抑郁症模型大鼠学习记忆能力变化研究

为探讨抑郁症发生发展过程中学习记忆能力的变化模式及其可能机制．分别采用21天慢性非预见性刺激法和嗅球切除法建立的抑郁症模型大鼠．运用旷场行为实验（open—field behavior）检测大鼠主动性活动能力,用Morris水迷宫法检测大鼠空间学习记忆能力,HPLC—UV法测定大鼠血清皮质醇含量。电生理法记录海马CA1区LTP与LTD,观察海马神经元的突触可塑性。结果显示：与对照组相比,两种模型的自主活动性、空间探索兴趣和学习能力都明显降低,而记忆的反馈功能没有明显的变化。同时．两种模型大鼠海马神经细胞的突触可塑性显著下降,血清皮质醇的含量则明显上升。提示两种建模方法均导致大鼠产生抑郁症状和学习能力障碍．但对记忆反馈功能无明显影响。     

Phenomenological models of synaptic plasticity based on spike timing

Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of the document is to provide a framework for classifying and evaluating different models of plasticity. We focus on phenomenological synaptic models that are compatible with integrate-and-fire type neuron models where each neuron is described by a small number of variables. This implies that synaptic update rules for short-term or long-term plasticity can only depend on spike timing and, potentially, on membrane potential, as well as on the value of the synaptic weight, or on low-pass filtered (temporally averaged) versions of the above variables. We examine the ability of the models to account for experimental data and to fulfill expectations derived from theoretical considerations. We further discuss their relations to teacher-based rules (supervised learning) and reward-based rules (reinforcement learning). All models discussed in this paper are suitable for large-scale network simulations.     

Memory retention in pyramidal neurons: a unified model of energy-based homo and heterosynaptic plasticity with homeostasis

The brain can learn new tasks without forgetting old ones. This memory retention is closely associated with the long-term stability of synaptic strength. To understand the capacity of pyramidal neurons to preserve memory under different tasks, we established a plasticity model based on the postsynaptic membrane energy state, in which the change in synaptic strength depends on the difference between the energy state after stimulation and the resting energy state. If the post-stimulation energy state is higher than the resting energy state, then synaptic depression occurs. On the contrary, the synapse is strengthened. Our model unifies homo- and heterosynaptic plasticity and can reproduce synaptic plasticity observed in multiple experiments, such as spike-timing-dependent plasticity, and cooperative plasticity with few and common parameters. Based on the proposed plasticity model, we conducted a simulation study on how the activation patterns of dendritic branches by different tasks affect the synaptic connection strength of pyramidal neurons. We further investigate the formation mechanism by which different tasks activate different dendritic branches. Simulation results show that compare to the classic plasticity model, the plasticity model we proposed can achieve a better spatial separation of different branches activated by different tasks in pyramidal neurons, which deepens our insight into the memory retention mechanism of brains.     

Models for the study of memory and neurosteroids]

The steroids dehydroepiandrosterone sulfate (DHEA-S) and pregnenolone sulfate (Preg-S) are naturally synthetized in the brain. They improve short term and long term memory performances in a variety of learning tasks and models of amnesia in rodents. DHEA-S and Preg-S modulate GABAergic and glutamatergic synaptic transmission through direct interactions with GABA-A, NMDA and/or sigma 1 membrane receptors. In addition, these two neurosteroids facilitate the release of acetylcholine and modulate synaptic plasticity phenomena in cerebral structures, such as the hippocampus, known to play a role in learning and memory processes. The possible links between these actions and the promnestic effects of DHEA-S and Preg-S are discussed in the present review.     

