A research team, led by Assistant Professor Desmond Loke from the Singapore University of Technology and Design (SUTD), has developed a new type of artificial synapse based on two-dimensional (2D) materials for highly scalable brain-inspired computing. Brain-inspired computing, which mimics how the human brain functions, has drawn significant scientific attention because of its uses in artificial intelligence functions and low energy consumption. For brain-inspired computing to work, synapses remembering the connections between two neurons are necessary, like human brains. In developing brains, synapses can be grouped into functional synapses and silent synapses. For functional synapses, the synapses are active, while for silent synapses, the synapses are inactive under normal conditions.

A lab, whose work is concentrated on neuromorphic computing or brain-inspired computing, has new research that introduces hardware improvements by harnessing a quality known as 'randomness' or 'stochasticity'. Their research contradicts the perception of randomness as a quality that will negatively impact computation results and demonstrates the utilization of finely controlled stochastic features in semiconductor devices to improve performing optimization. This has potential to create a more sophisticated building block for creating computers that can tackle sophisticated optimization problems, which can potentially be more efficient. What's more they can consume less power.

New computing technologies inspired by the brain promise fundamentally different ways to process information with extreme energy efficiency and the ability to handle the avalanche of unstructured and noisy data that we are generating at an ever-increasing rate. To realise this promise requires a brave and coordinated plan to bring together disparate research communities and to provide them with the funding, focus and support needed. We have done this in the past with digital technologies; we are in the process of doing it with quantum technologies; can we now do it for brain-inspired computing?

The next generation of high-performance, low-power computer systems might be inspired by the brain. However, as designers move away from conventional computer technology towards brain-inspired (neuromorphic) systems, they must also move away from the established formal hierarchy that underpins conventional machines -- that is, the abstract framework that broadly defines how software is processed by a digital computer and converted into operations that run on the machine's hardware. This hierarchy has helped enable the rapid growth in computer performance. Writing in Nature, Zhang et al.1 define a new hierarchy that formalizes the requirements of algorithms and their implementation on a range of neuromorphic systems, thereby laying the foundations for a structured approach to research in which algorithms and hardware for brain-inspired computers can be designed separately. The performance of conventional digital computers has improved over the past 50 years in accordance with Moore's law, which states that technical advances will enable integrated circuits (microchips) to double their resources approximately every 18–24 months.

T. Poggio observes, analyzes and predicts the evolution of deep learning from both mathematical and biological sides(which is the focus in our article) in [1]"Deep learning: mathematics and neuroscience". He mentions that, "it is telling that several of the algorithmic tricks that were touted as breakthroughs just a couple of years ago are now regarded as unnecessary ", while " some of the other ideas " such as residual learning " are more fundamental" "and likely to be more durable, though their exact form is bound to change somewhat " . In a word, he predicts that residual learning is a more durable component within the evolution of deep learning.

Scientists at IBM Research – Almaden have demonstrated that the TrueNorth brain-inspired computer chip, with its 1 million neurons and 256 million synapses, can efficiently implement inference with deep networks that approach state-of-the-art classification accuracy on several vision and speech datasets. The essence of the innovation was a new algorithm for training deep networks to run efficiently on a neuromorphic architecture, such as TrueNorth, by using 1-bit neural spikes, low-precision synapses, and constrained block-wise connectivity--a task that was previously thought to be difficult, if not, impossible. "The goal of brain-inspired computing is to deliver a scalable neural network substrate while approaching fundamental limits of time, space, and energy," said IBM Fellow Dharmendra Modha, chief scientist, Brain-inspired Computing, IBM Research. Today, the TrueNorth development ecosystem includes not only the TrueNorth brain-inspired processor, the novel algorithm for training deep networks and the scaled-up NS16e System but also a simulator, a programming language, an integrated programming environment, a library of algorithms and applications, firmware, a teaching curriculum, single-chip boards, and scaled-out systems.

Scientists at IBM Research – Almaden have demonstrated that the TrueNorth brain-inspired computer chip, with its 1 million neurons and 256 million synapses, can efficiently implement inference with deep networks that approach state-of-the-art classification accuracy on several vision and speech datasets. This will open up the possibilities of embedding intelligence in the entire computing stack from the Internet of Things, to smartphones, to robotics, to cars, to cloud computing, and even supercomputing. The novel architecture of the TrueNorth processor can classify image data at between 1,200 and 2,600 frames per second while using a mere 25 to 275 mW, which is effectively greater than 6,000 fps per Watt. Like that kung fu master in the movies who simultaneously fights assaults from many opponents, this processor can detect patterns in real time from 50-100 cameras at once – each with 32 32 color pixels and streaming information at the standard TV rate of 24 fps – while running on a smartphone battery for days without recharging. The breakthrough was published this week in the peer-reviewed Proceedings of the National Academy of Sciences (PNAS).

The deep-learning software driving the modern artificial intelligence revolution has mostly run on fairly standard computer hardware. Some tech giants such as Google and Intel have focused some of their considerable resources on creating more specialized computer chips designed for deep learning. But IBM has taken a more unusual approach: It is testing its brain-inspired TrueNorth computer chip as a hardware platform for deep learning. Deep learning's powerful capabilities rely on algorithms called convolutional neural networks that consist of layers of nodes (also known as neurons). Such neural networks can filter huge amounts of data through their "deep" layers to become better at, say, automatically recognizing individual human faces or understanding different languages. These are the types of capabilities that already empower online services offered by the likes of Google, Facebook, Amazon, and Microsoft.