慢性应激对大鼠学习记忆能力和海马LTP的影响

目的和方法：本研究采用一种多因素的２１ｄ慢性应激动物模型,以Ｙ迷宫和ＬＴＰ为指标,探讨慢性应激对运动学习记忆能力和海马神经突触可塑性的影响。结果：长期慢性应激使大鼠空间学习记忆能力下降,而且,使中枢海马齿状回ＬＴＰ的诱生受到抑制。结论：慢性应激可能使大鼠海马齿状回神经突触可塑性降低,并进一步影响到学习记忆的功能。     

An imperfect dopaminergic error signal can drive temporal-difference learning

An open problem in the field of computational neuroscience is how to link synaptic plasticity to system-level learning. A promising framework in this context is temporal-difference (TD) learning. Experimental evidence that supports the hypothesis that the mammalian brain performs temporal-difference learning includes the resemblance of the phasic activity of the midbrain dopaminergic neurons to the TD error and the discovery that cortico-striatal synaptic plasticity is modulated by dopamine. However, as the phasic dopaminergic signal does not reproduce all the properties of the theoretical TD error, it is unclear whether it is capable of driving behavior adaptation in complex tasks. Here, we present a spiking temporal-difference learning model based on the actor-critic architecture. The model dynamically generates a dopaminergic signal with realistic firing rates and exploits this signal to modulate the plasticity of synapses as a third factor. The predictions of our proposed plasticity dynamics are in good agreement with experimental results with respect to dopamine, pre- and post-synaptic activity. An analytical mapping from the parameters of our proposed plasticity dynamics to those of the classical discrete-time TD algorithm reveals that the biological constraints of the dopaminergic signal entail a modified TD algorithm with self-adapting learning parameters and an adapting offset. We show that the neuronal network is able to learn a task with sparse positive rewards as fast as the corresponding classical discrete-time TD algorithm. However, the performance of the neuronal network is impaired with respect to the traditional algorithm on a task with both positive and negative rewards and breaks down entirely on a task with purely negative rewards. Our model demonstrates that the asymmetry of a realistic dopaminergic signal enables TD learning when learning is driven by positive rewards but not when driven by negative rewards.     

How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks

Intelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this ability. During learning these neurons become tuned to relevant features and start to represent them with persistent activity during memory delays. This learning process is not well understood. Here we develop a biologically plausible learning scheme that explains how trial-and-error learning induces neuronal selectivity and working memory representations for task-relevant information. We propose that the response selection stage sends attentional feedback signals to earlier processing levels, forming synaptic tags at those connections responsible for the stimulus-response mapping. Globally released neuromodulators then interact with tagged synapses to determine their plasticity. The resulting learning rule endows neural networks with the capacity to create new working memory representations of task relevant information as persistent activity. It is remarkably generic: it explains how association neurons learn to store task-relevant information for linear as well as non-linear stimulus-response mappings, how they become tuned to category boundaries or analog variables, depending on the task demands, and how they learn to integrate probabilistic evidence for perceptual decisions.     

Calcium control of triphasic hippocampal STDP

Synaptic plasticity is believed to represent the neural correlate of mammalian learning and memory function. It has been demonstrated that changes in synaptic conductance can be induced by approximately synchronous pairings of pre- and post- synaptic action potentials delivered at low frequencies. It has also been established that NMDAr-dependent calcium influx into dendritic spines represents a critical signal for plasticity induction, and can account for this spike-timing dependent plasticity (STDP) as well as experimental data obtained using other stimulation protocols. However, subsequent empirical studies have delineated a more complex relationship between spike-timing, firing rate, stimulus duration and post-synaptic bursting in dictating changes in the conductance of hippocampal excitatory synapses. Here, we present a detailed biophysical model of single dendritic spines on a CA1 pyramidal neuron, describe the NMDAr-dependent calcium influx generated by different stimulation protocols, and construct a parsimonious model of calcium driven kinase and phosphatase dynamics that dictate the probability of stochastic transitions between binary synaptic weight states in a Markov model. We subsequently demonstrate that this approach can account for a range of empirical observations regarding the dynamics of synaptic plasticity induced by different stimulation protocols, under regimes of pharmacological blockade and metaplasticity. Finally, we highlight the strengths and weaknesses of this parsimonious, unified computational synaptic plasticity model, discuss differences between the properties of cortical and hippocampal plasticity highlighted by the experimental literature, and the manner in which further empirical and theoretical research might elucidate the cellular basis of mammalian learning and memory function.     