Learning,AMPA receptor mobility and synaptic plasticity depend on n‐cofilin‐mediated actin dynamics

Neuronal plasticity is an important process for learning, memory and complex behaviour. Rapid remodelling of the actin cytoskeleton in the postsynaptic compartment is thought to have an important function for synaptic plasticity. However, the actin‐binding proteins involved and the molecular mechanisms that in vivo link actin dynamics to postsynaptic physiology are not well understood. Here, we show that the actin filament depolymerizing protein n‐cofilin is controlling dendritic spine morphology and postsynaptic parameters such as late long‐term potentiation and long‐term depression. Loss of n‐cofilin‐mediated synaptic actin dynamics in the forebrain specifically leads to impairment of all types of associative learning, whereas exploratory learning is not affected. We provide evidence for a novel function of n‐cofilin function in synaptic plasticity and in the control of extrasynaptic excitatory AMPA receptors diffusion. These results suggest a critical function of actin dynamics in associative learning and postsynaptic receptor availability.     

神经元的突触可塑性与学习和记忆

大量研究表明,神经元的突触可塑性包括功能可塑性和结构可塑性,与学习和记忆密切相关.最近,在经过训练的动物海马区,记录到了学习诱导的长时程增强(long term potentiation,LTP),如果用激酶抑制剂阻断晚期LTP,就会使大鼠丧失训练形成的记忆.这些结果指出,LTP可能是形成记忆的分子基础.因此,进一步研究哺乳动物脑内突触可塑性的分子机制,对揭示学习和记忆的神经基础有重要意义.此外,在精神迟滞性疾病和神经退行性疾病患者脑内记录到异常的LTP,并发现神经元的树突棘数量减少,形态上产生畸变或萎缩,同时发现,产生突变的基因大多编码调节突触可塑性的信号通路蛋白,故突触可塑性研究也将促进精神和神经疾病的预防和治疗.综述了突触可塑性研究的最新进展,并展望了其发展前景.     

Amphetamine-Induced Sensitization Has Little Effect on Multiple Learning Paradigms and Fails to Rescue Mice with a Striatal Learning Defect

Behavioral sensitization to psychostimulants such as amphetamine (AMPH) is associated with synaptic modifications that are thought to underlie learning and memory. Because AMPH enhances extracellular dopamine in the striatum where dopamine and glutamate signaling are essential for learning, one might expect that the molecular and morphological changes that occur in the striatum in response to AMPH, including changes in synaptic plasticity, would affect learning. To ascertain whether AMPH sensitization affects learning, we tested wild-type mice and mice lacking NMDA receptor signaling in striatal medium spiny neurons in several different learning tests (motor learning, Pavlovian association, U-maze escape test with strategy shifting) with or without prior sensitization to AMPH. Prior sensitization had minimal effect on learning in any of these paradigms in wild-type mice and failed to restore learning in mutant mice, despite the fact that the mutant mice became sensitized by the AMPH treatment. We conclude that the changes in synaptic plasticity and many other signaling events that occur in response to AMPH sensitization are dissociable from those involved in learning the tasks used in our experiments.     

Episodic-like memory in animals: psychological criteria, neural mechanisms and the value of episodic-like tasks to investigate animal models of neurodegenerative disease

The question of whether any non-human species displays episodic memory is controversial. Associative accounts of animal learning recognize that behaviour can change in response to single events but this does not imply that animals need or are later able to recall representations of unique events at a different time and place. The lack of language is also relevant, being the usual medium for communicating about the world, but whether it is critical for the capacity to represent and recall events is a separate matter. One reason for suspecting that certain animals possess an episodic-like memory system is that a variety of learning and memory tasks have been developed that, even though they do not meet the strict criteria required for episodic memory, have an 'episodic-like' character. These include certain one-trial learning tasks, scene-specific discrimination learning, multiple reversal learning, delayed matching and non-matching tasks and, most recently, tasks demanding recollection of 'what, where and when' an event happened. Another reason is that the neuronal architecture of brain areas thought to be involved in episodic memory (including the hippocampal formation) are substantially similar in mammals and, arguably, all vertebrates. Third, our developing understanding of activity-dependent synaptic plasticity (which is a candidate neuronal mechanism for encoding memory traces) suggests that its expression reflects certain physiological characteristics that are ideal components of a neuronal episodic memory system. These include the apparently digital character of synaptic change at individual terminals and the variable persistence of potentiation accounted for by the synaptic tag hypothesis. A further value of studying episodic-like memory in animals is the opportunity it affords to model certain kinds of neurodegenerative disease that, in humans, affect episodic memory. An example is recent work on a transgenic mouse that over-expresses a mutation of human amyloid precursor protein (APP) that occurs in familial Alzheimer's disease, under the control of platelet derived (PD) growth factor promoter (the PDAPP mouse). A striking age- and amyloid plaque-related deficit is seen using a task in which the mice have to keep changing their memory representation of the world rather than learn a single fact.     

