Interrogating structural plasticity among synaptic engrams

Our daily experiences and learnings are stored in the form of memories. These experiences trigger synaptic plasticity and persistent structural and functional changes in neuronal synapses. Recently, cellular studies of memory storage and engrams have emerged over the last decade. Engram cells reflect interconnected neurons via modified synapses. However, we were unable to observe the structural changes arising from synaptic plasticity in the past, because it was not possible to distinguish the synapses between engram cells. To overcome this barrier, dual-eGRASP (enhanced green fluorescent protein reconstitution across synaptic partners) technology can label specific synapses among multiple synaptic ensembles. Selective labeling of engram synapses elucidated their role by observing the structural changes in synapses according to the memory state. Dual-eGRASP extends cellular level engram studies to introduce the era of synaptic level studies. Here, we review this concept and possible applications of the dual-eGRASP, including recent studies that provided visual evidence of structural plasticity at the engram synapse.     

An analytical short- and long-term memory model of presynaptic plasticity

A mathematical model, called the Learning Gate Model (LGM), that describes phenomena responsible for biological synaptic plasticity, is presented. The functionality of the model are mainly based on the work of Kandel and colleagues on the most elementary forms of learning observed in the Aplysia Californica marine mollusc. In particular, emphasis is placed on the double temporal dynamics of synaptic plasticity and the temporal specificity of classical conditioning. By properly modeling the effect of the binding of Ca⁺⁺ ions to the serotonin-sensitive adenylate cyclase enzyme, it is shown how a positively accelerated learning curve can be obtained for sensitization and classical conditioning. Phenomena of spontaneous recovery and second-order conditioning are reproduced through simulations. Mathematical analyses of the temporal trace of conditioned stimulus and of the Short-Term Memory steady state are also given.     

Role of delayed nonsynaptic neuronal plasticity in long-term associative memory

BACKGROUND: It is now well established that persistent nonsynaptic neuronal plasticity occurs after learning and, like synaptic plasticity, it can be the substrate for long-term memory. What still remains unclear, though, is how nonsynaptic plasticity contributes to the altered neural network properties on which memory depends. Understanding how nonsynaptic plasticity is translated into modified network and behavioral output therefore represents an important objective of current learning and memory research. RESULTS: By using behavioral single-trial classical conditioning together with electrophysiological analysis and calcium imaging, we have explored the cellular mechanisms by which experience-induced nonsynaptic electrical changes in a neuronal soma remote from the synaptic region are translated into synaptic and circuit level effects. We show that after single-trial food-reward conditioning in the snail Lymnaea stagnalis, identified modulatory neurons that are extrinsic to the feeding network become persistently depolarized between 16 and 24 hr after training. This is delayed with respect to early memory formation but concomitant with the establishment and duration of long-term memory. The persistent nonsynaptic change is extrinsic to and maintained independently of synaptic effects occurring within the network directly responsible for the generation of feeding. Artificial membrane potential manipulation and calcium-imaging experiments suggest a novel mechanism whereby the somal depolarization of an extrinsic neuron recruits command-like intrinsic neurons of the circuit underlying the learned behavior. CONCLUSIONS: We show that nonsynaptic plasticity in an extrinsic modulatory neuron encodes information that enables the expression of long-term associative memory, and we describe how this information can be translated into modified network and behavioral output.     