The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning

To survive, animals have to quickly modify their behaviour when the reward changes. The internal representations responsible for this are updated through synaptic weight changes, mediated by certain neuromodulators conveying feedback from the environment. In previous experiments, we discovered a form of hippocampal Spike-Timing-Dependent-Plasticity (STDP) that is sequentially modulated by acetylcholine and dopamine. Acetylcholine facilitates synaptic depression, while dopamine retroactively converts the depression into potentiation. When these experimental findings were implemented as a learning rule in a computational model, our simulations showed that cholinergic-facilitated depression is important for reversal learning. In the present study, we tested the model’s prediction by optogenetically inactivating cholinergic neurons in mice during a hippocampus-dependent spatial learning task with changing rewards. We found that reversal learning, but not initial place learning, was impaired, verifying our computational prediction that acetylcholine-modulated plasticity promotes the unlearning of old reward locations. Further, differences in neuromodulator concentrations in the model captured mouse-by-mouse performance variability in the optogenetic experiments. Our line of work sheds light on how neuromodulators enable the learning of new contingencies.     

寡聚体Aβ1-42对大鼠海马突触可塑性的慢性影响

突触传递的长时程抑制(long-term depression,LTD)和长时程增强(longterm-potentiation,LTP)是突触可塑性的两种重要形式,并且与学习记忆密切相关.本文探讨Sprague-Dawley(SD)大鼠在海马齿状回区(dentate gyrus,DG)注射36 h孵育形成的寡聚体Aβ...     

Mechanisms by which autophagy regulates memory capacity in ageing

Autophagy agonists have been proposed to slow down neurodegeneration. Spermidine, a polyamine that acts as an autophagy agonist, is currently under clinical trial for the treatment of age‐related memory decline. How Spermidine and other autophagy agonists regulate memory and synaptic plasticity is under investigation. We set up a novel mouse model of mild cognitive impairment (MCI), in which middle‐aged (12‐month‐old) mice exhibit impaired memory capacity, lysosomes engulfed with amyloid fibrils (β‐amyloid and α‐synuclein) and impaired task‐induced GluA1 hippocampal post‐translation modifications. Subchronic treatment with Spermidine as well as the autophagy agonist TAT‐Beclin 1 rescued memory capacity and GluA1 post‐translational modifications by favouring the autophagy/lysosomal‐mediated degradation of amyloid fibrils. These findings provide new mechanistic evidence on the therapeutic relevance of autophagy enhancers which, by improving the degradation of misfolded proteins, slow down age‐related memory decline.     

Arc/Arg3.1 is essential for the consolidation of synaptic plasticity and memories

Arc/Arg3.1 is robustly induced by plasticity-producing stimulation and specifically targeted to stimulated synaptic areas. To investigate the role of Arc/Arg3.1 in synaptic plasticity and learning and memory, we generated Arc/Arg3.1 knockout mice. These animals fail to form long-lasting memories for implicit and explicit learning tasks, despite intact short-term memory. Moreover, they exhibit a biphasic alteration of hippocampal long-term potentiation in the dentate gyrus and area CA1 with an enhanced early and absent late phase. In addition, long-term depression is significantly impaired. Together, these results demonstrate a critical role for Arc/Arg3.1 in the consolidation of enduring synaptic plasticity and memory storage.     

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.     

Forebrain-specific calcineurin knockout selectively impairs bidirectional synaptic plasticity and working/episodic-like memory.

Calcineurin is a calcium-dependent protein phosphatase that has been implicated in various aspects of synaptic plasticity. By using conditional gene-targeting techniques, we created mice in which calcineurin activity is disrupted specifically in the adult forebrain. At hippocampal Schaffer collateral-CA1 synapses, LTD was significantly diminished, and there was a significant shift in the LTD/LTP modification threshold in mutant mice. Strikingly, although performance was normal in hippocampus-dependent reference memory tasks, including contextual fear conditioning and the Morris water maze, the mutant mice were impaired in hippocampus-dependent working and episodic-like memory tasks, including the delayed matching-to-place task and the radial maze task. Our results define a critical role for calcineurin in bidirectional synaptic plasticity and suggest a novel mechanistic distinction between working/episodic-like memory and reference memory.     

A role for α-adducin (ADD-1) in nematode and human memory

Identifying molecular mechanisms that underlie learning and memory is one of the major challenges in neuroscience. Taken the advantages of the nematode Caenorhabditis elegans, we investigated α-adducin (add-1) in aversive olfactory associative learning and memory. Loss of add-1 function selectively impaired short- and long-term memory without causing acquisition, sensory, or motor deficits. We showed that α-adducin is required for consolidation of synaptic plasticity, for sustained synaptic increase of AMPA-type glutamate receptor (GLR-1) content and altered GLR-1 turnover dynamics. ADD-1, in a splice-form- and tissue-specific manner, controlled the storage of memories presumably through actin-capping activity. In support of the C. elegans results, genetic variability of the human ADD1 gene was significantly associated with episodic memory performance in healthy young subjects. Finally, human ADD1 expression in nematodes restored loss of C. elegans add-1 gene function. Taken together, our findings support a role for α-adducin in memory from nematodes to humans. Studying the molecular and genetic underpinnings of memory across distinct species may be helpful in the development of novel strategies to treat memory-related diseases.     

