Synaptic clustering within dendrites: an emerging theory of memory formation

An emergent neural coactivity code for dynamic memory

Neural correlates of external variables provide potential internal codes that guide an animal’s behaviour. Notably, first-order features of neural activity, such as single-neuron firing rates, have been implicated in encoding information. However, the extent to which higher-order features, such as multi-neuron coactivity, play primary roles in encoding information or secondary roles in supporting single-neuron codes remains unclear. Here we show that millisecond-timescale coactivity amongst hippocampal CA1 neurons discriminates distinct millisecond-lived behavioural contingencies. This contingency discrimination was unrelated to the tuning of individual neurons but instead an emergent property of their coactivity. Contingency discriminating patterns were reactivated offline after learning and their reinstatement predicted trial-by-trial memory performance. Moreover, optogenetic suppression of inputs from the upstream CA3 region selectively during learning impaired coactivity-based contingency information in CA1 and subsequent dynamic memory retrieval. These findings identify coactivity as a primary feature of neural firing that discriminates distinct behaviourally-relevant variables and supports memory retrieval.

Two spaced training trials induce associative ERK-dependent long term memory in Neohelice granulata

Memory formation depends upon several parametric training conditions. Among them, trial number and inter-trial interval (ITI) are key factors to induce long-term retention. However, it is still unclear how individual training trials contribute to mechanisms underlying memory formation and stabilization. Contextual conditioning in Neohelice granulata has traditionally elicited associative long-term memory (LTM) after 15 spaced (ITI = 3 min) trials. Here, we show that LTM in crabs can be induced after only two training trials by increasing the ITI to 45 min (2t-LTM) and maintaining the same training duration as in traditional protocols. This newly observed LTM was preserved for at least 96 h, exhibiting protein synthesis dependence during consolidation and reconsolidation as well as context-specificity. Moreover, we demonstrate that 2t-LTM depends on inter-trial and post-training ERK activation showing a faster phosphorylation after the second trial compared to the first one. In summary, we present a new training protocol in crabs through a reduced number of trials showing associative features similar to traditional spaced training. This novel protocol allows for intra-training manipulation and the assessment of individual trial contribution to LTM formation.

Excessive Formation and Stabilization of Dendritic Spine Clusters in the MECP2-Duplication Syndrome Mouse Model of Autism

Autism-associated genetic mutations may perturb the balance between stability and plasticity of synaptic connections in the brain. Here, we report an increase in the formation and stabilization of dendritic spines in the cerebral cortex of the mouse model of MECP2-duplication syndrome, a high-penetrance form of syndromic autism. Increased stabilization is mediated entirely by spines that form cooperatively in 10-μm clusters and is observable across multiple cortical areas both spontaneously and following motor training. Excessive stability of dendritic spine clusters could contribute to behavioral rigidity and other phenotypes in syndromic autism.

Neuronal Activity at Synapse Resolution: Reporters and Effectors for Synaptic Neuroscience

The development of methods for the activity-dependent tagging of neurons enabled a new way to tackle the problem of engram identification at the cellular level, giving rise to groundbreaking findings in the field of memory studies. However, the resolution of activity-dependent tagging remains limited to the whole-cell level. Notably, events taking place at the synapse level play a critical role in the establishment of new memories, and strong experimental evidence shows that learning and synaptic plasticity are tightly linked. Here, we provide a comprehensive review of the currently available techniques that enable to identify and track the neuronal activity with synaptic spatial resolution. We also present recent technologies that allow to selectively interfere with specific subsets of synapses. Lastly, we discuss how these technologies can be applied to the study of learning and memory.

Unifying Long-Term Plasticity Rules for Excitatory Synapses by Modeling Dendrites of Cortical Pyramidal Neurons

A large number of experiments have indicated that precise spike times, firing rates, and synapse locations crucially determine the dynamics of long-term plasticity induction in excitatory synapses. However, it remains unknown how plasticity mechanisms of synapses distributed along dendritic trees cooperate to produce the wide spectrum of outcomes for various plasticity protocols. Here, we propose a four-pathway plasticity framework that is well grounded in experimental evidence and apply it to a biophysically realistic cortical pyramidal neuron model. We show in computer simulations that several seemingly contradictory experimental landmark studies are consistent with one unifying set of mechanisms when considering the effects of signal propagation in dendritic trees with respect to synapse location. Our model identifies specific spatiotemporal contributions of dendritic and axo-somatic spikes as well as of subthreshold activation of synaptic clusters, providing a unified parsimonious explanation not only for rate and timing dependence but also for location dependence of synaptic changes.

