Do stress and long-term potentiation share the same molecular mechanisms?

Stress is a biological, significant factor shown to influence hippocampal synaptic plasticity and cognitive functions. Although numerous studies have reported that stress produces a suppression in long-term potentiation (LTP; a putative synaptic mechanism underlying learning and memory), little is known about the mechanism by which this occurs. Because the effects of stress on LTP and its converse process, long-term depression (LTD), parallel the changes in synapticity that occur following the establishment of LTP with tetanic stimulation (i.e., occluding LTP and enhancing LTD induction), it has been proposed that stress affects subsequent hippocampal plasticity by sharing the same molecular machinery required to support LTP. This article summarizes recent findings from ours and other laboratories to assess this view and discusses relevant hypotheses in the study of stress-related modifications of synaptic plasticity.     

Memory Maintenance in Synapses with Calcium-Based Plasticity in the Presence of Background Activity

Most models of learning and memory assume that memories are maintained in neuronal circuits by persistent synaptic modifications induced by specific patterns of pre- and postsynaptic activity. For this scenario to be viable, synaptic modifications must survive the ubiquitous ongoing activity present in neural circuits in vivo. In this paper, we investigate the time scales of memory maintenance in a calcium-based synaptic plasticity model that has been shown recently to be able to fit different experimental data-sets from hippocampal and neocortical preparations. We find that in the presence of background activity on the order of 1 Hz parameters that fit pyramidal layer 5 neocortical data lead to a very fast decay of synaptic efficacy, with time scales of minutes. We then identify two ways in which this memory time scale can be extended: (i) the extracellular calcium concentration in the experiments used to fit the model are larger than estimated concentrations in vivo. Lowering extracellular calcium concentration to in vivo levels leads to an increase in memory time scales of several orders of magnitude; (ii) adding a bistability mechanism so that each synapse has two stable states at sufficiently low background activity leads to a further boost in memory time scale, since memory decay is no longer described by an exponential decay from an initial state, but by an escape from a potential well. We argue that both features are expected to be present in synapses in vivo. These results are obtained first in a single synapse connecting two independent Poisson neurons, and then in simulations of a large network of excitatory and inhibitory integrate-and-fire neurons. Our results emphasise the need for studying plasticity at physiological extracellular calcium concentration, and highlight the role of synaptic bi- or multistability in the stability of learned synaptic structures.     

Mapping molecular memory: navigating the cellular pathways of learning

A consolidated map of the signalling pathways that function in the formation of short- and long-term cellular memory could be considered the ultimate means of defining the molecular basis of learning. Research has established that experience-dependent activation of these complex cellular cascades leads to many changes in the composition and functioning of a neuron's proteome, resulting in the modulation of its synaptic strength and structure. However, although generally accepted that synaptic plasticity is the mechanism whereby memories are stored in the brain, there is much controversy over whether the site of this neuronal memory expression is predominantly pre- or postsynaptic. Much of the early research into the neuromolecular mechanisms of memory performed using the model organism, the marine snail Aplysia, has focused on the associated presynaptic events. Recently however, postsynaptic mechanisms have been shown to contribute definitively to long term memory processes, and are in fact critical for persistent learning-induced synaptic changes. In this review, in which we aimed to integrate many of the early and recent advances concerning coordinated neuronal signaling in both the pre- and postsynaptic neurons, we have provided a detailed account of the diverse cellular events that lead to modifications in synaptic strength. Thus, a comprehensive synaptic model is presented that could explain a few of the shortcomings that arise when the presynaptic and postsynaptic changes are considered separately. Although it is clear that there is still much to be learnt and that the exact nature of many of the signalling cascades and their components are yet to be fully understood, this still incomplete but integrated illustrative map of the cellular pathways involved provides an overview which expands understanding of the neuromolecular mechanisms of learning and memory.     

Hebbian learning in parallel and modular memories

Many cognitive and sensorimotor functions in the brain involve parallel and modular memory subsystems that are adapted by activity-dependent Hebbian synaptic plasticity. This is in contrast to the multilayer perceptron model of supervised learning where sensory information is presumed to be integrated by a common pool of hidden units through backpropagation learning. Here we show that Hebbian learning in parallel and modular memories is more advantageous than backpropagation learning in lumped memories in two respects: it is computationally much more efficient and structurally much simpler to implement with biological neurons. Accordingly, we propose a more biologically relevant neural network model, called a tree-like perceptron, which is a simple modification of the multilayer perceptron model to account for the general neural architecture, neuronal specificity, and synaptic learning rule in the brain. The model features a parallel and modular architecture in which adaptation of the input-to-hidden connection follows either a Hebbian or anti-Hebbian rule depending on whether the hidden units are excitatory or inhibitory, respectively. The proposed parallel and modular architecture and implicit interplay between the types of synaptic plasticity and neuronal specificity are exhibited by some neocortical and cerebellar systems. Received: 13 October 1996 / Accepted in revised form: 16 October 1997     

Spatial learning and action planning in a prefrontal cortical network model

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive "insight" capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates.     

