大鼠海马CA1区神经元高级可塑性（Metaplasticity）的研究

经验（experience）或突触活动依赖的突触信息传递效能的增强（LTP）或抑制（LTD）被称为突触可塑性（synaPticplasticity），是公认的学习和记忆的细胞分子学基础。突触可塑性的方向是突触信息传递效能的增强或降低；突触可塑性的程度是突触信息传递的效能增加量或降低量。突触活动历史可影响随后突触可塑性的方向或程度。这种突触可塑性的可塑性被称为高级可塑性（MetaP1asticity）。最明显的高级可塑性现象是发生在突触效能没有改变的情况下。经验或者突触活动可以同时诱导出突触可塑性和高级可塑性。除非知道突触活动的历史，否则很难预测突触可塑性的大小和方向。高级可塑性简单地讲就是过去的神经信息留下痕迹会对神经信息的加工处理产生持续性影响。目前高级可塑性的机理并不完全清楚。采用全细胞电压钳记录方法，在3周龄wistar大鼠海马脑片中，同时记录schaffer侧枝纤维一以1处兴奋性突触后膜电流（evokedEPsC）和自发性微小兴奋性突触后膜"电流（mEPSC）。结果显示，mEPSC和低频刺激诱导的长时程抑制（Long一TermDePre55ion，LTD）之间呈负相关，mEPsc的频率或幅值越小，诱导出的LTD幅度越大；应激和低钙溶液处理在降低记PsC的频率和幅值的同时并没有明显改变随后LTD的诱导幅，但是这两者负相关性依然存。相关系数在应激条件下有增加，在低钙溶液中有降低。以上结果提示，mPEsc的频率或幅值可能指示了过去的突触活动或经验产牛的高幼可朔件＿田、冲稀恻了喃层的，：。、云导的幅度。C鹉子妙触可黔和高级可黔中起着关键作。作为一种非选择性阳离子通道，van11101drec即tortyPel（VR1或TRPVI）在脑区中广泛表达，尤其在海马高表达。内源性配体可以激活该受体通道。但目前并不清楚vRI在海马突触可塑性当中的作用。我们发现VRI激动剂辣椒素不影响脑片中海马以1区基础突触传递，但是它阻断低频诱导（LFS，3Hz）的LTD，增强高频诱导（HFs，20OHz）的长时程增强（Long一TerlnPotetiantion，LTP）。也就是vRI激活改变了突触可塑性的方向，是一种经典的高级可塑性现象。正常情况下50Hz不能诱导出LTP，而加入辣椒素后易化了LTP的诱导。因此LTP诱导的闽值降低。辣椒素激动剂对突触可塑性或高级可塑性的效应不依赖协。A受体而依赖于H型钙调蛋白激酶（CaMKII）的调控。因为场。A受体电流和它的电流一电压曲线不受此激动剂影响，但CaMKII抑制剂KN-93或者VRI拮抗剂capsazepine却阻断了辣椒素的这种效应。这些发现，显示VRI可以调控海马以1区的高级突触可塑性，可能参与某些类型的学习和记忆。

Structural plasticity can produce metaplasticity.