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A phenomenological synaptic plasticity rule is applied to a pyramidal neuron model

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Model reproduces rate-, timing-, and location-dependent plasticity results

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Active dendrites allow plasticity via dendritic spikes and subthreshold events

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Cooperative plasticity exists across the dendritic tree and within single branches

Emodin protected against synaptic impairment and oxidative stress induced by fluoride in SH-SY5Y cells by modulating ERK1/2/Nrf2/HO-1 pathway

Excessive fluoride exposure contributes to neurotoxic effects. Emodin exhibits antioxidative functions in the central nervous system (CNS); however, its neuroprotective mechanism against fluoride remains to be elucidated. Our aim was to explore the neuroprotective efficacy and the possible mechanisms of emodin. In our study, synaptic proteins and oxidative stress damage were examined after human neuroblastoma SH-SY5Y cells were treated with high doses of NaF for 24 hours. Moreover, pretreatment with emodin was used to shed light on the neuroprotective effects in NaF-induced toxicity in SH-SY5Y cells. We found that NaF significantly lowered the protein expressions of SNAP 25, synaptophysin and PSD 95 in SH-SY5Y cells. In addition, NaF exposure increased the protein expression of p-ERK1/2 and decreased the protein expressions of Nrf2 and HO-1, as well as facilitated increasing ROS, 4-hydroxynonenal (4-HNE), and 8-Hydroxy-2'-deoxyguanosine (8-OHdG). Pretreatment with emodin significantly recovered these alterations caused by NaF. These data implied that the neuroprotective effects of emodin and pointed to the promising utilization for protecting against neurotoxicity induced by fluoride.

Emergence of Stable Synaptic Clusters on Dendrites Through Synaptic Rewiring

The connectivity structure of neuronal networks in cortex is highly dynamic. This ongoing cortical rewiring is assumed to serve important functions for learning and memory. We analyze in this article a model for the self-organization of synaptic inputs onto dendritic branches of pyramidal cells. The model combines a generic stochastic rewiring principle with a simple synaptic plasticity rule that depends on local dendritic activity. In computer simulations, we find that this synaptic rewiring model leads to synaptic clustering, that is, temporally correlated inputs become locally clustered on dendritic branches. This empirical finding is backed up by a theoretical analysis which shows that rewiring in our model favors network configurations with synaptic clustering. We propose that synaptic clustering plays an important role in the organization of computation and memory in cortical circuits: we find that synaptic clustering through the proposed rewiring mechanism can serve as a mechanism to protect memories from subsequent modifications on a medium time scale. Rewiring of synaptic connections onto specific dendritic branches may thus counteract the general problem of catastrophic forgetting in neural networks.

Spiny and Non-spiny Parvalbumin-Positive Hippocampal Interneurons Show Different Plastic Properties

Dendritic spines control synaptic transmission and plasticity by augmenting post-synaptic potentials and providing biochemical compartmentalization. In principal cells, spines cover the dendritic tree at high densities, receive the overwhelming majority of excitatory inputs, and undergo experience-dependent structural re-organization. Although GABAergic interneurons have long been considered to be devoid of spines, a number of studies have reported the sparse existence of spines in interneurons. However, little is known about their organization or function at the cellular and network level. Here, we show that a subset of hippocampal parvalbumin-positive interneurons forms numerous dendritic spines with highly variable densities and input-selective organization. These spines form in areas with reduced perineuronal net sheathing, predispose for plastic changes in protein expression, and show input-specific re-organization after behavioral experience.