Epigenetic mechanisms in cognition

Although the critical role for epigenetic mechanisms in development and cell differentiation has long been appreciated, recent evidence reveals that these mechanisms are also employed in postmitotic neurons as a means of consolidating and stabilizing cognitive-behavioral memories. In this review, we discuss evidence for an "epigenetic code" in the central nervous system that mediates synaptic plasticity, learning, and memory. We consider how specific epigenetic changes are regulated and may interact with each other during memory formation and how these changes manifest functionally at the cellular and circuit levels. We also describe a central role for mitogen-activated protein kinases in controlling chromatin signaling in plasticity and memory. Finally, we consider how aberrant epigenetic modifications may lead to cognitive disorders that affect learning and memory, and we review the therapeutic potential of epigenetic treatments for the amelioration of these conditions.     

Hebbian plasticity in parallel synaptic pathways: A circuit mechanism for systems memory consolidation

Systems memory consolidation involves the transfer of memories across brain regions and the transformation of memory content. For example, declarative memories that transiently depend on the hippocampal formation are transformed into long-term memory traces in neocortical networks, and procedural memories are transformed within cortico-striatal networks. These consolidation processes are thought to rely on replay and repetition of recently acquired memories, but the cellular and network mechanisms that mediate the changes of memories are poorly understood. Here, we suggest that systems memory consolidation could arise from Hebbian plasticity in networks with parallel synaptic pathways—two ubiquitous features of neural circuits in the brain. We explore this hypothesis in the context of hippocampus-dependent memories. Using computational models and mathematical analyses, we illustrate how memories are transferred across circuits and discuss why their representations could change. The analyses suggest that Hebbian plasticity mediates consolidation by transferring a linear approximation of a previously acquired memory into a parallel pathway. Our modelling results are further in quantitative agreement with lesion studies in rodents. Moreover, a hierarchical iteration of the mechanism yields power-law forgetting—as observed in psychophysical studies in humans. The predicted circuit mechanism thus bridges spatial scales from single cells to cortical areas and time scales from milliseconds to years.     

Theta Coordinated Error-Driven Learning in the Hippocampus

The learning mechanism in the hippocampus has almost universally been assumed to be Hebbian in nature, where individual neurons in an engram join together with synaptic weight increases to support facilitated recall of memories later. However, it is also widely known that Hebbian learning mechanisms impose significant capacity constraints, and are generally less computationally powerful than learning mechanisms that take advantage of error signals. We show that the differential phase relationships of hippocampal subfields within the overall theta rhythm enable a powerful form of error-driven learning, which results in significantly greater capacity, as shown in computer simulations. In one phase of the theta cycle, the bidirectional connectivity between CA1 and entorhinal cortex can be trained in an error-driven fashion to learn to effectively encode the cortical inputs in a compact and sparse form over CA1. In a subsequent portion of the theta cycle, the system attempts to recall an existing memory, via the pathway from entorhinal cortex to CA3 and CA1. Finally the full theta cycle completes when a strong target encoding representation of the current input is imposed onto the CA1 via direct projections from entorhinal cortex. The difference between this target encoding and the attempted recall of the same representation on CA1 constitutes an error signal that can drive the learning of CA3 to CA1 synapses. This CA3 to CA1 pathway is critical for enabling full reinstatement of recalled hippocampal memories out in cortex. Taken together, these new learning dynamics enable a much more robust, high-capacity model of hippocampal learning than was available previously under the classical Hebbian model.     