BACKGROUND: Synaptic plasticity underlies many aspect of learning memory and development. The properties of synaptic plasticity can change as a function of previous plasticity and previous activation of synapses, a phenomenon called metaplasticity. Synaptic plasticity not only changes the functional connectivity between neurons but in some cases produces a structural change in synaptic spines; a change thought to form a basis for this observed plasticity. Here we examine to what extent structural plasticity of spines can be a cause for metaplasticity. This study is motivated by the observation that structural changes in spines are likely to affect the calcium dynamics in spines. Since calcium dynamics determine the sign and magnitude of synaptic plasticity, it is likely that structural plasticity will alter the properties of synaptic plasticity. METHODOLOGY/PRINCIPAL FINDINGS: In this study we address the question how spine geometry and alterations of N-methyl-D-aspartic acid (NMDA) receptors conductance may affect plasticity. Based on a simplified model of the spine in combination with a calcium-dependent plasticity rule, we demonstrated that after the induction phase of plasticity a shift of the long term potentiation (LTP) or long term depression (LTD) threshold takes place. This induces a refractory period for further LTP induction and promotes depotentiation as observed experimentally. That resembles the BCM metaplasticity rule but specific for the individual synapse. In the second phase, alteration of the NMDA response may bring the synapse to a state such that further synaptic weight alterations are feasible. We show that if the enhancement of the NMDA response is proportional to the area of the post synaptic density (PSD) the plasticity curves most likely return to the initial state. CONCLUSIONS/SIGNIFICANCE: Using simulations of calcium dynamics in synaptic spines, coupled with a biophysically motivated calcium-dependent plasticity rule, we find under what conditions structural plasticity can form the basis of synapse specific metaplasticity.

A biophysical model of synaptic plasticity and metaplasticity can account for the dynamics of the backward shift of hippocampal place fields.

Hippocampal place cells in the rat undergo experience-dependent changes when the rat runs stereotyped routes. One such change, the backward shift of the place field center of mass, has been linked by previous modeling efforts to spike-timing-dependent plasticity (STDP). However, these models did not account for the termination of the place field shift and they were based on an abstract implementation of STDP that ignores many of the features found in cortical plasticity. Here, instead of the abstract STDP model, we use a calcium-dependent plasticity (CaDP) learning rule that can account for many of the observed properties of cortical plasticity. We use the CaDP learning rule in combination with a model of metaplasticity to simulate place field dynamics. Without any major changes to the parameters of the original model, the present simulations account both for the initial rapid place field shift and for the subsequent slowing down of this shift. These results suggest that the CaDP model captures the essence of a general cortical mechanism of synaptic plasticity, which may underlie numerous forms of synaptic plasticity observed both in vivo and in vitro.

On the Biological Plausibility of Artificial Metaplasticity

The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausibility, a Multilayer Perceptron has been used. During the training phase, the artificial metaplasticity multilayer perceptron could be considered a new probabilistic version of the presynaptic rule, as during the training phase the algorithm assigns higher values for updating the weights in the less probable activations than in the ones with higher probability

A Prediction Model to Diabetes using Artificial Metaplasticity

Diabetes is the most common disease nowadays in all populations and in all age groups. Different techniques of artificial intelligence has been applied to diabetes problem. This research proposed the artificial metaplasticity on multilayer perceptron (AMMLP) as prediction model for prediction of diabetes. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with other algorithms, recently proposed by other researchers, that were applied to the same database. The best result obtained so far with the AMMLP algorithm is 89.93%

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

A Calcium-Dependent Plasticity Rule for HCN Channels Maintains Activity Homeostasis and Stable Synaptic Learning

Theoretical and computational frameworks for synaptic plasticity and learning have a long and cherished history, with few parallels within the well-established literature for plasticity of voltage-gated ion channels. In this study, we derive rules for plasticity in the hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, and assess the synergy between synaptic and HCN channel plasticity in establishing stability during synaptic learning. To do this, we employ a conductance-based model for the hippocampal pyramidal neuron, and incorporate synaptic plasticity through the well-established Bienenstock-Cooper-Munro (BCM)-like rule for synaptic plasticity, wherein the direction and strength of the plasticity is dependent on the concentration of calcium influx. Under this framework, we derive a rule for HCN channel plasticity to establish homeostasis in synaptically-driven firing rate, and incorporate such plasticity into our model. In demonstrating that this rule for HCN channel plasticity helps maintain firing rate homeostasis after bidirectional synaptic plasticity, we observe a linear relationship between synaptic plasticity and HCN channel plasticity for maintaining firing rate homeostasis. Motivated by this linear relationship, we derive a calcium-dependent rule for HCN-channel plasticity, and demonstrate that firing rate homeostasis is maintained in the face of synaptic plasticity when moderate and high levels of cytosolic calcium influx induced depression and potentiation of the HCN-channel conductance, respectively. Additionally, we show that such synergy between synaptic and HCN-channel plasticity enhances the stability of synaptic learning through metaplasticity in the BCM-like synaptic plasticity profile. Finally, we demonstrate that the synergistic interaction between synaptic and HCN-channel plasticity preserves robustness of information transfer across the neuron under a rate-coding schema. Our results establish specific physiological roles for experimentally observed plasticity in HCN channels accompanying synaptic plasticity in hippocampal neurons, and uncover potential links between HCN-channel plasticity and calcium influx, dynamic gain control and stable synaptic learning.