Protein kinase D promotes plasticity-induced F-actin stabilization in dendritic spines and regulates memory formation

Actin turnover in dendritic spines influences spine development, morphology, and plasticity, with functional consequences on learning and memory formation. In nonneuronal cells, protein kinase D (PKD) has an important role in stabilizing F-actin via multiple molecular pathways. Using in vitro models of neuronal plasticity, such as glycine-induced chemical long-term potentiation (LTP), known to evoke synaptic plasticity, or long-term depolarization block by KCl, leading to homeostatic morphological changes, we show that actin stabilization needed for the enlargement of dendritic spines is dependent on PKD activity. Consequently, impaired PKD functions attenuate activity-dependent changes in hippocampal dendritic spines, including LTP formation, cause morphological alterations in vivo, and have deleterious consequences on spatial memory formation. We thus provide compelling evidence that PKD controls synaptic plasticity and learning by regulating actin stability in dendritic spines.     

Hippocampal MMP-3 elevation is associated with passive avoidance conditioning

Alterations in synaptic efficiency that underlie learning and memory consolidation appear to require an accompanying reconfiguration of the extracellular matrix (ECM). This restructuring of the ECM is carried out, in part, by a family of enzymes called, the matrix metalloproteinases, which includes matrix metalloproteinase-3 (MMP-3: stromelysin-1). The present study determined that a transient elevation in hippocampal MMP-3 expression occurred in rats following associative learning in the passive avoidance (PA) task. No change in MMP-3 was observed when rats were exposed either to the behavioral apparatus or the training stimulus alone. Furthermore, when an MMP-3 inhibitor was administered prior to PA training, dose-dependent learning deficits were observed, suggesting a causal relationship between learning-induced hippocampal MMP-3 elevation and associative memory formation. These findings suggest that increased hippocampal MMP-3 expression is an event that may play an important role in synaptic plasticity and memory consolidation.     

Hippocampal expression of myelin-associated inhibitors is induced with age-related cognitive decline and correlates with deficits of spatial learning and memory

Impairment of cognitive functions including hippocampus-dependent spatial learning and memory affects nearly half of the aged population. Age-related cognitive decline is associated with synaptic dysfunction that occurs in the absence of neuronal cell loss, suggesting that impaired neuronal signaling and plasticity may underlie age-related deficits of cognitive function. Expression of myelin-associated inhibitors (MAIs) of synaptic plasticity, including the ligands myelin-associated glycoprotein, neurite outgrowth inhibitor A, and oligodendrocyte myelin glycoprotein, and their common receptor, Nogo-66 receptor, was examined in hippocampal synaptosomes and Cornu ammonis area (CA)1, CA3 and dentate gyrus subregions derived from adult (12-13 months) and aged (26-28 months) Fischer 344 × Brown Norway rats. Rats were behaviorally phenotyped by Morris water maze testing and classified as aged cognitively intact (n = 7-8) or aged cognitively impaired (n = 7-10) relative to adults (n = 5-7). MAI protein expression was induced in cognitively impaired, but not cognitively intact, aged rats and correlated with cognitive performance in individual rats. Immunohistochemical experiments demonstrated that up-regulation of MAIs occurs, in part, in hippocampal neuronal axons and somata. While a number of pathways and processes are altered with brain aging, we report a coordinated induction of myelin-associated inhibitors of functional and structural plasticity only in cognitively impaired aged rats. Induction of MAIs may decrease stimulus-induced synaptic strengthening and structural remodeling, ultimately impairing synaptic mechanisms of spatial learning and memory and resulting in cognitive decline.     

Independent Component Analysis in Spiking Neurons

Although models based on independent component analysis (ICA) have been successful in explaining various properties of sensory coding in the cortex, it remains unclear how networks of spiking neurons using realistic plasticity rules can realize such computation. Here, we propose a biologically plausible mechanism for ICA-like learning with spiking neurons. Our model combines spike-timing dependent plasticity and synaptic scaling with an intrinsic plasticity rule that regulates neuronal excitability to maximize information transmission. We show that a stochastically spiking neuron learns one independent component for inputs encoded either as rates or using spike-spike correlations. Furthermore, different independent components can be recovered, when the activity of different neurons is decorrelated by adaptive lateral inhibition.     

