Synapses and Memory Storage

The synapse is the functional unit of the brain. During the last several decades we have acquired a great deal of information on its structure, molecular components, and physiological function. It is clear that synapses are morphologically and molecularly diverse and that this diversity is recruited to different functions. One of the most intriguing findings is that the size of the synaptic response in not invariant, but can be altered by a variety of homo- and heterosynaptic factors such as past patterns of use or modulatory neurotransmitters. Perhaps the most difficult challenge in neuroscience is to design experiments that reveal how these basic building blocks of the brain are put together and how they are regulated to mediate the information flow through neural circuits that is necessary to produce complex behaviors and store memories. In this review we will focus on studies that attempt to uncover the role of synaptic plasticity in the regulation of whole-animal behavior by learning and memory.The idea that learning results from changes in the strength of the synapse was first suggested by Santiago Ramon y Cajal (1894) based on insights from his anatomical studies. That modulation of synaptic connectivity is a critical mechanism of learning was incorporated into more refined models by Hebb in the 1940s and 1950s. The experimental investigation of these intriguing conjectures required the development of behavioral systems in which one could examine changes in the neuronal components of a specific behavior during or after the modification of that behavior with learning (Kandel and Spencer 1968).     

Robust Short-Term Memory without Synaptic Learning

Short-term memory in the brain cannot in general be explained the way long-term memory can – as a gradual modification of synaptic weights – since it takes place too quickly. Theories based on some form of cellular bistability, however, do not seem able to account for the fact that noisy neurons can collectively store information in a robust manner. We show how a sufficiently clustered network of simple model neurons can be instantly induced into metastable states capable of retaining information for a short time (a few seconds). The mechanism is robust to different network topologies and kinds of neural model. This could constitute a viable means available to the brain for sensory and/or short-term memory with no need of synaptic learning. Relevant phenomena described by neurobiology and psychology, such as local synchronization of synaptic inputs and power-law statistics of forgetting avalanches, emerge naturally from this mechanism, and we suggest possible experiments to test its viability in more biological settings.     

The making of a memory mechanism

Long-Term Potentiation (LTP) is akind of synaptic plasticity that manycontemporary neuroscientists believe is acomponent in mechanisms of memory. This essaydescribes the discovery of LTP and thedevelopment of the LTP research program. Thestory begins in the 1950's with the discoveryof synaptic plasticity in the hippocampus (amedial temporal lobe structure now associatedwith memory), and it ends in 1973 with thepublication of three papers sketching thefuture course of the LTP research program. Themaking of LTP was a protracted affair.Hippocampal synaptic plasticity was initiallyencountered as an experimental tool, thenreported as a curiosity, and finally includedin the ontic store of the neurosciences. Earlyresearchers were not investigating thehippocampus in search of a memory mechanism;rather, they saw the hippocampus as a usefulexperimental model or as a structure implicatedin the etiology of epilepsy. The link betweenhippocampal synaptic plasticity and learning ormemory was a separate conceptual achievement.That link was formulated in at least threedifferent ways at different times: reductively(claiming that plasticity is identical tolearning), analogically (claiming thatplasticity is an example or model of learning),and mechanistically (claiming that plasticityis a component in learning or memorymechanisms). The hypothesized link withlearning or memory, coupled with developmentsin experimental techniques and preparations,shaped how researchers understood LTP itself.By 1973, the mechanistic formulation of thelink between LTP and memory provided anabstract framework around which findings frommultiple perspectives could be integrated intoa multifield research program.     

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     

Physical models of neural networks

The state of art in computer modelling of neural networks with associative memory is reviewed. The available experimental data are considered on learning and memory of small neural systems, on isolated synapses and on molecular level. Computer simulations demonstrate that realistic models of neural ensembles exhibit properties which can be interpreted as image recognition, categorization, learning, prototype forming, etc. A bilayer model of associative neural network is proposed. One layer corresponds to the short-term memory, the other one to the long-term memory. Patterns are stored in terms of the synaptic strength matrix. We have studied the relaxational dynamics of neurons firing and suppression within the short-term memory layer under the influence of the long-term memory layer. The interaction among the layers has found to create a number of novel stable states which are not the learning patterns. These synthetic patterns may consist of elements belonging to different non-intersecting learning patterns. Within the framework of a hypothesis of selective and definite coding of images in brain one can interpret the observed effect as the "idea? generating" process.     