Analogous Synaptic Plasticity Profiles Emerge from Disparate Channel Combinations

An open question within the Bienenstock-Cooper-Munro theory for synaptic modification concerns the specific mechanism that is responsible for regulating the sliding modification threshold (SMT). In this conductance-based modeling study on hippocampal pyramidal neurons, we quantitatively assessed the impact of seven ion channels (R- and T-type calcium, fast sodium, delayed rectifier, A-type, and small-conductance calcium-activated (SK) potassium and HCN) and two receptors (AMPAR and NMDAR) on a calcium-dependent Bienenstock-Cooper-Munro-like plasticity rule. Our analysis with R- and T-type calcium channels revealed that differences in their activation-inactivation profiles resulted in differential impacts on how they altered the SMT. Further, we found that the impact of SK channels on the SMT critically depended on the voltage dependence and kinetics of the calcium sources with which they interacted. Next, we considered interactions among all the seven channels and the two receptors through global sensitivity analysis on 11 model parameters. We constructed 20,000 models through uniform randomization of these parameters and found 360 valid models based on experimental constraints on their plasticity profiles. Analyzing these 360 models, we found that similar plasticity profiles could emerge with several nonunique parametric combinations and that parameters exhibited weak pairwise correlations. Finally, we used seven sets of virtual knock-outs on these 360 models and found that the impact of different channels on the SMT was variable and differential. These results suggest that there are several nonunique routes to regulate the SMT, and call for a systematic analysis of the variability and state dependence of the mechanisms underlying metaplasticity during behavior and pathology.

Amplitude/frequency of spontaneous mEPSC correlates to the degree of long-term depression in the CA1 region of the hippocampal slice

Prior synaptic or cellular activity influences degree or threshold for subsequent induction of synaptic plasticity, a process known as metaplasticity. Thus, the continual synaptic activity, spontaneous miniature excitatory synaptic current (mEPSC) may correlate to the induction of long-teen depression (LTD). Here, we recorded whole-cell EPSC and mEPSC alternately in the Schaffer-CA1 synapses in brain slice of young rats, and found that this recording configuration affected neither EPSC nor mEPSC. Low frequency stimulation (LFS) induced variable magnitudes of LTD. Remarkably, larger magnitudes of LTD were significantly correlated to smaller amplitude/lower frequency of the basal mEPSC. Furthermore, under the conditions reduced amplitude/frequency of the basal mEPSC by exposure to behavioral stress immediately before slice preparation or low concentration of calcium in bath solution, the magnitudes of LTD were still inversely correlated to mEPSC amplitude/frequency. These new findings suggest that spontaneous mEPSC may reflect functional and/or structural aspects of the synapses, the synaptic history ongoing metaplasticity. (C) 2005 Elsevier B.V. All rights reserved.

Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3.