Structural plasticity at identified synapses during long-term memory in Aplysia

We have used the gill- and siphon-withdrawal reflex of Aplysia californica to determine the morphological basis of the prolonged changes in synaptic effectiveness that underlie long-term habituation and sensitization. We have found that clear structural changes accompany behavioral modification and have demonstrated that these can be detected at the level of identified sensory neuron synapses, a critical site of plasticity for the short-term forms of both types of learning. These alterations occur at two different levels of synaptic organization and include (1) changes in focal regions of synaptic membrane specialization--the number, size and vesicle complement of sensory neuron active zones are larger in sensitized animals and smaller in habituated animals compared with controls--and (2) a parallel but more dramatic and global trend involving modulation of the total number of presynaptic varicosities per sensory neuron. Quantitative analysis of the time course over which these structural alterations occur during sensitization has further demonstrated that changes in the number of varicosities and active zones persist in parallel with the behavioral retention of the memory. This increase in the number of sensory neuron synapses during long-term sensitization in Aplysia is similar to changes in the number of synapses in the mammalian brain following various forms of environmental manipulations and learning (Greenough, 1984). Therefore learning may involve a form of neuronal growth across a broad segment of the animal kingdom, thereby suggesting a role for structural synaptic plasticity during long-term behavioral modifications.     

A model of late long-term potentiation simulates aspects of memory maintenance

Late long-term potentiation (L-LTP) denotes long-lasting strengthening of synapses between neurons. L-LTP appears essential for the formation of long-term memory, with memories at least partly encoded by patterns of strengthened synapses. How memories are preserved for months or years, despite molecular turnover, is not well understood. Ongoing recurrent neuronal activity, during memory recall or during sleep, has been hypothesized to preferentially potentiate strong synapses, preserving memories. This hypothesis has not been evaluated in the context of a mathematical model representing ongoing activity and biochemical pathways important for L-LTP. In this study, ongoing activity was incorporated into two such models - a reduced model that represents some of the essential biochemical processes, and a more detailed published model. The reduced model represents synaptic tagging and gene induction simply and intuitively, and the detailed model adds activation of essential kinases by Ca(2+). Ongoing activity was modeled as continual brief elevations of Ca(2+). In each model, two stable states of synaptic strength/weight resulted. Positive feedback between synaptic weight and the amplitude of ongoing Ca(2+) transients underlies this bistability. A tetanic or theta-burst stimulus switches a model synapse from a low basal weight to a high weight that is stabilized by ongoing activity. Bistability was robust to parameter variations in both models. Simulations illustrated that prolonged periods of decreased activity reset synaptic strengths to low values, suggesting a plausible forgetting mechanism. However, episodic activity with shorter inactive intervals maintained strong synapses. Both models support experimental predictions. Tests of these predictions are expected to further understanding of how neuronal activity is coupled to maintenance of synaptic strength. Further investigations that examine the dynamics of activity and synaptic maintenance can be expected to help in understanding how memories are preserved for up to a lifetime in animals including humans.     

A computational model of dopaminergic modulation of hippocampal Schaffer collateral-CA1 long-term plasticity

Journal of Computational Neuroscience - Dopamine plays a critical role in modulating the long-term synaptic plasticity of the hippocampal Schaffer collateral-CA1 pyramidal neuron synapses (SC-CA1),...     

When shared concept cells support associations: Theory of overlapping memory engrams

Assemblies of neurons, called concepts cells, encode acquired concepts in human Medial Temporal Lobe. Those concept cells that are shared between two assemblies have been hypothesized to encode associations between concepts. Here we test this hypothesis in a computational model of attractor neural networks. We find that for concepts encoded in sparse neural assemblies there is a minimal fraction c_min of neurons shared between assemblies below which associations cannot be reliably implemented; and a maximal fraction c_max of shared neurons above which single concepts can no longer be retrieved. In the presence of a periodically modulated background signal, such as hippocampal oscillations, recall takes the form of association chains reminiscent of those postulated by theories of free recall of words. Predictions of an iterative overlap-generating model match experimental data on the number of concepts to which a neuron responds.     

A relative survival model for clustered responses

Relative Survival is the ratio of the overall survival of a group of patients to the expected survival for a demographically similar group. It is commonly used in disease registries to estimate the effect of a particular disease when the true cause of death is not reliably known. Regression models for relative survival have been described and we extend these models to allow for clustered responses by embedding them into the class of Generalized linear mixed models (GLMM). The method is motivated and demonstrated by a data set from the HALLUCA study, an epidemiological study which investigated provision of medical care to lung cancer patients in the region of Halle in the eastern part of Germany.     

