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Drug addiction may be viewed as a form of learning during which strong associations linking actions to drug-seeking are expressed as persistent stimulus–response habits, thereby maintaining a vulnerability to relapse. Disrupting cue–drug memory could be an efficient strategy to reduce the strength of cues in motivating drug-taking behavior. Upon reactivation, these memories undergo a reconsolidation process that can be blocked pharmacologically, providing an opportunity to prevent the powerful control of drug cues on behavior. This conceptually elegant approach still calls for more experimental data. However, an increasing body of evidence suggests that drug taking not only accelerates habit forming, but has long-lasting effects on interactions between memory systems eventually leading to a functional imbalance. The dorsal part of the striatum plays a critical role in habit/procedural learning, whereas the hippocampal memory system encodes relationships between events and their later flexible use. Both humans and rodents studies support the view that the hippocampus and the dorsal striatum interact in either a cooperative or competitive manner during learning, the prefrontal cortex being involved in the selection of an appropriate learning strategy. Chronic drug consumption biases normal interactions between these memory systems. For instance, drug-experienced rodents tend to use preferentially striatum-dependent learning strategies in navigational tasks. These persistent effects seem to occur at cellular, neurophysiological and behavioral levels to promote specific, striatal-dependent forms of learning, to the detriment of spatial/declarative, hippocampal-dependent and more flexible types of memory. Whether cue sensitive and response learners, in contrast to spatial learners, could be prone to drug addiction is an intriguing hypothesis which clearly deserves to be further explored. A loss of flexibility may be uncovered also by imposing changing rules on the subject, such as requiring an attentional shift between different perceptual features of a complex stimulus, as in the attentional set shifting task which was recently adapted to rodents. Working memory is at risk during transition phases, although it remains to be determined whether withdrawal-induced alterations are observed also during protracted abstinence. Drug-induced cognitive biases thus lead to cognitive rigidity which could play a critical, yet overlooked role in different phases of addiction (acquisition, extinction/withdrawal and relapse). They are also likely to preclude the clinical efficiency of treatments. Therefore, the aim of this research topic is to provide an overview of the current work investigating the long-term impact of drug use on learning and memory processes, how multiple memory systems modulate drug-seeking behavior, as well as how drug-induced cognitive biases could contribute to the persistence of addictive behaviors.

alcohol --- memory systems --- decision-making --- addiction --- habit learning

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Drug addiction may be viewed as a form of learning during which strong associations linking actions to drug-seeking are expressed as persistent stimulus–response habits, thereby maintaining a vulnerability to relapse. Disrupting cue–drug memory could be an efficient strategy to reduce the strength of cues in motivating drug-taking behavior. Upon reactivation, these memories undergo a reconsolidation process that can be blocked pharmacologically, providing an opportunity to prevent the powerful control of drug cues on behavior. This conceptually elegant approach still calls for more experimental data. However, an increasing body of evidence suggests that drug taking not only accelerates habit forming, but has long-lasting effects on interactions between memory systems eventually leading to a functional imbalance. The dorsal part of the striatum plays a critical role in habit/procedural learning, whereas the hippocampal memory system encodes relationships between events and their later flexible use. Both humans and rodents studies support the view that the hippocampus and the dorsal striatum interact in either a cooperative or competitive manner during learning, the prefrontal cortex being involved in the selection of an appropriate learning strategy. Chronic drug consumption biases normal interactions between these memory systems. For instance, drug-experienced rodents tend to use preferentially striatum-dependent learning strategies in navigational tasks. These persistent effects seem to occur at cellular, neurophysiological and behavioral levels to promote specific, striatal-dependent forms of learning, to the detriment of spatial/declarative, hippocampal-dependent and more flexible types of memory. Whether cue sensitive and response learners, in contrast to spatial learners, could be prone to drug addiction is an intriguing hypothesis which clearly deserves to be further explored. A loss of flexibility may be uncovered also by imposing changing rules on the subject, such as requiring an attentional shift between different perceptual features of a complex stimulus, as in the attentional set shifting task which was recently adapted to rodents. Working memory is at risk during transition phases, although it remains to be determined whether withdrawal-induced alterations are observed also during protracted abstinence. Drug-induced cognitive biases thus lead to cognitive rigidity which could play a critical, yet overlooked role in different phases of addiction (acquisition, extinction/withdrawal and relapse). They are also likely to preclude the clinical efficiency of treatments. Therefore, the aim of this research topic is to provide an overview of the current work investigating the long-term impact of drug use on learning and memory processes, how multiple memory systems modulate drug-seeking behavior, as well as how drug-induced cognitive biases could contribute to the persistence of addictive behaviors.