A venerable history of classical work on autoassociative memory has significantly shaped our understanding of several features of the hippocampus, and most prominently of its CA3 area, in relation to memory storage and retrieval. However, existing theories of hippocampal memory processing ignore a key biological constraint affecting memory storage in neural circuits: the bounded dynamical range of synapses. Recent treatments based on the notion of metaplasticity provide a powerful model for individual bounded synapses; however, their implications for the ability of the hippocampus to retrieve memories well and the dynamics of neurons associated with that retrieval are both unknown. Here, we develop a theoretical framework for memory storage and recall with bounded synapses. We formulate the recall of a previously stored pattern from a noisy recall cue and limited-capacity (and therefore lossy) synapses as a probabilistic inference problem, and derive neural dynamics that implement approximate inference algorithms to solve this problem efficiently. In particular, for binary synapses with metaplastic states, we demonstrate for the first time that memories can be efficiently read out with biologically plausible network dynamics that are completely constrained by the synaptic plasticity rule, and the statistics of the stored patterns and of the recall cue. Our theory organises into a coherent framework a wide range of existing data about the regulation of excitability, feedback inhibition, and network oscillations in area CA3, and makes novel and directly testable predictions that can guide future experiments.

Impact d’une sieste sur plasticité cérébrale induite par stimulation magnétique transcrânienne

Chez l’humain, différents protocoles de stimulation magnétique transcrânienne répétée (SMTr) peuvent être utilisés afin de manipuler expérimentalement la plasticité cérébrale au niveau du cortex moteur primaire (M1). Ces techniques ont permis de mieux comprendre le rôle du sommeil dans la régulation de la plasticité cérébrale. Récemment, une étude a montré que lorsqu’une première session de stimulation SMTr au niveau de M1 est suivie d’une nuit de sommeil, l’induction subséquente de la plasticité par une deuxième session SMTr est augmentée. La présente étude a investigué si ce type de métaplasticité pouvait également bénéficier d’une sieste diurne. Quatorze sujets en santé ont reçu deux sessions de intermittent theta burst stimulation (iTBS) connue pour son effet facilitateur sur l’excitabilité corticale. Les sessions de stimulation étaient séparées par une sieste de 90 minutes ou par une période équivalente d’éveil. L’excitabilité corticale était quantifiée en terme d’amplitude des potentiels évoqués moteurs (PEM) mesurés avant et après chaque session de iTBS. Les résultats montrent que la iTBS n’est pas parvenue à augmenter de manière robuste l’amplitude des PEMs lors de la première session de stimulation. Lors de la deuxième session de stimulation, la iTBS a produit des changements plastiques variables et ce peu importe si les sujets ont dormi ou pas. Les effets de la iTBS sur l’excitabilité corticale étaient marqués par une importante variabilité inter et intra-individuelle dont les possibles causes sont discutées.

Spatial and temporal aspects of synaptic plasticity

The notion that changes in synaptic efficacy underlie learning and memory processes is now widely accepted even if definitive proof of the synaptic plasticity and memory hypothesis is still lacking. When learning occurs, patterns of neural activity representing the occurrence of events cause changes in the strength of synaptic connections within the brain. Reactivation of these altered connections constitutes the experience of memory for these events and for other events with which they may be associated. These statements summarize a long-standing theory of memory formation that we refer to as the synaptic plasticity and memory hypothesis. Since activity-dependent synaptic plasticity is induced at appropriate synapses during memory formation, and is both necessary and sufficient for the information storage, we can speculate that a methodological study of the synapse will help us understand the mechanism of learning. Random events underlie a wide range of biological processes as diverse as genetic drift and molecular diffusion, regulation of gene expression and neural network function. Additionally spatial variability may be important especially in systems with nonlinear behavior. Since synapse is a complex biological system we expect that stochasticity as well as spatial gradients of different enzymes may be significant for induction of plasticity. ^ In that study we address the question "how important spatial and temporal aspects of synaptic plasticity may be". We developed methods to justify our basic assumptions and examined the main sources of variability of calcium dynamics. Among them, a physiological method to estimate the number of postsynaptic receptors as well as a hybrid algorithm for simulating postsynaptic calcium dynamics. Additionally we studied how synaptic geometry may enhance any possible spatial gradient of calcium dynamics and how that spatial variability affect plasticity curves. Finally, we explored the potential of structural synaptic plasticity to provide a metaplasticity mechanism specific for the synapse. ^