Homeostatic control of synaptic rewiring in recurrent networks induces the formation of stable memory engrams

Brain networks store new memories using functional and structural synaptic plasticity. Memory formation is generally attributed to Hebbian plasticity, while homeostatic plasticity is thought to have an ancillary role in stabilizing network dynamics. Here we report that homeostatic plasticity alone can also lead to the formation of stable memories. We analyze this phenomenon using a new theory of network remodeling, combined with numerical simulations of recurrent spiking neural networks that exhibit structural plasticity based on firing rate homeostasis. These networks are able to store repeatedly presented patterns and recall them upon the presentation of incomplete cues. Storage is fast, governed by the homeostatic drift. In contrast, forgetting is slow, driven by a diffusion process. Joint stimulation of neurons induces the growth of associative connections between them, leading to the formation of memory engrams. These memories are stored in a distributed fashion throughout connectivity matrix, and individual synaptic connections have only a small influence. Although memory-specific connections are increased in number, the total number of inputs and outputs of neurons undergo only small changes during stimulation. We find that homeostatic structural plasticity induces a specific type of “silent memories”, different from conventional attractor states.     

Attention and available long-term memory in an activation-based model

The influence of attention on memorizing related items and on available long-term memory (ALTM) was explored, showing that N400 of no-memory items was more negative than that of the memory item. The results of the category comparison task indicated that information processing under attention-driven in WM determined the availability of related long-term memory, i.e., specific content, which was formerly concerned or ignored, yielding different indirect semantic priming effects. These indicate that the orientation of conceptual attention leads the related representations of LTM to diverse activation patterns, supporting the activation-based model.     

What does engram encode?: Heterogeneous memory engrams for different aspects of experience

Long-lasting synaptic changes within the neuronal network mediate memory. Neurons bearing such physical traces of memory (memory engram cells) are often equated with neurons expressing immediate early genes (IEGs) during a specific experience. However, past studies observed the expression of different IEGs in non-overlapping neurons or synaptic plasticity in neurons that do not express a particular IEG. Importantly, recent studies revealed that distinct subsets of neurons expressing different IEGs or even IEG negative-(yet active) neurons support different aspects of memory or computation, suggesting a more complex nature of memory engram cells than previously thought. In this short review, we introduce studies revealing such heterogeneous composition of the memory engram and discuss how the memory system benefits from it.     

Observations on clustered synaptic plasticity and highly structured input patterns

In this issue of Neuron, Makino and Malinow and Kleindienst et?al. present evidence of a behaviorally induced form of synaptic plasticity that would encourage the development of fine-scale structured input patterns and?the binding of features within single neurons.     

Spike timing-dependent plasticity and memory

Spike timing-dependent plasticity (STDP) is a bidirectional form of synaptic plasticity discovered about 30 years ago and based on the relative timing of pre- and post-synaptic spiking activity with a millisecond precision. STDP is thought to be involved in the formation of memory but the millisecond-precision spike-timing required for STDP is difficult to reconcile with the much slower timescales of behavioral learning. This review therefore aims to expose and discuss recent findings about i) the multiple STDP learning rules at both excitatory and inhibitory synapses in vitro, ii) the contribution of STDP-like synaptic plasticity in the formation of memory in vivo and iii) the implementation of STDP rules in artificial neural networks and memristive devices.     

Ubiquitous plasticity and memory storage

To date, most hypotheses of memory storage in the mammalian brain have focused upon long-term synaptic potentiation and depression (LTP and LTD) of fast glutamatergic excitatory postsynaptic currents (EPSCs). In recent years, it has become clear that many additional electrophysiological components of neurons, from electrical synapses to glutamate transporters to voltage-sensitive ion channels, can also undergo use-dependent long-term plasticity. Models of memory storage that incorporate this full range of demonstrated electrophysiological plasticity are better able to account for both the storage of memory in neuronal networks and the complexities of memory storage, indexing, and recall as measured behaviorally.