alcohol --- memory systems --- decision-making --- addiction --- habit learning

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Computer storage devices --- Computer memory systems --- Computers --- Electronic digital computers --- Storage devices, Computer --- Computer input-output equipment --- Memory management (Computer science) --- Security measures --- Memory systems --- Storage devices

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Storage Systems: Organization, Performance, Coding, Reliability and Their Data Processing was motivated by the 1988 Redundant Array of Inexpensive/Independent Disks proposal to replace large form factor mainframe disks with an array of commodity disks. Disk loads are balanced by striping data into strips--with one strip per disk-- and storage reliability is enhanced via replication or erasure coding, which at best dedicates k strips per stripe to tolerate k disk failures. Flash memories have resulted in a paradigm shift with Solid State Drives (SSDs) replacing Hard Disk Drives (HDDs) for high performance applications. RAID and Flash have resulted in the emergence of new storage companies, namely EMC, NetApp, SanDisk, and Purestorage, and a multibillion-dollar storage market. Key new conferences and publications are reviewed in this book. The goal of the book is to expose students, researchers, and IT professionals to the more important developments in storage systems, while covering the evolution of storage technologies, traditional and novel databases, and novel sources of data. We describe several prototypes: FAWN at CMU, RAMCloud at Stanford, and Lightstore at MIT; Oracle's Exadata, AWS' Aurora, Alibaba's PolarDB, Fungible Data Center; and author's paper designs for cloud storage, namely heterogeneous disk arrays and hierarchical RAID. Surveys storage technologies and lists sources of data: measurements, text, audio, images, and video Familiarizes with paradigms to improve performance: caching, prefetching, log-structured file systems, and merge-trees (LSMs) Describes RAID organizations and analyzes their performance and reliability Conserves storage via data compression, deduplication, compaction, and secures data via encryption Specifies implications of storage technologies on performance and power consumption Exemplifies database parallelism for big data, analytics, deep learning via multicore CPUs, GPUs, FPGAs, and ASICs, e.g., Google's Tensor Processing Units.

Computer storage devices. --- Computer memory systems --- Computers --- Electronic digital computers --- Storage devices, Computer --- Computer input-output equipment --- Memory management (Computer science) --- Memory systems --- Storage devices

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As a step toward ultimate low-power computing, this book introduces normally-off computing, which involves inactive components of computer systems being aggressively powered off with the help of new non-volatile memories (NVMs). Because the energy consumption of modern information devices strongly depends on both hardware and software, co-design and co-optimization of hardware and software are indispensable to improve energy efficiency. The book discusses various topics including (1) details of low-power technologies including power gating, (2) characteristics of several new-generation NVMs, (3) normally-off computing architecture, (4) important technologies for implementing normally-off computing, (5) three practical implementations: healthcare, mobile information devices, and sensor network systems for smart city applications, and (6) related research and development. Bridging computing methodology and emerging memory devices, the book is designed for both hardware and software designers, engineers, and developers as comprehensive material for understanding normally-off computing.

Computer memory systems. --- Electrical engineering. --- Renewable energy resources. --- Memory Structures. --- Electrical Engineering. --- Renewable and Green Energy.