Removing pathogenic memories

Experimental research examining the neural bases of nondeclarative memory has offered intriguing insight into how functional and dysfunctional implicit learning affects the brain. Long-term modifications of synaptic transmission, in particular, are currently considered the most plausible mechanism underlying memory trace encoding and compulsions, addiction, anxiety, and phobias. Therefore, an effective psychotherapy must be directed to erase maladaptive implicit memories and aberrant synaptic plasticity. This article describes the neurobiological bases of pathogenic memory disruption to provide some insight into how psychotherapy works. At least two mechanisms of unwanted memory erasing appear to be implicated in the effects of psychotherapy: inhibition of memory consolidation/reconsolidation and extinction. Behavioral evidence demonstrated that these two ways to forget are profoundly distinct in nature, and it is increasingly clear that their cellular, synaptic, and molecular underpinnings are different. Accordingly, the blockade of consolidation/reconsolidation erases memories by reversing the plasticity associated with memory maintenance, whereas extinction is a totally new form of plasticity that, similar to the plasticity underlying the old memory, requires protein synthesis-dependent synaptic remodeling.     

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.     

Cannabinoid inhibition improves memory in food-storing birds, but with a cost

Food-storing birds demonstrate remarkable memory ability in recalling the locations of thousands of hidden food caches. Although this behaviour requires the hippocampus, its synaptic mechanisms are not understood. Here we show the effects of cannabinoid receptor (CB1-R) blockade on spatial memory in food-storing black-capped chickadees (Poecile atricapilla). Intra-hippocampal infusions of the CB1-R antagonist SR141716A enhanced long-term memory for the location of a hidden food reward, measured 72 h after encoding. However, when the reward location changed during the retention interval, birds that had received SR141716A during initial learning showed impairments in recalling the most recent reward location. Thus, blocking CB1-R activity may lead to more robust, long-lasting memories, but these memories may be a source of proactive interference. The relationship between trace strength and interference may be important in understanding neural mechanisms of hippocampal function in general, as well as understanding the enhanced memory of food-storing birds.     

LTP, post or pre? A look at the evidence for the locus of long-term potentiation.

It seems self-evident that changes in the cellular synaptic function of the brain must underlie the formation and storage of cognitive memories. Because it has been identified as a brain area important in the formation of memory, the hippocampus has been a focus in the study of such synaptic changes. An activity-induced increase in hippocampal synaptic efficacy, known as long-term potentiation (LTP), has been widely studied as a potential substrate for memory. This paper briefly reviews some of the significant progress that has been made in understanding the cellular mechanisms that underlie LTP, including recent experiments dealing with its synaptic locus, or the question of whether the mechanism regulating LTP is pre- or postsynaptic.     

Dynamics of learning-induced cellular modifications in the cortex

This aim of this review is to describe the dynamics of learning-induced cellular modifications in the rat piriform (olfactory) cortex after olfactory discrimination learning and to describe their functional significance to long-term memory consolidation. The first change to occur is in the intrinsic properties of the neurons. One day after learning, pyramidal neurons show enhanced neuronal excitability. This enhancement results from reduction in calcium-dependent conductance that mediates the post burst after-hyperpolarization. Such enhanced excitability lasts for 3 days and is followed by a series of synaptic modifications. Several forms of long-term enhancement in synaptic connections between layer II pyramidal neurons in the piriform cortex accompany olfactory learning. Enhanced synaptic release is indicated by reduced paired-pulse facilitation. Post-synaptic enhancement of synaptic transmission is indicated by reduced rise time of post-synaptic potentials and formation of new synaptic connections is indicated by increased spine density along dendrites of these neurons. Such modifications last for up to 5 days. Thus, olfactory discrimination rule learning is accompanied by a series of cellular modifications which occur and then disappear at different times. These modifications overlap partially, allowing the maintenance of the cortical system in a ‘learning mode’ in which memories for specific odors can be acquired rapidly and efficiently.     

Synaptic plasticity in learning and memory

Pavlovian conditioning has been considered as one of the principal experimental approaches to understanding such complex brain functions as learning and memory. Use-dependent alterations in synaptic efficacy are believed to form the basis for these functions. The algorithm of synapse modification proposed by D. Hebb as early as 1949 is the coincident activation of pre- and postsynaptic neurons. The present review considers the evolution of experimental protocols which were used to reveal the manifestations of Hebb-type plasticity in the synaptic inputs to neocortical and hippocampal neurons. Special attention is focused on long-term modifications of synaptic efficacy in the hippocampus as a possible neuronal mechanism of learning and the role of disinhibition in their development. The effects of various neuromodulators on hippocampal long-term potentiation are considered. It is suggested that along with their involvement in disinhibition processes these substances may control the Hebb-type plasticity through intracellular second messenger systems.     