Synaptic Potentiation Facilitates Memory-like Attractor Dynamics in Cultured In Vitro Hippocampal Networks

Collective rhythmic dynamics from neurons is vital for cognitive functions such as memory formation but how neurons self-organize to produce such activity is not well understood. Attractor-based computational models have been successfully implemented as a theoretical framework for memory storage in networks of neurons. Additionally, activity-dependent modification of synaptic transmission is thought to be the physiological basis of learning and memory. The goal of this study is to demonstrate that using a pharmacological treatment that has been shown to increase synaptic strength within in vitro networks of hippocampal neurons follows the dynamical postulates theorized by attractor models. We use a grid of extracellular electrodes to study changes in network activity after this perturbation and show that there is a persistent increase in overall spiking and bursting activity after treatment. This increase in activity appears to recruit more “errant” spikes into bursts. Phase plots indicate a conserved activity pattern suggesting that a synaptic potentiation perturbation to the attractor leaves it unchanged. Lastly, we construct a computational model to demonstrate that these synaptic perturbations can account for the dynamical changes seen within the network.     

受体、突触和记忆

大脑中神经元突触间的信号传递是由许多神经递质受体介导的。在过去,Richard L．Huganir实验室一直致力于神经递质受体功能调节的分子机制。而最近,该实验室又聚焦到大脑中一种最主要的兴奋性受体的研究——谷氨酸受体。谷氨酸受体主要可以分为两大类：AMPA受体和NMDA受体。AMPA受体主要介导了快速的兴奋性突触传递;而NMDA受体则在神经可塑性和发育中起到重要作用。实验发现,AMPA受体和NMDA受体都可以被一系列的蛋白激酶磷酸化,而磷酸化的水平则直接影响了这些受体的功能特性,包括通道电导和受体膜定位等。AMPA受体磷酸化的水平同时还在学习和记忆的细胞模型中发生改变,如长时程增强（LTP）和长时程抑制（LTD）。此外,AMPA受体中GluR1亚单位的磷酸化对于各种形式的可塑性以及空间记忆的维持有重要的作用。实验室主要研究突触部位谷氨酸受体在亚细胞水平的定位和聚集的分子机制。最近,一系列可以直接或间接与AMPA和NMDA受体相互作用的蛋白质得以发现,其中包括一个新发现的蛋白家族GRIPs（glutamate receptor interacting proteins）。GRIPs可以直接和AMPA受体的GluR2／3亚单位的C端结合。GRIPs包含7个PDZ结构域,可以介导蛋白与蛋白直接的相互连接,从而把各个AMPA受体交互连接在一起并与其他蛋白相连。另外,GluR2亚单位的c端还可以和兴奋性突触中的蛋白激酶C结合蛋白（PICK1）的PDZ结构域相互作用。另外,GluR2亚单位的C端也可以与一种参与膜融合的蛋白NSF相互作用。这些与AMPA受体相互作用的蛋白质对于受体在膜上的运输以及定位有至关重要的作用。同时,受体与PICK1和GRIP的结合对于小脑运动学习中的LTD有重要作用。总体上说,该实验室发现了一系列可以调节神经递质受体功能的分子机制,这些工作提示受体功能的调节可能是?     