Experience-Dependent Development of Dendritic Arbors in Mouse Visual Cortex

The dendritic arbor of neurons constrains the pool of available synaptic partners and influences the electrical integration of synaptic currents. Despite these critical functions, our knowledge of the dendritic structure of cortical neurons during early postnatal development and how these dendritic structures are modified by visual experience is incomplete. Here, we present a large-scale dataset of 849 3D reconstructions of the basal arbor of pyramidal neurons collected across early postnatal development in visual cortex of mice of either sex. We found that the basal arbor grew substantially between postnatal day 7 (P7) and P30, undergoing a 45% increase in total length. However, the gross number of primary neurites and dendritic segments was largely determined by P7. Growth from P7 to P30 occurred primarily through extension of dendritic segments. Surprisingly, comparisons of dark-reared and typically reared mice revealed that a net gain of only 15% arbor length could be attributed to visual experience; most growth was independent of experience. To examine molecular contributions, we characterized the role of the activity-regulated small GTPase Rem2 in both arbor development and the maintenance of established basal arbors. We showed that Rem2 is an experience-dependent negative regulator of dendritic segment number during the visual critical period. Acute deletion of Rem2 reduced directionality of dendritic arbors. The data presented here establish a highly detailed, quantitative analysis of basal arbor development that we believe has high utility both in understanding circuit development as well as providing a framework for computationalists wishing to generate anatomically accurate neuronal models.SIGNIFICANCE STATEMENT Dendrites are the sites of the synaptic connections among neurons. Despite their importance for neural circuit function, only a little is known about the postnatal development of dendritic arbors of cortical pyramidal neurons and the influence of experience. Here we show that the number of primary basal dendritic arbors is already established before eye opening, and that these arbors primarily grow through lengthening of dendritic segments and not through addition of dendritic segments. Surprisingly, visual experience has a modest net impact on overall arbor length (15%). Experiments in KO animals revealed that the gene Rem2 is positive regulator of dendritic length and a negative regulator of dendritic segments.

Temporal Gating of Synaptic Competition in the Amygdala by Cannabinoid Receptor Activation

The acquisition of fear memories involves plasticity of the thalamic and cortical pathways to the lateral amygdala (LA). In turn, the maintenance of synaptic plasticity requires the interplay between input-specific synaptic tags and the allocation of plasticity-related proteins. Based on this interplay, weakly activated synapses can express long-lasting forms of synaptic plasticity by cooperating with strongly activated synapses. Increasing the number of activated synapses can shift cooperation to competition. Synaptic cooperation and competition can determine whether two events, separated in time, are associated or whether a particular event is selected for storage. The rules that determine whether synapses cooperate or compete are unknown. We found that synaptic cooperation and competition, in the LA, are determined by the temporal sequence of cortical and thalamic stimulation and that the strength of the synaptic tag is modulated by the endocannabinoid signaling. This modulation is particularly effective in thalamic synapses, supporting a critical role of endocannabinoids in restricting thalamic plasticity. Also, we found that the availability of synaptic proteins is activity-dependent, shifting competition to cooperation. Our data present the first evidence that presynaptic modulation of synaptic activation, by the cannabinoid signaling, functions as a temporal gating mechanism limiting synaptic cooperation and competition.

A mechanism for learning with sleep spindles

Spindles are ubiquitous oscillations during non-rapid eye movement (NREM) sleep. A growing body of evidence points to a possible link with learning and memory, and the underlying mechanisms are now starting to be unveiled. Specifically, spindles are associated with increased dendritic activity and high intracellular calcium levels, a situation favourable to plasticity, as well as with control of spiking output by feed-forward inhibition. During spindles, thalamocortical networks become unresponsive to inputs, thus potentially preventing interference between memory-related internal information processing and extrinsic signals. At the system level, spindles are co-modulated with other major NREM oscillations, including hippocampal sharp wave-ripples (SWRs) and neocortical slow waves, both previously shown to be associated with learning and memory. The sequential occurrence of reactivation at the time of SWRs followed by neuronal plasticity-promoting spindles is a possible mechanism to explain NREM sleep-dependent consolidation of memories. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Illuminating dendritic function with computational models