Probabilistic versus incremental presynaptic learning in biological plausible synapses

In this paper, the presynaptic rule, a classical rule for hebbian learning, is revisited. It is shown that the presynaptic rule exhibits relevant synaptic properties like synaptic directionality, and LTP metaplasticity (long-term potentiation threshold metaplasticity). With slight modifications, the presynaptic model also exhibits metaplasticity of the long-term depression threshold, being also consistent with Artola, Brocher and Singer’s (ABS) influential model. Two asymptotically equivalent versions of the presynaptic rule were adopted for this analysis: the first one uses an incremental equation while the second, conditional probabilities. Despite their simplicity, both types of presynaptic rules exhibit sophisticated biological properties, specially the probabilistic version

Data mining for the diagnosis of type 2 diabetes

Diabetes is the most common disease nowadays in all populations and in all age groups. diabetes contributing to heart disease, increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Different techniques of artificial intelligence has been applied to diabetes problem. The purpose of this study is apply the artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining (DM) technique for the diabetes disease diagnosis. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with decision tree (DT), Bayesian classifier (BC) and other algorithms, recently proposed by other researchers, that were applied to the same database. The robustness of the algorithms are examined using classification accuracy, analysis of sensitivity and specificity, confusion matrix. The results obtained by AMMLP are superior to obtained by DT and BC.

Implication du domaine intracellulaire du précurseur de la protéine amyloïde dans la modulation de la plasticité synaptique

Alzheimer's disease is the most common type of dementia in the elderly; it is characterized by early deficits in learning and memory formation and ultimately leads to a generalised loss of higher cognitive functions. While amyloid beta (Aβ) and tau are traditionally associated with the development of Alzheimer disease, recent studies suggest that other factors, like the intracellular domain (APP-ICD) of the amyloid precursor protein (APP), could play a role. In this study, we investigated whether APP-ICD could affect synaptic transmission and synaptic plasticity in the hippocampus, which is involved in learning and memory processes. Our results indicated that overexpression of APP-ICD in hippocampal CA1 neurons leads to a decrease in evoked AMPA-receptor and NMDA-receptor dependent synaptic transmission. Our study demonstrated that this effect is specific for APP-ICD since its closest homologue APLP2-ICD did not reproduce this effect. In addition, APP-ICD blocks the induction of long term potentiation (LTP) and leads to increased of expression and facilitated induction of long term depression (LTD), while APLP2-ICD shows neither of these effects. Our study showed that this difference observed in synaptic transmission and plasticity between the two intracellular domains resides in the difference of one alanine in the APP-ICD versus a proline in the APLP2-ICD. Exchanging this critical amino-acid through point-mutation, we observed that APP(PAV)-ICD had no longer an effect on synaptic plasticity. We also demonstrated that APLP2(AAV)-ICD mimic the effect of APP-ICD in regards of facilitated LTD. Next we showed that the full length APP-APLP2-APP (APP with a substitution of the Aβ component for its homologous APLP2 part) had no effect on synaptic transmission or synaptic plasticity when compared to the APP-ICD. However, by activating caspase cleavage prior to induction of LTD or LTP, we observed an LTD facilitation and a block of LTP with APP-APLP2-APP, effects that were not seen with the full length APLP2 protein. APP is phosphorylated at threonine 668 (Thr668), which is localized directly after the aforementioned critical alanine and the caspase cleavage site in APP-APLP2-APP. Mutating this Thr668 for an alanine abolishes the effects on LTD and restores LTP induction. Finally, we showed that the facilitation of LTD with APP-APLP2-APP involves ryanodine receptor dependent calcium release from intracellular stores. Taken together, we propose the emergence of a new APP intracellular domain, which plays a critical role in the regulation of synaptic plasticity and by extension, could play a role in the development of memory loss in Alzheimer’s disease.