Memory

Memories become stabilized through a time-dependent process that requires gene expression and is commonly known as consolidation. During this time, memories are labile and can be disrupted by a number of interfering events, including electroconvulsive shock, trauma and other learning or the transient effect of drugs such as protein synthesis inhibitors. Once consolidated, memories are insensitive to these disruptions. However, they can again become fragile if recalled or reactivated. Reactivation creates another time-dependent process, known as reconsolidation, during which the memory is restabilized. Here we discuss some of the questions currently debated in the field of memory consolidation and reconsolidation, the molecular and anatomical requirements for both processes and, finally, their functional relationship.     

Memory

Our understanding of the cellular and molecular mechanisms underlying learning and memory formation derives from studies of species as diverse as worms, mollusks, insects, birds and mammals. Despite the quite different brain structures and neuronal networks, the studies support the current notion that neuronal activity leads to changes in synaptic connections as the neural substrate of behavioral plasticity. The analysis of the mechanisms underlying learning and memory formation reveals a surprisingly high conservation between invertebrates and mammals, both at the behavioral as well as the molecular level. This special issue provides an overview of the current knowledge on cellular and molecular processes underlying memory formation. The contributing reviews summarize the findings in different organisms, such as Aplysia, Drosophila, honeybees and mammals, and discuss new approaches, developments and hypotheses all aimed at understanding how the nervous system acquires, stores and retrieves information.     

Memory

In this review we address the idea that conservation of epigenetic mechanisms for information storage represents a unifying model in biology, with epigenetic mechanisms being utilized for cellular memory at levels from behavioral memory to development to cellular differentiation. Epigenetic mechanisms typically involve alterations in chromatin structure, which in turn regulate gene expression. An emerging idea is that the regulation of chromatin structure through histone acetylation and DNA methylation may mediate long-lasting behavioral change in the context of learning and memory. We find this idea fascinating because similar mechanisms are used for triggering and storing long-term 'memory' at the cellular level, for example when cells differentiate. An additional intriguing aspect of the hypothesis of a role for epigenetic mechanisms in information storage is that lifelong behavioral memory storage may involve lasting changes in the physical, three-dimensional structure of DNA itself.     

Memory

Recent research in a variety of systems indicates that memory formation can involve the activation of a wide range of molecular cascades. In assessing this recent work it is clear that no single cascade is uniquely important for all forms of memory, nor is a single form of memory uniquely dependent on a single cascade. Rather, it appears that molecular networks are differentially engaged in the induction of various forms of memory. Despite this highly interactive array of possible cascades, specific 'molecular nodes' have emerged as critical regulatory points in memory formation. Functionally, these nodes can operate in two sequential steps, beginning with a convergence of inputs which coordinately influence the activation state of the node, in which the nature of stimulation determines the dynamics of nodal activity, followed by a divergence of substrate selection, in which the node serves as a gateway that activates specific downstream effectors. Finally, specific nodes can be differentially engaged (i.e. have different 'weights') depending upon the nature and pattern of the activating stimulus. The marine mollusk Aplysia has proven useful for a molecular analysis of memory formation. We will use this system to highlight some of the molecular strategies employed by the nervous system in the formation of memory for sensitization, and we will focus on extracellular signal-related kinase as a candidate node integral to these processes.     

Memory

The molecular mechanisms underlying the induction and maintenance of memory are highly dynamic and comprise distinct phases covering a time window from seconds to even a lifetime. Neuronal networks, which contribute to these processes, have been extensively characterized on various levels of analysis, and imaging techniques allow monitoring of both gross brain activity as well as functional changes in defined brain areas during the time course of memory formation. New techniques developed in honeybees and fruit flies even allow for manipulation of neuronal networks and molecular cascades in a short temporal domain while a living animal under observation acquires new associative memories. These advantages make honeybees and flies ideal organisms to study transient molecular events underlying dynamic memory processing in vivo. In this review we will focus on the temporal features of molecular processes in learning and memory formation, summarize recent knowledge and present an outlook on future developments.     

Memory T lymphocytes

Immunological memory protects organisms from recurrent challenge by pathogens. The persistence of a heightened reactive state initiated by antigenic challenge is mediated by long-lived memory lymphocytes. The survival of memory T cells is thought to require stimulation through the T cell receptor (TCR), sometimes by persistent antigen. However, memory T cells can survive in the absence of antigen, in which case TCR stimulation provided by cell surface self-peptide/ major histocompatibility complex (MHC) molecules and cytokines are required to sustain memory T cells. Recent work using mouse models has provided insights into the origin of memory T cells. Understanding the mechanisms that underlie the differentiation and persistence of memory T cells may improve the effectiveness of vaccines through the induction of T cell memory.     