Dendrites have always fascinated researchers: from the artistic drawings by Ramon y Cajal to the beautiful recordings of today, neuroscientists have been striving to unravel the mysteries of these structures. Theoretical work in the 1960s predicted important dendritic effects on neuronal processing, establishing computational modelling as a powerful technique for their investigation. Since then, modelling of dendrites has been instrumental in driving neuroscience research in a targeted manner, providing experimentally testable predictions that range from the subcellular level to the systems level, and their relevance extends to fields beyond neuroscience, such as machine learning and artificial intelligence. Validation of modelling predictions often requires - and drives - new technological advances, thus closing the loop with theory-driven experimentation that moves the field forward. This Review features the most important, to our understanding, contributions of modelling of dendritic computations, including those pending experimental verification, and highlights studies of successful interactions between the modelling and experimental neuroscience communities.

Perceptron Learning and Classification in a Modeled Cortical Pyramidal Cell

The perceptron learning algorithm and its multiple-layer extension, the backpropagation algorithm, are the foundations of the present-day machine learning revolution. However, these algorithms utilize a highly simplified mathematical abstraction of a neuron; it is not clear to what extent real biophysical neurons with morphologically-extended non-linear dendritic trees and conductance-based synapses can realize perceptron-like learning. Here we implemented the perceptron learning algorithm in a realistic biophysical model of a layer 5 cortical pyramidal cell with a full complement of non-linear dendritic channels. We tested this biophysical perceptron (BP) on a classification task, where it needed to correctly binarily classify 100, 1,000, or 2,000 patterns, and a generalization task, where it was required to discriminate between two "noisy" patterns. We show that the BP performs these tasks with an accuracy comparable to that of the original perceptron, though the classification capacity of the apical tuft is somewhat limited. We concluded that cortical pyramidal neurons can act as powerful classification devices.

Maturation of newborn neurons predicts social memory persistence in mice

Memory transience is essential to gain cognitive flexibility. Recently, hippocampal neurogenesis is emerging as one of the mechanisms involved in the balance between persistence and forgetting. Social recognition memory (SRM) has its duration prolonged by neurogenesis. However, it is still to be determined whether boosting neurogenesis in distinct phases of SRM may favor forgetting over persistence. In the present study, we used enriched environment (EE) and memantine (MEM) to increase neurogenesis. SRM was ubiquitously prolonged by both, while EE after the memory acquisition did not favor forgetting. Interestingly, the proportion of newborn neurons with mature morphology in the dorsal hippocampus was higher in animals where persistence prevailed. Finally, one of the main factors for dendritic growth is the formation of cytoskeleton. We found that Latrunculin A, an inhibitor of actin polymerization, blunted the promnesic effect of EE. Altogether, our results indicate that the mechanisms triggered by EE to improve SRM are not limited to increasing the number of newborn neurons.

The Interstitial System of the Brain in Health and Disease

The brain interstitial fluid (ISF) and the cerebrospinal fluid (CSF) cushion and support the brain cells. The ISF occupies the brain interstitial system (ISS), whereas the CSF fills the brain ventricles and the subarachnoid space. The brain ISS is an asymmetrical, tortuous, and exceptionally confined space between neural cells and the brain microvasculature. Recently, with a newly developed in vivo measuring technique, a series of discoveries have been made in the brain ISS and the drainage of ISF. The goal of this review is to confer recent advances in our understanding of the brain ISS, including its structure, function, and the various processes mediating or disrupting ISF drainage in physiological and pathological conditions. The brain ISF in the deep brain regions has recently been demonstrated to drain in a compartmentalized ISS instead of a highly connected system, together with the drainage of ISF into the cerebrospinal fluid (CSF) at the surface of the cerebral cortex and the transportation from CSF into cervical lymph nodes. Besides, accumulation of tau in the brain ISS in conditions such as Alzheimer’s disease and its link to the sleep-wake cycle and sleep deprivation, clearance of ISF in a deep sleep via increased CSF flow, novel approaches to remove beta-amyloid from the brain ISS, and obstruction to the ISF drainage in neurological conditions are deliberated. Moreover, the role of ISS in the passage of extracellular vesicles (EVs) released from neural cells and the rapid targeting of therapeutic EVs into neural cells in the entire brain following an intranasal administration, and the promise and limitations of ISS based drug delivery approaches are discussed