Dynamics of memory representations in networks with novelty-facilitated synaptic plasticity

The ability to associate some stimuli while differentiating between others is an essential characteristic of biological memory. Theoretical models identify memories as attractors of neural network activity, with learning based on Hebb-like synaptic modifications. Our analysis shows that when network inputs are correlated, this mechanism results in overassociations, even up to several memories "merging" into one. To counteract this tendency, we introduce a learning mechanism that involves novelty-facilitated modifications, accentuating synaptic changes proportionally to the difference between network input and stored memories. This mechanism introduces a dependency of synaptic modifications on previously acquired memories, enabling a wide spectrum of memory associations, ranging from absolute discrimination to complete merging. The model predicts that memory representations should be sensitive to learning order, consistent with recent psychophysical studies of face recognition and electrophysiological experiments on hippocampal place cells. The proposed mechanism is compatible with a recent biological model of novelty-facilitated learning in hippocampal circuitry.     

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.     

The Functional and Molecular Properties,Physiological Functions,and Pathophysiological Roles of GluN2A in the Central Nervous System

The NMDA receptor, which is heavily involved in several human brain diseases, is a heteromeric ligand-gated ion channel that interacts with multiple intracellular proteins through the C-termini of different subunits. GluN2A and GluN2B are the two primary types of GluN2 subunits in the forebrain. During the developmental period, there is a switch from GluN2B- to GluN2A-containing NMDA receptors in synapses. In the adult brain, GluN2A exists at synaptic sites more abundantly than GluN2B. GluN2A plays important roles not only in synaptic plasticity but also in mediating physiological functions, such as learning and memory. GluN2A has also been involved in many common human diseases, such as cerebral ischemia, seizure disorder, Alzheimer’s disease, and systemic lupus erythematosus. The following review investigates the functional and molecular properties, physiological functions, and pathophysiological roles of the GluN2A subunit.     

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.     

A working memory model for serial order that stores information in the intrinsic excitability properties of neurons

Models for temporary information storage in neuronal populations are dominated by mechanisms directly dependent on synaptic plasticity. There are nevertheless other mechanisms available that are well suited for creating short-term memories. Here we present a model for working memory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. We show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity. For this we introduce a functional class of neurons, named gate interneurons, which can store information in their membrane dynamics and can literally act as gates routing the flow of activations in the principal neurons population. The presented model exhibits properties which are in close agreement with experimental results in working memory. Namely, the recall process plays an important role in stabilizing and prolonging the memory trace. This means that the stored information is correctly maintained as long as it is being used. Moreover, the working memory model is adequate for storing completely new information, in time windows compatible with the notion of “one-shot” learning (hundreds of milliseconds).     

Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia,Catastrophic Forgetting,and the Spacing Effect

Although already William James and, more explicitly, Donald Hebb''s theory of cell assemblies have suggested that activity-dependent rewiring of neuronal networks is the substrate of learning and memory, over the last six decades most theoretical work on memory has focused on plasticity of existing synapses in prewired networks. Research in the last decade has emphasized that structural modification of synaptic connectivity is common in the adult brain and tightly correlated with learning and memory. Here we present a parsimonious computational model for learning by structural plasticity. The basic modeling units are “potential synapses” defined as locations in the network where synapses can potentially grow to connect two neurons. This model generalizes well-known previous models for associative learning based on weight plasticity. Therefore, existing theory can be applied to analyze how many memories and how much information structural plasticity can store in a synapse. Surprisingly, we find that structural plasticity largely outperforms weight plasticity and can achieve a much higher storage capacity per synapse. The effect of structural plasticity on the structure of sparsely connected networks is quite intuitive: Structural plasticity increases the “effectual network connectivity”, that is, the network wiring that specifically supports storage and recall of the memories. Further, this model of structural plasticity produces gradients of effectual connectivity in the course of learning, thereby explaining various cognitive phenomena including graded amnesia, catastrophic forgetting, and the spacing effect.     