学习和记忆的神经基础

学习和记忆是脑是基本的高级功能,是我们每时每刻都在经历着的事情。学习和记忆过程中脑内发生了什么？在过去的２０多年中,有关学习和记忆的神经机制研究取得了重大进展。     

星形胶质细胞在学习记忆中的作用

星形胶质细胞是神经胶质细胞的主要组成部分,遍布整个神经系统.以往认为星形胶质细胞的功能主要为支持营养神经元,参与物质代谢,血脑屏障形成等等,但最近研究发现它们还可促进突触形成,影响信号传导及突触可塑性,从而在学习记忆的形成中发挥重要作用.     

Ni2MnGa铁磁形状记忆材料

铁磁形状记忆合金 (FSMA)是在一定温度范围马氏体相稳定同时又具铁磁性的一类特殊的形状记忆合金。Ni2MnGa铁磁形状记忆合金近年来成为呈现磁场驱动大应变的新型驱动材料 ,这些应变来自磁场诱发马氏体孪晶的重排 ,而不是磁场对奥氏体至马氏体相变的作用。孪晶变体的重排在宏观上呈现为正或切应变 ,一非化学计量比Ni2 MnGa单晶于室温加 0 .4T磁场能产生6 %的应变 ,Ni Mn Ga单晶在高至 15 0Hz的交变磁场仍可得到 2 .5 %的应变。本文阐述了与这种磁控形状记忆效应相关的孪晶界迁动的磁学和晶体学理论。马氏体相的大磁晶各向异性能使磁化沿c轴方向有利 ,穿过孪晶界c轴刚好转动 90度 ,同时 ,这个孪晶界也构成了约 90度的畴界。在各向异性的情况下 ,孪晶界的迁动仅有相邻孪晶变体的Zeeman能差驱动 ,μ0 ΔMis·Hi。磁场和外应力对应变的影响通过对一简单的自由能表达式取极小值来表示 ,自由能表达式包括Zeeman能、磁晶各向异性能和外应力以及在某些情况下需考虑的内部弹性能 ,模型的所有参数可通过应力 应变曲线和磁化曲线测量得到。铁磁形状记忆合金的磁场诱发应变可类比传统热弹性形状记忆效应 ,与更为人们所熟知的磁致伸缩现象不同。     

记忆过程的脑/心相关研究

本文首先介绍认知神经科学研究的实验范式,然后比编码／提取的非对称模型、启动效应的神经机制,情景记忆提取的两过程及其脑区和记忆是怎 的几个方面,介绍近年来认知神经科学关于记忆的研究。最后,强调了脑认和成象于心理学与脑科学的重要性。     

Memory formation and the regulation of gene expression

On a cellular level, formation of memory is based on a selective change in synaptic efficacy that is both fast and, in case of important information, long-lasting. Rapidity of cellular changes is achieved by modifying preexisting synaptic molecules (receptors, ion channels), which instantaneously alters the efficacy of synaptic transmission. Endurance, that is the formation of long-term memory (LTM), is based on transient and perhaps also long-lasting changes in protein synthesis. A number of different methods exist to interfere with the synthesis of specific proteins or proteins in general. Other methods, in turn, help to identify proteins whose synthesis is changed following learning. These mostly molecular methods are briefly described in the present review. Their successful application in a variety of memory paradigms in invertebrates and vertebrates is illustrated. The data support the importance of selective changes in gene expression for LTM. Proteins newly synthesized during memory consolidation are likely to contribute to restructuring processes at the synapse, altering the efficiency of transmission beyond the scope of STM. Increased or, less often, decreased synthesis of proteins appears during specific time windows following learning. Recent evidence supports older data suggesting that two or even more waves of protein synthesis exist during the consolidation period. It is expected that the new molecular methods will help to identify and characterize molecules whose expression changes during LTM formation even in complex vertebrate learning paradigms.     

Memory processes during sleep: beyond the standard consolidation theory

Two-step theories of memory formation suggest that an initial encoding stage, during which transient neural assemblies are formed in the hippocampus, is followed by a second step called consolidation, which involves re-processing of activity patterns and is associated with an increasing involvement of the neocortex. Several studies in human subjects as well as in animals suggest that memory consolidation occurs predominantly during sleep (standard consolidation model). Alternatively, it has been suggested that consolidation may occur during waking state as well and that the role of sleep is rather to restore encoding capabilities of synaptic connections (synaptic downscaling theory). Here, we review the experimental evidence favoring and challenging these two views and suggest an integrative model of memory consolidation.     