Synaptic Clustering and Memory Formation

In the study of memory engrams, synaptic memory allocation is a newly emerged theme that focuses on how specific synapses are engaged in the storage of a given memory. Cumulating evidence from imaging and molecular experiments indicates that the recruitment of synapses that participate in the encoding and expression of memory is neither random nor uniform. A hallmark observation is the emergence of groups of synapses that share similar response properties and/or similar input properties and are located within a stretch of a dendritic branch. This grouping of synapses has been termed "synapse clustering" and has been shown to emerge in many different memory-related paradigms, as well as in in vitro studies. The clustering of synapses may emerge from synapses receiving similar input, or via many processes which allow for cross-talk between nearby synapses within a dendritic branch, leading to cooperative plasticity. Clustered synapses can act in concert to maximally exploit the nonlinear integration potential of the dendritic branches in which they reside. Their main contribution is to facilitate the induction of dendritic spikes and dendritic plateau potentials, which provide advanced computational and memory-related capabilities to dendrites and single neurons. This review focuses on recent evidence which investigates the role of synapse clustering in dendritic integration, sensory perception, learning, and memory as well as brain dysfunction. We also discuss recent theoretical work which explores the computational advantages provided by synapse clustering, leading to novel and revised theories of memory. As an eminent phenomenon during memory allocation, synapse clustering both shapes memory engrams and is also shaped by the parallel plasticity mechanisms upon which it relies.

Plasticity and recovery of function

The frontal lobe plays a crucial role in human motor behavior. It is one of the last areas of the brain to mature, especially the prefrontal regions. After a brief historical perspective on the perceived dichotomy between the view of the brain as a static organ and that of a plastic, constantly changing structure, we discuss the stability/plasticity dilemma including examples of documented cortical reorganization taking place at multiple spatial and temporal scales. We pose that while plasticity is needed for motor learning, stability of the system is necessary for storage and maintenance of memorized skills. We discuss how this plasticity/stability dilemma is resolved along the life span and after a brain injury. We then examine the main challenges that clinicians have to overcome to promote recovery of function in patients with brain lesions, including attempts to use neurostimulation techniques as adjuvant to training-based customary neurorehabilitation.

Noradrenergic and dopaminergic involvement in novelty modulation of aversive memory generalization of adult rats

The generalization of aversive memory can be defined as the phenomenon in which a situation similar to (but distinctive from) a previous aversive event triggers an avoidance response. This phenomenon has been suggested to play a role in several psychological disorders. In this study, we investigate the effects of novelty on the generalization of fear memories, and the involvement of noradrenergic and dopaminergic systems in this process. For this study we used male Wistar rats (3 months old, 300-400 g). The participation of each neurotransmitter system was evaluated separately (two set of experiments). In each experimental set, the animals were divided in groups (8 animals each): (i) control, (ii) novelty, and, (iii) antagonist + novelty group (timolol, a β-adrenergic antagonist, or SCH23390, a D1/D5 dopaminergic antagonist, in the first and in the second set of experiments, respectively). Additionaly, to investigate whether novelty exposure increases the levels of noradrenaline and/or dopamine in the hippocampus fifteen animals were divided in three groups (5 animals each).: (i) naïve, (ii) control; and, (iii) novelty. To examine aversive memory, and generalization of aversive memory, we trained adult male Wistar rats in an inhibitory avoidance (IA) memory task and after in a modified inhibitory avoidance (MIA). Before the MIA training some of the animals were exposed to environmental novelty (open field). Immediately before this novelty exposure, some animals received intrahippocampal infusion of timolol (β-adrenergic antagonist), SCH23390 (D1/D5 antagonist) or vehicle to evaluate the involvement of noradrenergic and dopaminergic systems. Finally, to evaluate aversive memory and generalization of aversive memory respectively, half of the animals in each group were tested on IA and half on MIA. We confirmed that the exposure to novelty blocks the generalization of aversive memory, but moreover, demonstrated that this process involves activation of β-adrenergic and dopaminergic D1/D5 pathways. We additionally observed that exposure to novelty raises hippocampal levels of noradrenaline and dopamine. This suggests that these neurotransmitters not only influence long-term memory (LTM) as such, but also aversive memory generalization.