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.     

Input-dependent learning rule for the memory of spatiotemporal sequences in hippocampal network with theta phase precession

Theta phase precession in rat hippocampal place cells is hypothesized to contribute to memory encoding of running experience in the sense that it provides the ideal timing for synaptic plasticity and enables the asymmetric associative connections under the Hebbian learning rule with asymmetric time window (Yamaguchi 2003). When the sequence of place fields is considered as the episodic memory of running experience, a given spatial route should be accurately stored in spite of differing overlap extent among place fields and varying running velocity. Using a hippocampal network model with phase precession and the Hebbian learning rule with asymmetric time window, we investigate the memory encoding of place field sequences in a single traversal experience. Computer experiments show that place fields cannot be stored correctly until an input-dependent feature is introduced into the learning rule. These experiments further indicate that there exists an optimum value for the saturation level of synaptic plasticity and the speed of synaptic plasticity in the learning rule, which are correlated with, respectively, the overlap extent of place field sequence and the running velocity of animal during traversal. A comparison of these results with biological evidences shows good agreement and suggests that behavior-dependent regulation of the learning rule is necessary for memory encoding.     

Spike-timing-dependent plasticity leads to gamma band responses in a neural network

Early gamma band responses of the human electroencephalogram have been identified as an early interface linking top-down and bottom-up processing. This was based on findings that observed strong sensitivity of this signal to stimulus size and at the same time, to processes of attention and memory. Here, we simulate these findings in a simple random network of biologically plausible spiking neurons. During a learning phase, different stimuli were presented to the network and the synaptic connections were modified according to a spike-timing-dependent plasticity learning rule. In a subsequent test phase, we stimulated the network with (i) patterns of different sizes to simulate bottom-up effects and (ii) with patterns that were or were not presented during the learning phase. The network displayed qualitatively similar behavior as early gamma band responses measured from the scalp of human subjects: there was a general increase in response strength with increasing stimulus size and stronger responses for learned stimuli. We demonstrated that within one neural architecture early gamma band responses can be modulated both by bottom-up factors and by basal learning mechanisms mediated via spike-timing-dependent plasticity.     

Leptin and its role in hippocampal synaptic plasticity

It is well documented that the hormone leptin plays a pivotal role in regulating food intake and body weight via its hypothalamic actions. However, leptin receptors are expressed throughout the brain with high levels found in the hippocampus. Evidence is accumulating that leptin has widespread actions on CNS function and in particular learning and memory. Recent studies have demonstrated that leptin-deficient or-insensitive rodents have impairments in hippocampal synaptic plasticity and in spatial memory tasks performed in the Morris water maze. Moreover, direct administration of leptin into the brain facilitates hippocampal long-term potentiation (LTP), and improves memory performance in mice. There is also evidence that, at the cellular level, leptin has the capacity to convert hippocampal short-term potentiation (STP) into LTP, via enhancing NMDA receptor function. Recent data indicates that leptin can also induce a novel form of NMDA receptor-dependent hippocampal long-term depression. Here, we review the evidence implicating a key role for the hormone leptin in modulating hippocampal synaptic plasticity and discuss the role of lipid signaling cascades in this process.     

Cell assembly dynamics in detailed and abstract attractor models of cortical associative memory

Summary During the last few decades we have seen a convergence among ideas and hypotheses regarding functional principles underlying human memory. Hebb’s now more than fifty years old conjecture concerning synaptic plasticity and cell assemblies, formalized mathematically as attractor neural networks, has remained among the most viable and productive theoretical frameworks. It suggests plausible explanations for Gestalt aspects of active memory like perceptual completion, reconstruction and rivalry. We review the biological plausibility of these theories and discuss some critical issues concerning their associative memory functionality in the light of simulation studies of models with palimpsest memory properties. The focus is on memory properties and dynamics of networks modularized in terms of cortical minicolumns and hypercolumns. Biophysical compartmental models demonstrate attractor dynamics that support cell assembly operations with fast convergence and low firing rates. Using a scaling model we obtain reasonable relative connection densities and amplitudes. An abstract attractor network model reproduces systems level psychological phenomena seen in human memory experiments as the Sternberg and von Restorff effects. We conclude that there is today considerable substance in Hebb’s theory of cell assemblies and its attractor network formulations, and that they have contributed to increasing our understanding of cortical associative memory function. The criticism raised with regard to biological and psychological plausibility as well as low storage capacity, slow retrieval etc has largely been disproved. Rather, this paradigm has gained further support from new experimental data as well as computational modeling.