A Novel Whole-Cell Mechanism for Long-Term Memory Enhancement

Olfactory-discrimination learning was shown to induce a profound long-lasting enhancement in the strength of excitatory and inhibitory synapses of pyramidal neurons in the piriform cortex. Notably, such enhancement was mostly pronounced in a sub-group of neurons, entailing about a quarter of the cell population. Here we first show that the prominent enhancement in the subset of cells is due to a process in which all excitatory synapses doubled their strength and that this increase was mediated by a single process in which the AMPA channel conductance was doubled. Moreover, using a neuronal-network model, we show how such a multiplicative whole-cell synaptic strengthening in a sub-group of cells that form a memory pattern, sub-serves a profound selective enhancement of this memory. Network modeling further predicts that synaptic inhibition should be modified by complex learning in a manner that much resembles synaptic excitation. Indeed, in a subset of neurons all GABA_A-receptors mediated inhibitory synapses also doubled their strength after learning. Like synaptic excitation, Synaptic inhibition is also enhanced by two-fold increase of the single channel conductance. These findings suggest that crucial learning induces a multiplicative increase in strength of all excitatory and inhibitory synapses in a subset of cells, and that such an increase can serve as a long-term whole-cell mechanism to profoundly enhance an existing Hebbian-type memory. This mechanism does not act as synaptic plasticity mechanism that underlies memory formation but rather enhances the response of already existing memory. This mechanism is cell-specific rather than synapse-specific; it modifies the channel conductance rather than the number of channels and thus has the potential to be readily induced and un-induced by whole-cell transduction mechanisms.     

A behavioral role for dendritic integration: HCN1 channels constrain spatial memory and plasticity at inputs to distal dendrites of CA1 pyramidal neurons

The importance of long-term synaptic plasticity as a cellular substrate for learning and memory is well established. By contrast, little is known about how learning and memory are regulated by voltage-gated ion channels that integrate synaptic information. We investigated this question using mice with general or forebrain-restricted knockout of the HCN1 gene, which we find encodes a major component of the hyperpolarization-activated inward current (Ih) and is an important determinant of dendritic integration in hippocampal CA1 pyramidal cells. Deletion of HCN1 from forebrain neurons enhances hippocampal-dependent learning and memory, augments the power of theta oscillations, and enhances long-term potentiation (LTP) at the direct perforant path input to the distal dendrites of CA1 pyramidal neurons, but has little effect on LTP at the more proximal Schaffer collateral inputs. We suggest that HCN1 channels constrain learning and memory by regulating dendritic integration of distal synaptic inputs to pyramidal cells.     

Role for brain-derived neurotrophic factor in learning and memory

In addition to its actions on neuronal survival and differentiation, brain-derived neurotrophic factor (BDNF) has a role in the regulation of synaptic strength. Long-term potentiation, a form of synaptic plasticity, is markedly impaired in BDNF mutant mice, but the changes were restored by the re-expression of BDNF. BDNF also influences the development of patterned connections and the growth and complexity of dendrites in the cerebral cortex. These results suggest a role for BDNF in learning and memory processes, since memory acquisition is considered to involve both short-term changes in electrical properties and long-term structural alterations in synapses. Memory acquisition is associated with an increase in BDNF mRNA and TrkB receptor activation in specific brain areas. Moreover, the pharmacologic and genetic deprivation of BDNF or its receptor TrkB results in severe impairment of learning and memory in mice, rats and chicks. The effect of BDNF on learning and memory may be linked to the modulation of NMDA and non-NMDA receptor functions as well as the expression of synaptic proteins required for exocytosis. Activation of the mitogen-associated protein kinase and/or phosphatidylinositol 3-kinase signaling pathways may be involved in BDNF-dependent learning and memory formation. It is concluded that BDNF/TrkB signaling plays an important role in learning and memory.     

Synaptic conditions for auto-associative memory storage and pattern completion in Jensen et al.’s model of hippocampal area CA3

Jensen et al. (Learn Memory 3(2–3):243–256, 1996b) proposed an auto-associative memory model using an integrated short-term memory (STM) and long-term memory (LTM) spiking neural network. Their model requires that distinct pyramidal cells encoding different STM patterns are fired in different high-frequency gamma subcycles within each low-frequency theta oscillation. Auto-associative LTM is formed by modifying the recurrent synaptic efficacy between pyramidal cells. In order to store auto-associative LTM correctly, the recurrent synaptic efficacy must be bounded. The synaptic efficacy must be upper bounded to prevent re-firing of pyramidal cells in subsequent gamma subcycles. If cells encoding one memory item were to re-fire synchronously with other cells encoding another item in subsequent gamma subcycle, LTM stored via modifiable recurrent synapses would be corrupted. The synaptic efficacy must also be lower bounded so that memory pattern completion can be performed correctly. This paper uses the original model by Jensen et al. as the basis to illustrate the following points. Firstly, the importance of coordinated long-term memory (LTM) synaptic modification. Secondly, the use of a generic mathematical formulation (spiking response model) that can theoretically extend the results to other spiking network utilizing threshold-fire spiking neuron model. Thirdly, the interaction of long-term and short-term memory networks that possibly explains the asymmetric distribution of spike density in theta cycle through the merger of STM patterns with interaction of LTM network.     