Mechanisms of systems memory consolidation during sleep

Long-term memory formation is a major function of sleep. Based on evidence from neurophysiological and behavioral studies mainly in humans and rodents, we consider the formation of long-term memory during sleep as an active systems consolidation process that is embedded in a process of global synaptic downscaling. Repeated neuronal replay of representations originating from the hippocampus during slow-wave sleep leads to a gradual transformation and integration of representations in neocortical networks. We highlight three features of this process: (i) hippocampal replay that, by capturing episodic memory aspects, drives consolidation of both hippocampus-dependent and non-hippocampus-dependent memory; (ii) brain oscillations hallmarking slow-wave and rapid-eye movement sleep that provide mechanisms for regulating both information flow across distant brain networks and local synaptic plasticity; and (iii) qualitative transformations of memories during systems consolidation resulting in abstracted, gist-like representations.

Mammillothalamic Disconnection Alters Hippocampocortical Oscillatory Activity and Microstructure: Implications for Diencephalic Amnesia

Diencephalic amnesia can be as debilitating as the more commonly known temporal lobe amnesia, yet the precise contribution of diencephalic structures to memory processes remains elusive. Across four cohorts of male rats, we used discrete lesions of the mammillothalamic tract to model aspects of diencephalic amnesia and assessed the impact of these lesions on multiple measures of activity and plasticity within the hippocampus and retrosplenial cortex. Lesions of the mammillothalamic tract had widespread indirect effects on hippocampocortical oscillatory activity within both theta and gamma bands. Both within-region oscillatory activity and cross-regional synchrony were altered. The network changes were state-dependent, displaying different profiles during locomotion and paradoxical sleep. Consistent with the associations between oscillatory activity and plasticity, complementary analyses using several convergent approaches revealed microstructural changes, which appeared to reflect a suppression of learning-induced plasticity in lesioned animals. Together, these combined findings suggest a mechanism by which damage to the medial diencephalon can impact upon learning and memory processes, highlighting an important role for the mammillary bodies in the coordination of hippocampocortical activity.SIGNIFICANCE STATEMENT Information flow within the Papez circuit is critical to memory. Damage to ascending mammillothalamic projections has consistently been linked to amnesia in humans and spatial memory deficits in animal models. Here we report on the changes in hippocampocortical oscillatory dynamics that result from chronic lesions of the mammillothalamic tract and demonstrate, for the first time, that the mammillary bodies, independently of the supramammillary region, contribute to frequency modulation of hippocampocortical theta oscillations. Consistent with the associations between oscillatory activity and plasticity, the lesions also result in a suppression of learning-induced plasticity. Together, these data support new functional models whereby mammillary bodies are important for coordinating hippocampocortical activity rather than simply being a relay of hippocampal information as previously assumed.

Challenging the point neuron dogma: FS basket cells as 2-stage nonlinear integrators

Interneurons are critical for the proper functioning of neural circuits. While often morphologically complex, their dendrites have been ignored for decades, treating them as linear point neurons. Exciting new findings reveal complex, non-linear dendritic computations that call for a new theory of interneuron arithmetic. Using detailed biophysical models, we predict that dendrites of FS basket cells in both hippocampus and prefrontal cortex come in two flavors: supralinear, supporting local sodium spikes within large-volume branches and sublinear, in small-volume branches. Synaptic activation of varying sets of these dendrites leads to somatic firing variability that cannot be fully explained by the point neuron reduction. Instead, a 2-stage artificial neural network (ANN), with sub- and supralinear hidden nodes, captures most of the variance. Reduced neuronal circuit modeling suggest that this bi-modal, 2-stage integration in FS basket cells confers substantial resource savings in memory encoding as well as the linking of memories across time.

Memory Prosthesis: Is It Time for a Deep Neuromimetic Computing Approach?