早期母子隔离应激对大鼠学习记忆能力的影响

目的探讨早期母子隔离应激对幼鼠学习记忆能力的影响。方法30只SD新生大鼠用于实验,实验随机分成3组,正常对照纽、母子隔离15分钟纽、母子隔离3小时纽。在生后40天,三组大鼠进行Morris水迷宫及记录海马脑片长时程增强电位（LTP）实验。结果Morris水迷宫测试,与正常组比较母子隔离3h组大鼠寻找平台需要的潜伏期延长（P〈0．05）,而母子隔离15分钟组大鼠寻找平台需要的潜伏期与正常组比较则无明显变化（P〉0．05）;海马脑片长时程增强电位提示：正常对照组条件刺激（CS）前后fEPSP斜率变化率为：64．7±22．9％;母子隔离3h组为：35．3±14．2％,低于正常组（P〈0．01）;母子隔离15min组为：66．3±29．7％,与正常组比较无显著性意义（P〉0．05）。结论幼鼠生后早期过度的应激导致大鼠的学习记忆能力减弱。     

Memory corticalization triggered by REM sleep: mechanisms of cellular and systems consolidation

Once viewed as a passive physiological state, sleep is a heterogeneous and complex sequence of brain states with essential effects on synaptic plasticity and neuronal functioning. Rapid-eye-movement (REM) sleep has been shown to promote calcium-dependent plasticity in principal neurons of the cerebral cortex, both during memory consolidation in adults and during post-natal development. This article reviews the plasticity mechanisms triggered by REM sleep, with a focus on the emerging role of kinases and immediate-early genes for the progressive corticalization of hippocampus-dependent memories. The body of evidence suggests that memory corticalization triggered by REM sleep is a systemic phenomenon with cellular and molecular causes.     

Realized Volatility Forecasting of Agricultural Commodity Futures Using Long Memory and Regime Switching

We investigate the dynamic properties of the realized volatility of five agricultural commodity futures by employing the high‐frequency data from Chinese markets and find that the realized volatility exhibits both long memory and regime switching. To capture these properties simultaneously, we utilize a Markov switching autoregressive fractionally integrated moving average (MS‐ARFIMA) model to forecast the realized volatility by combining the long memory process with regime switching component, and compare its forecast performances with the competing models at various horizons. The full‐sample estimation results show that the dynamics of the realized volatility of agricultural commodity futures are characterized by two levels of long memory: one associated with the low‐volatility regime and the other with the high‐volatility regime, and the probability to stay in the low‐volatility regime is higher than that in the high‐volatility regime. The out‐of‐sample volatility forecast results show that the combination of long memory with switching regimes improves the performance of realized volatility forecast, and the proposed model represents a superior out‐of‐sample realized volatility forecast to the competing models. Copyright © 2016 John Wiley & Sons, Ltd.     

Forecasting Temperature Indices Density with Time‐Varying Long‐Memory Models

The hedging of weather risks has become extremely relevant in recent years, promoting the diffusion of weather‐derivative contracts. The pricing of such contracts requires the development of appropriate models for the prediction of the underlying weather variables. Within this framework, a commonly used specification is the ARFIMA‐GARCH. We provide a generalization of such a model, introducing time‐varying memory coefficients. Our model satisfies the empirical evidence of the changing memory level observed in average temperature series, and provides useful improvements in the forecasting, simulation, and pricing issues related to weather derivatives. We present an application related to the forecast and simulation of a temperature index density, which is then used for the pricing of weather options. Copyright © 2011 John Wiley & Sons, Ltd.     

Predicting Bid–Ask Spreads Using Long‐Memory Autoregressive Conditional Poisson Models

We introduce a long‐memory autoregressive conditional Poisson (LMACP) model to model highly persistent time series of counts. The model is applied to forecast quoted bid–ask spreads, a key parameter in stock trading operations. It is shown that the LMACP nicely captures salient features of bid–ask spreads like the strong autocorrelation and discreteness of observations. We discuss theoretical properties of LMACP models and evaluate rolling‐window forecasts of quoted bid–ask spreads for stocks traded at NYSE and NASDAQ. We show that Poisson time series models significantly outperform forecasts from AR, ARMA, ARFIMA, ACD and FIACD models. The economic significance of our results is supported by the evaluation of a trade schedule. Scheduling trades according to spread forecasts we realize cost savings of up to 14 % of spread transaction costs. Copyright © 2013 John Wiley & Sons, Ltd.     

Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.