Hormonal regulation of hippocampal dendritic morphology and synaptic plasticity

The peripheral functions of hormones such as leptin, insulin and estrogens are well documented. An important and rapidly expanding field is demonstrating that as well as their peripheral actions, these hormones play an important role in modulating synaptic function and structure within the CNS. The hippocampus is a major mediator of spatial learning and memory and is also an area highly susceptible to epileptic seizure. As such, the hippocampus has been extensively studied with particular regard to synaptic plasticity, a process thought to be necessary for learning and memory. Modulators of hippocampal function are therefore of particular interest, not only as potential modulators of learning and memory processes, but also with regard to CNS driven diseases such as epilepsy. Hormones traditionally thought of as only having peripheral roles are now increasingly being shown to have an important role in modulating synaptic plasticity and dendritic morphology. Here we review recent findings demonstrating that a number of hormones are capable of modulating both these phenomena.Key words: synaptic plasticity, leptin, estrogen, insulin, hippocampus, LTD, LTP     

Self-selected modular recurrent neural networks with postural and inertial subnetworks applied to complex movements

 It has been shown that dynamic recurrent neural networks are successful in identifying the complex mapping relationship between full-wave-rectified electromyographic (EMG) signals and limb trajectories during complex movements. These connectionist models include two types of adaptive parameters: the interconnection weights between the units and the time constants associated to each neuron-like unit; they are governed by continuous-time equations. Due to their internal structure, these models are particularly appropriate to solve dynamical tasks (with time-varying input and output signals). We show in this paper that the introduction of a modular organization dedicated to different aspects of the dynamical mapping including privileged communication channels can refine the architecture of these recurrent networks. We first divide the initial individual network into two communicating subnetworks. These two modules receive the same EMG signals as input but are involved in different identification tasks related to position and acceleration. We then show that the introduction of an artificial distance in the model (using a Gaussian modulation factor of weights) induces a reduced modular architecture based on a self-elimination of null synaptic weights. Moreover, this self-selected reduced model based on two subnetworks performs the identification task better than the original single network while using fewer free parameters (better learning curve and better identification quality). We also show that this modular network exhibits several features that can be considered as biologically plausible after the learning process: self-selection of a specific inhibitory communicating path between both subnetworks after the learning process, appearance of tonic and phasic neurons, and coherent distribution of the values of the time constants within each subnetwork. Received: 17 September 2001 / Accepted in revised form: 15 January 2002     

A computational study of synaptic mechanisms of partial memory transfer in cerebellar vestibulo-ocular-reflex learning

There is a debate regarding whether motor memory is stored in the cerebellar cortex, or the cerebellar nuclei, or both. Memory may be acquired in the cortex and then be transferred to the cerebellar nuclei. Based on a dynamical system modeling with a minimal set of variables, we theoretically investigated possible mechanisms of memory transfer and consolidation in the context of vestibulo-ocular reflex learning. We tested different plasticity rules for synapses in the cerebellar nuclei and took robustness of behavior against parameter variation as the criterion of plausibility of a model variant. In the most plausible scenarios, mossy-fiber nucleus-neuron synapses or Purkinje-cell nucleus-neuron synapses are plastic on a slow time scale and store permanent memory, whose content is passed from the cerebellar cortex storing transient memory. In these scenarios, synaptic strengths are potentiated when the mossy-fiber afferents to the nuclei are active during a pause in Purkinje-cell activities. Furthermore, assuming that mossy fibers create a limited variety of signals compared to parallel fibers, our model shows partial memory transfer from the cortex to the nuclei.     