Memory loss, one of the most dreaded afflictions of the human condition, presents considerable burden on the world's health care system and it is recognized as a major challenge in the elderly. There are only a few neuromodulation treatments for memory dysfunctions. Open loop deep brain stimulation is such a treatment for memory improvement, but with limited success and conflicting results. In recent years closed-loop neuroprosthesis systems able to simultaneously record signals during behavioral tasks and generate with the use of internal neural factors the precise timing of stimulation patterns are presented as attractive alternatives and show promise in memory enhancement and restoration. A few such strides have already been made in both animals and humans, but with limited insights into their mechanisms of action. Here, I discuss why a deep neuromimetic computing approach linking multiple levels of description, mimicking the dynamics of brain circuits, interfaced with recording and stimulating electrodes could enhance the performance of current memory prosthesis systems, shed light into the neurobiology of learning and memory and accelerate the progress of memory prosthesis research. I propose what the necessary components (nodes, structure, connectivity, learning rules, and physiological responses) of such a deep neuromimetic model should be and what type of data are required to train/test its performance, so it can be used as a true substitute of damaged brain areas capable of restoring/enhancing their missing memory formation capabilities. Considerations to neural circuit targeting, tissue interfacing, electrode placement/implantation, and multi-network interactions in complex cognition are also provided.

Differential role of pre- and postsynaptic neurons in the activity-dependent control of synaptic strengths across dendrites

Neurons receive a large number of active synaptic inputs from their many presynaptic partners across their dendritic tree. However, little is known about how the strengths of individual synapses are controlled in balance with other synapses to effectively encode information while maintaining network homeostasis. This is in part due to the difficulty in assessing the activity of individual synapses with identified afferent and efferent connections for a synapse population in the brain. Here, to gain insights into the basic cellular rules that drive the activity-dependent spatial distribution of pre- and postsynaptic strengths across incoming axons and dendrites, we combine patch-clamp recordings with live-cell imaging of hippocampal pyramidal neurons in dissociated cultures and organotypic slices. Under basal conditions, both pre- and postsynaptic strengths cluster on single dendritic branches according to the identity of the presynaptic neurons, thus highlighting the ability of single dendritic branches to exhibit input specificity. Stimulating a single presynaptic neuron induces input-specific and dendritic branchwise spatial clustering of presynaptic strengths, which accompanies a widespread multiplicative scaling of postsynaptic strengths in dissociated cultures and heterosynaptic plasticity at distant synapses in organotypic slices. Our study provides evidence for a potential homeostatic mechanism by which the rapid changes in global or distant postsynaptic strengths compensate for input-specific presynaptic plasticity.

How Dendrites Affect Online Recognition Memory

In order to record the stream of autobiographical information that defines our unique personal history, our brains must form durable memories from single brief exposures to the patterned stimuli that impinge on them continuously throughout life. However, little is known about the computational strategies or neural mechanisms that underlie the brain's ability to perform this type of "online" learning. Based on increasing evidence that dendrites act as both signaling and learning units in the brain, we developed an analytical model that relates online recognition memory capacity to roughly a dozen dendritic, network, pattern, and task-related parameters. We used the model to determine what dendrite size maximizes storage capacity under varying assumptions about pattern density and noise level. We show that over a several-fold range of both of these parameters, and over multiple orders-of-magnitude of memory size, capacity is maximized when dendrites contain a few hundred synapses-roughly the natural number found in memory-related areas of the brain. Thus, in comparison to entire neurons, dendrites increase storage capacity by providing a larger number of better-sized learning units. Our model provides the first normative theory that explains how dendrites increase the brain's capacity for online learning; predicts which combinations of parameter settings we should expect to find in the brain under normal operating conditions; leads to novel interpretations of an array of existing experimental results; and provides a tool for understanding which changes associated with neurological disorders, aging, or stress are most likely to produce memory deficits-knowledge that could eventually help in the design of improved clinical treatments for memory loss.