A molecular biological approach to synaptic plasticity and learning

Until the more recent advances made in molecular biology, attempts to link synaptic plasticity and learning have focused on using LTP as a marker of learning-induced synaptic plasticity, where one has expected to observe the same magnitude of change in synaptic strength as that observed with artificial stimulation. To a large extent this approach has been frustrated by the fact that it is generally assumed that the representation of the memory traces is distributed thoughout widespread networks of cells. By implication it is more likely that one would observe small distributed changes within a network; a formidable task to measure. In this review we describe how the advances in molecular biology give us both the tools to investigate the mechanisms of synaptic plasticity and to apply these to investigations of the underlying mechanisms in learning and the formation of memories that have until now remained out of our grasp.     

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.     

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.     

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.     

Place Cells,Grid Cells,and Memory

The hippocampal system is critical for storage and retrieval of declarative memories, including memories for locations and events that take place at those locations. Spatial memories place high demands on capacity. Memories must be distinct to be recalled without interference and encoding must be fast. Recent studies have indicated that hippocampal networks allow for fast storage of large quantities of uncorrelated spatial information. The aim of the this article is to review and discuss some of this work, taking as a starting point the discovery of multiple functionally specialized cell types of the hippocampal–entorhinal circuit, such as place, grid, and border cells. We will show that grid cells provide the hippocampus with a metric, as well as a putative mechanism for decorrelation of representations, that the formation of environment-specific place maps depends on mechanisms for long-term plasticity in the hippocampus, and that long-term spatiotemporal memory storage may depend on offline consolidation processes related to sharp-wave ripple activity in the hippocampus. The multitude of representations generated through interactions between a variety of functionally specialized cell types in the entorhinal–hippocampal circuit may be at the heart of the mechanism for declarative memory formation.The scientific study of human memory started with Herman Ebbinghaus, who initiated the quantitative investigation of associative memory processes as they take place (Ebbinghaus 1885). Ebbinghaus described the conditions that influence memory formation and he determined several basic principles of encoding and recall, such as the law of frequency and the effect of time on forgetting. With Ebbinghaus, higher mental functions were brought to the laboratory. In parallel with the human learning tradition that Ebbinghaus started, a new generation of experimental psychologists described the laws of associative learning in animals. With behaviorists like Pavlov, Watson, Hull, Skinner, and Tolman, a rigorous program for identifying the laws of animal learning was initiated. By the middle of the 20th century, a language for associative learning processes had been developed, and many of the fundamental relationships between environment and behavior had been described. What was completely missing, though, was an understanding of the neural activity underlying the formation of the memory. The behaviorists had deliberately shied away from physiological explanations because of the intangible nature of neural activity at that time.Then the climate began to change. Karl Lashley had shown that lesions in the cerebral cortex had predictable effects on behavior in animals (Lashley 1929, 1950), and Donald Hebb introduced concepts and ideas to account for complex brain functions at the neural circuit level, many of which have retained a place in modern neuroscience (Hebb 1949). Both Lashley and Hebb searched for the engram, but they found no specific locus for it. A significant turning point was reached when Scoville and Milner (1957) reported severe loss of memory in an epileptic patient, patient H.M., after bilateral surgical removal of the hippocampal formation and the surrounding medial temporal lobe areas. “After operation this young man could no longer recognize the hospital staff nor find his way to the bathroom, and he seemed to recall nothing of the day-to-day events of his hospital life.” This tragic misfortune inspired decades of research on the function of the hippocampus in memory. H.M.’s memory impairment could be reproduced in memory tasks in animals and studies of H.M., as well as laboratory animals, pointed to a critical role for the hippocampus in declarative memory—memory, which, in humans, can be consciously recalled and declared, such as memories of experiences and facts (Milner et al. 1968; Mishkin 1978; Cohen and Squire 1980; Squire 1992; Corkin 2002). What was missing from these early studies, however, was a way to address the neuronal mechanisms that led information to be stored as memory.The aim of this article is to show how studies of hippocampal neuronal activity during the past few decades have brought us to a point at which a mechanistic basis of memory formation is beginning to surface. An early landmark in this series of investigations was the discovery of place cells, cells that fire selectively at one or few locations in the environment. At first, these cells seemed to be part of the animal’s instantaneous representation of location, independent of memory, but gradually, over the course of several decades, it has become clear that place cells express current as well as past and future locations. In many ways, place cells can be used as readouts of the memories that are stored in the hippocampus. More recent work has also shown that place cells are part of a wider network of spatially modulated neurons, including grid, border, and head direction cells, each with distinct roles in the representation of space and spatial memory. In this article, we shall discuss potential mechanisms by which these cell types, particularly place and grid cells, in conjunction with synaptic plasticity, may form the basis of a mammalian system for fast high-capacity declarative memory.