Impaired associative learning in schizophrenia: behavioral and computational studies

Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto–hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits. We present a preliminary computational model designed to capture learning dynamics in healthy control and schizophrenia subjects. Experimental data was collected on a spatial-object paired-associate learning task. The task evinces classic patterns of negatively accelerated learning in both healthy control subjects and patients, with patients demonstrating lower rates of learning than controls. Our rudimentary computational model of the task was based on biologically plausible assumptions, including the separation of dorsal/spatial and ventral/object visual streams, implementation of rules of learning, the explicit parameterization of learning rates (a plausible surrogate for synaptic plasticity), and learning capacity (a plausible surrogate for memory capacity). Reductions in learning dynamics in schizophrenia were well-modeled by reductions in learning rate and learning capacity. The synergy between experimental research and a detailed computational model of performance provides a framework within which to infer plausible biological bases of impaired learning dynamics in schizophrenia.     

Are binary synapses superior to graded weight representations in stochastic attractor networks?

Synaptic plasticity is an underlying mechanism of learning and memory in neural systems, but it is controversial whether synaptic efficacy is modulated in a graded or binary manner. It has been argued that binary synaptic weights would be less susceptible to noise than graded weights, which has impelled some theoretical neuroscientists to shift from the use of graded to binary weights in their models. We compare retrieval performance of models using both binary and graded weight representations through numerical simulations of stochastic attractor networks. We also investigate stochastic attractor models using multiple discrete levels of weight states, and then investigate the optimal threshold for dilution of binary weight representations. Our results show that a binary weight representation is not less susceptible to noise than a graded weight representation in stochastic attractor models, and we find that the load capacities with an increasing number of weight states rapidly reach the load capacity with graded weights. The optimal threshold for dilution of binary weight representations under stochastic conditions occurs when approximately 50% of the smallest weights are set to zero.     

Spatio-temporal credit assignment in neuronal population learning

In learning from trial and error, animals need to relate behavioral decisions to environmental reinforcement even though it may be difficult to assign credit to a particular decision when outcomes are uncertain or subject to delays. When considering the biophysical basis of learning, the credit-assignment problem is compounded because the behavioral decisions themselves result from the spatio-temporal aggregation of many synaptic releases. We present a model of plasticity induction for reinforcement learning in a population of leaky integrate and fire neurons which is based on a cascade of synaptic memory traces. Each synaptic cascade correlates presynaptic input first with postsynaptic events, next with the behavioral decisions and finally with external reinforcement. For operant conditioning, learning succeeds even when reinforcement is delivered with a delay so large that temporal contiguity between decision and pertinent reward is lost due to intervening decisions which are themselves subject to delayed reinforcement. This shows that the model provides a viable mechanism for temporal credit assignment. Further, learning speeds up with increasing population size, so the plasticity cascade simultaneously addresses the spatial problem of assigning credit to synapses in different population neurons. Simulations on other tasks, such as sequential decision making, serve to contrast the performance of the proposed scheme to that of temporal difference-based learning. We argue that, due to their comparative robustness, synaptic plasticity cascades are attractive basic models of reinforcement learning in the brain.     

Memory Capacity of Networks with Stochastic Binary Synapses

In standard attractor neural network models, specific patterns of activity are stored in the synaptic matrix, so that they become fixed point attractors of the network dynamics. The storage capacity of such networks has been quantified in two ways: the maximal number of patterns that can be stored, and the stored information measured in bits per synapse. In this paper, we compute both quantities in fully connected networks of N binary neurons with binary synapses, storing patterns with coding level , in the large and sparse coding limits (). We also derive finite-size corrections that accurately reproduce the results of simulations in networks of tens of thousands of neurons. These methods are applied to three different scenarios: (1) the classic Willshaw model, (2) networks with stochastic learning in which patterns are shown only once (one shot learning), (3) networks with stochastic learning in which patterns are shown multiple times. The storage capacities are optimized over network parameters, which allows us to compare the performance of the different models. We show that finite-size effects strongly reduce the capacity, even for networks of realistic sizes. We discuss the implications of these results for memory storage in the hippocampus and cerebral cortex.