Researchers propose a neuron-as-controller model, challenging the simplistic 1960s-era computational model of neurons.
Category: robotics/AI – Page 240
GXO has partnered with Apptronik to explore Apollo humanoid robot’s use in warehouses. Apollo is the first commercial humanoid robot that was designed for friendly interaction and mass manufacturability.
A recent study published in the journal Artificial Intelligence in Geosciences introduced an advanced method for automatic microfossil detection and analysis. The research team consisted of members from the machine learning group at the University of Tromso (UiT) The Arctic University of Norway.
They have developed a pipeline for extracting fossil information from microscope slide images. They found that deep learning techniques outperform traditional image processing methods and that self-supervision can be effectively used for feature extraction.
Kinetic Automation utilizes computer vision and machine-learning software for diagnosing and recalibrating advanced vehicle systems.
First-of-its-kind brand video premieres during cannes lions festival.
Toys R Us brand is leaping ahead of the curve by creating the first-ever brand film using OpenAI’s new text-to-video tool, Sora.
Toys R Us worked with ad agency Native Foreign to create a video made with OpenAI’s text-to-video platform Sora.
Ups now we have unsupported countries…by what other parameter will soon someone else be pronounced unsupported?
It’s not entirely clear what actions the ChatGPT maker plans to take, if any.
Growth of data eases the way to access the world but requires increasing amounts of energy to store and process. Neuromorphic electronics has emerged in the last decade, inspired by biological neurons and synapses, with in-memory computing ability, extenuating the ‘von Neumann bottleneck’ between the memory and processor and offering a promising solution to reduce the efforts both in data storage and processing, thanks to their multi-bit non-volatility, biology-emulated characteristics, and silicon compatibility. This work reviews the recent advances in emerging memristive devices for artificial neuron and synapse applications, including memory and data-processing ability: the physics and characteristics are discussed first, i.e., valence changing, electrochemical metallization, phase changing, interfaced-controlling, charge-trapping, ferroelectric tunnelling, and spin-transfer torquing. Next, we propose a universal benchmark for the artificial synapse and neuron devices on spiking energy consumption, standby power consumption, and spike timing. Based on the benchmark, we address the challenges, suggest the guidelines for intra-device and inter-device design, and provide an outlook for the neuromorphic applications of resistive switching-based artificial neuron and synapse devices.
Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.
Keywords: artificial neural networks; artificial neurons; artificial synapses; memristive electronic devices; memristors; neuromorphic electronics.
© 2020 Wiley-VCH GmbH.
Brain-inspired neuromorphic computing and portable intelligent electronic products have received increasing attention. In the present work, nanocellulose-gated indium tin oxide neuromorphic transistors are fabricated. The device exhibits good electrical performance. Short-term synaptic plasticities were mimicked, including excitatory postsynaptic current, paired-pulse facilitation, and dynamic high-pass synaptic filtering. Interestingly, an effective linear synaptic weight updating strategy was adopted, resulting in an excellent recognition accuracy of ∼92.93% for the Modified National Institute of Standard and Technology database adopting a two-layer multilayer perceptron neural network. Moreover, with unique interfacial protonic coupling, anxiety disorder behavior was conceptually emulated, exhibiting “neurosensitization”, “primary and secondary fear”, and “fear-adrenaline secretion-exacerbated fear”. Finally, the neuromorphic transistors could be dissolved in water, demonstrating potential in “green” electronics. These findings indicate that the proposed oxide neuromorphic transistors would have potential as implantable chips for nerve health diagnosis, neural prostheses, and brain-machine interfaces.
Keywords: anxiety disorders; neuromorphic computing; oxide neuromorphic transistors; proton coupling; synaptic plasticity.
Recently, neuromorphic computing has been proposed to overcome the drawbacks of the current von Neumann computing architecture. Especially, spiking neural network (SNN) has received significant attention due to its ability to mimic the spike-driven behavior of biological neurons and synapses, potentially leading to low-power consumption and other advantages. In this work, we designed the indium-gallium-zinc oxide (IGZO) channel charge-trap flash (CTF) synaptic device based on a HfO2/Al2O3/Si3N4/Al2O3 layer. Our IGZO-based CTF device exhibits synaptic functions with 128 levels of synaptic weight states and spike-timing-dependent plasticity. The SNN-restricted Boltzmann machine was used to simulate the fabricated CTF device to evaluate the efficiency for the SNN system, achieving the high pattern-recognition accuracy of 83.9%. We believe that our results show the suitability of the fabricated IGZO CTF device as a synaptic device for neuromorphic computing.
Keywords: charge trap flash; neuromorphic computing; nonvolatile memory; oxide semiconductor; spiking neural network.