다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Manipulating a stubborn magnet Spintronics is an alternative to conventional electronics, based on using the electron's spin rather than its charge. Spintronic devices, such as magnetic memory, have traditionally used ferromagnetic materials to encode the 1's and 0's of the binary code. A weakness of this approach—that strong magnetic fields can erase the encoded information—could be avoided by using antiferromagnets instead of ferromagnets. But manipulating the magnetic ordering of antiferromagnets is tricky. Now, Wadley et al. have found a way (see the Perspective by Marrows). Running currents along specific directions in the thin films of the antiferromagnetic compound CuMnAs reoriented the magnetic domains in the material. Science, this issue p. 587; see also p. 558 Transport and optical measurements are used to demonstrate the switching of domains in the antiferromagnetic compound CuMnAs. [Also see Perspective by Marrows] Antiferromagnets are hard to control by external magnetic fields because of the alternating directions of magnetic moments on individual atoms and the resulting zero net magnetization. However, relativistic quantum mechanics allows for generating current-induced internal fields whose sign alternates with the periodicity of the antiferromagnetic lattice. Using these fields, which couple strongly to the antiferromagnetic order, we demonstrate room-temperature electrical switching between stable configurations in antiferromagnetic CuMnAs thin-film devices by applied current with magnitudes of order 106 ampere per square centimeter. Electrical writing is combined in our solid-state memory with electrical readout and the stored magnetic state is insensitive to and produces no external magnetic field perturbations, which illustrates the unique merits of antiferromagnets for spintronics.

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Electrical switching of an antiferromagnet

The representation of contextual fear memories evolves over time in the hippocampus, amygdala, and prefrontal cortex. The network of memory consolidation Memories are thought to be formed in the hippocampus and later moved to the neocortex for long-term storage. However, little is known about the mechanisms that underlie the formation and maturation of neocortical memories and their interaction with the hippocampal network. Kitamura et al. discovered that at the onset of learning, neurons for contextual fear memory are quickly produced in the prefrontal cortex. This process depends on the activity of afferents from both the hippocampus and the amygdala. Over time, the prefrontal neurons consolidate their role in memory expression. In contrast, the hippocampal neurons slowly lose this function. Science, this issue p. 73 Episodic memories initially require rapid synaptic plasticity within the hippocampus for their formation and are gradually consolidated in neocortical networks for permanent storage. However, the engrams and circuits that support neocortical memory consolidation have thus far been unknown. We found that neocortical prefrontal memory engram cells, which are critical for remote contextual fear memory, were rapidly generated during initial learning through inputs from both the hippocampal–entorhinal cortex network and the basolateral amygdala. After their generation, the prefrontal engram cells, with support from hippocampal memory engram cells, became functionally mature with time. Whereas hippocampal engram cells gradually became silent with time, engram cells in the basolateral amygdala, which were necessary for fear memory, were maintained. Our data provide new insights into the functional reorganization of engrams and circuits underlying systems consolidation of memory.

Engrams and circuits crucial for systems consolidation of a memory

This tutorial describes challenges and possible avenues for the implementation of the components of a solid-state system, which emulates a biological brain. The tutorial is devoted mostly to a charge-based (i.e. electric controlled) implementation using transition metal oxides materials, which exhibit unique properties that emulate key functionalities needed for this application. In the Introduction, we compare the main differences between a conventional computational machine, based on the Turing-von Neumann paradigm, to a Neuromorphic machine, which tries to emulate important functionalities of a biological brain. We also describe the main electrical properties of biological systems, which would be useful to implement in a charge-based system. In Chapter II, we describe the main components of a possible solid-state implementation. In Chapter III, we describe a variety of Resistive Switching phenomena, which may serve as the functional basis for the implementation of key devices for Neuromorphic computing. In Chapter IV we describe why transition metal oxides, are promising materials for future Neuromorphic machines. Theoretical models describing different resistive switching mechanisms are discussed in Chapter V while existing implementations are described in Chapter VI. Chapter VII presents applications to practical problems. We list in Chapter VIII important basic research challenges and open issues. We discuss issues related to specific implementations, novel materials, devices and phenomena. The development of reliable, fault tolerant, energy efficient devices, their scaling and integration into a Neuromorphic computer may bring us closer to the development of a machine that rivals the brain.

Challenges in materials and devices for Resistive-Switching-based Neuromorphic Computing

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Robot Evolution The Development Of Anthrobotics

Neuromorphic electronics, an emerging field that aims for building electronic mimics of the biological brain, holds promise for reshaping the frontiers of information technology and enabling a more intelligent and efficient computing paradigm. As their biological brain counterpart, the neuromorphic electronic systems are complex, having multiple levels of organization. Inspired by David Marr's famous three‐level analytical framework developed for neuroscience, the advances in neuromorphic electronic systems are selectively surveyed and given significance to these research endeavors as appropriate from the computational level, algorithmic level, or implementation level. Under this framework, the problem of how to build a neuromorphic electronic system is defined in a tractable way. In conclusion, the development of neuromorphic electronic systems confronts a similar challenge to the one neuroscience confronts, that is, the limited constructability of the low‐level knowledge (implementations and algorithms) to achieve high‐level brain‐like (human‐level) computational functions. An opportunity arises from the communication among different levels and their codesign. Neuroscience lab‐on‐neuromorphic chip platforms offer additional opportunity for mutual benefit between the two disciplines.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202100054

Xiaolong Zou

Papers

A Marr's Three-Level Analytical Framework for Neuromorphic Electronic Systems