Multiple forms of long-term potentiation and multiple regulatory sites of N-methyl-D-aspartate receptors: Role of the redox site

Long-term potentiation (LTP) is a form of synaptic plasticity thought to be involved in learning and memory. Althrough extensively studied, mainly in the CA1 region of the hippocampus, the mechanisms underlying the induction and expression of LTP are poorly elucidated. This is probably due to the fact that LTP is not a unique process and indeed recent studies have shown that several forms of LTP could be generated depending on the experimental conditions. Furthermore, LTP is generally associated with a long-lasting increase of the synaptic efficacy of AMPA receptors but an increasing number of data also suggested that NMDA receptors could be potentiated as well. NMDA receptor responses are modulated by a large number of extracellular and intracellular events, providing additional possibilities for the generation of LTP. The role of these different modulatory sites of the NMDA receptor and their relation with LTP are reviewed with a particular attention to the redox site which seems to be a selective target to distinguish between AMPA and NMDA-LTP. © 1995 John Wiley & Sons, Inc.     

与学习记忆相关的睡眠新功能——突触稳态

近年来的许多研究证实睡眠有利于学习和记忆．不但学习后的睡眠具有记忆巩固功能,而且学习前的睡眠对于随后的学习也是必需的．长时间觉醒学习后脑内突触连接增多、增强,导致突触空间饱和,阻碍随后继续学习．睡眠的作用是减弱突触连接到基础水平,为随后的学习记忆提供充足的空间和能量．     

Hippocampal synaptic plasticity: role in spatial learning or the automatic recording of attended experience?

Allocentric spatial learning can sometimes occur in one trial. The incorporation of information into a spatial representation may, therefore, obey a one-trial correlational learning rule rather than a multi-trial error-correcting rule. It has been suggested that physiological implementation of such a rule could be mediated by N-methyl-D-aspartate (NMDA) receptor-dependent long-term potentiation (LTP) in the hippocampus, as its induction obeys a correlational type of synaptic learning rule. Support for this idea came originally from the finding that intracerebral infusion of the NMDA antagonist AP5 impairs spatial learning, but studies summarized in the first part of this paper have called it into question. First, rats previously given experience of spatial learning in a watermaze can learn a new spatial reference memory task at a normal rate despite an appreciable NMDA receptor blockade. Second, the classical phenomenon of ''blocking'' occurs in spatial learning. The latter finding implies that spatial learning can also be sensitive to an animal''s expectations about reward and so depend on more than the detection of simple spatial correlations. In this paper a new hypothesis is proposed about the function of hippocampal LTP. This hypothesis retains the idea that LTP subserves rapid one-trial memory, but abandons the notion that it serves any specific role in the geometric aspects of spatial learning. It is suggested that LTP participates in the automatic recording of attended experience'': a subsystem of episodic memory in which events are temporarily remembered in association with the contexts in which they occur. An automatic correlational form of synaptic plasticity is ideally suited to the online registration of context event associations. In support, it is reported that the ability of rats to remember the most recent place they have visited in a familiar environment is exquisitely sensitive to AP5 in a delay-dependent manner. Moreover, new studies of the lasting persistence of NMDA-dependent LTP, known to require protein synthesis, point to intracellular mechanisms that enable transient synaptic changes to be stabilized if they occur in close temporal proximity to important events. This new property of hippocampal LTP is a desirable characteristic of an event memory system.     

A cooperative switch determines the sign of synaptic plasticity in distal dendrites of neocortical pyramidal neurons

Pyramidal neurons in the cerebral cortex span multiple cortical layers. How the excitable properties of pyramidal neuron dendrites allow these neurons to both integrate activity and store associations between different layers is not well understood, but is thought to rely in part on dendritic backpropagation of action potentials. Here we demonstrate that the sign of synaptic plasticity in neocortical pyramidal neurons is regulated by the spread of the backpropagating action potential to the synapse. This creates a progressive gradient between LTP and LTD as the distance of the synaptic contacts from the soma increases. At distal synapses, cooperative synaptic input or dendritic depolarization can switch plasticity between LTD and LTP by boosting backpropagation of action potentials. This activity-dependent switch provides a mechanism for associative learning across different neocortical layers that process distinct types of information.