Stable memory and computation in randomly rewiring neural networks

Our brains must maintain a representation of the world over a period of time much longer than the typical lifetime of the biological components producing that representation. For example, recent research suggests that dendritic spines in the adult mouse hippocampus are transient with an average lifetime of ~10 days. If this is true, and if turnover is equally likely for all spines, ~95% of excitatory synapses onto a particular neuron will turn over within 30 days; however, a neuron's receptive field can be relatively stable over this period. Here, we use computational modeling to ask how memories can persist in neural circuits such as the hippocampus and visual cortex in the face of synapse turnover. We demonstrate that Hebbian plasticity during replay of presynaptic activity patterns can integrate newly formed synapses into pre-existing memories. Furthermore, we find that Hebbian plasticity during replay is sufficient to stabilize the receptive fields of hippocampal place cells in a model of the grid-cell-to-place-cell transformation in CA1 and of orientation-selective cells in a model of the center-surround-to-simple-cell transformation in V1. Together, these data suggest that a simple plasticity rule, correlative Hebbian plasticity of synaptic strengths, is sufficient to preserve neural representations in the face of synapse turnover, even in the absence of activity-dependent structural plasticity. NEW & NOTEWORTHY Recent research suggests that synapses turn over rapidly in some brain structures; however, memories seem to persist for much longer. We show that Hebbian plasticity of synaptic strengths during reactivation events can preserve memory in computational models of hippocampal and cortical networks despite turnover of all synapses. Our results suggest that memory can be stored in the correlation structure of a network undergoing rapid synaptic remodeling.

Fear conditioning and extinction induce opposing changes in dendritic spine remodeling and somatic activity of layer 5 pyramidal neurons in the mouse motor cortex

Multiple brain regions including the amygdala and prefrontal cortex are crucial for modulating fear conditioning and extinction. The primary motor cortex is known to participate in the planning, control, and execution of voluntary movements. Whether and how the primary motor cortex is involved in modulating freezing responses related to fear conditioning and extinction remains unclear. Here we show that inactivation of the mouse primary motor cortex impairs both the acquisition and extinction of freezing responses induced by auditory-cued fear conditioning. Fear conditioning significantly increases the elimination of dendritic spines on apical dendrites of layer 5 pyramidal neurons in the motor cortex. These eliminated spines are further apart from each other than expected from random distribution along dendrites. On the other hand, fear extinction causes the formation of new spines that are located near the site of spines eliminated previously after fear conditioning. We further show that fear conditioning decreases and fear extinction increases somatic activities of layer 5 pyramidal neurons in the motor cortex respectively. Taken together, these findings indicate fear conditioning and extinction induce opposing changes in synaptic connections and somatic activities of layer 5 pyramidal neurons in the primary motor cortex, a cortical region important for the acquisition and extinction of auditory-cued conditioned freezing responses.

Peeking into the sleeping brain: Using in vivo imaging in rodents to understand the relationship between sleep and cognition

Sleep is well known to benefit cognitive function. In particular, sleep has been shown to enhance learning and memory in both humans and animals. While the underlying mechanisms are not fully understood, it has been suggested that brain activity during sleep modulates neuronal communication through synaptic plasticity. These insights were mostly gained using electrophysiology to monitor ongoing large scale and single cell activity. While these efforts were instrumental in the characterisation of important network and cellular activity during sleep, several aspects underlying cognition are beyond the reach of this technology. Neuronal circuit activity is dynamically regulated via the precise interaction of different neuronal and non-neuronal cell types and relies on subtle modifications of individual synapses. In contrast to established electrophysiological approaches, recent advances in imaging techniques, mainly applied in rodents, provide unprecedented access to these aspects of neuronal function in vivo. In this review, we describe various techniques currently available for in vivo brain imaging, from single synapse to large scale network activity. We discuss the advantages and limitations of these approaches in the context of sleep research and describe which particular aspects related to cognition lend themselves to this kind of investigation. Finally, we review the few studies that used in vivo imaging in rodents to investigate the sleeping brain and discuss how the results have already significantly contributed to a better understanding on the complex relation between sleep and plasticity across development and adulthood.

Local resources of polyribosomes and SER promote synapse enlargement and spine clustering after long-term potentiation in adult rat hippocampus

Synapse clustering facilitates circuit integration, learning, and memory. Long-term potentiation (LTP) of mature neurons produces synapse enlargement balanced by fewer spines, raising the question of how clusters form despite this homeostatic regulation of total synaptic weight. Three-dimensional reconstruction from serial section electron microscopy (3DEM) revealed the shapes and distributions of smooth endoplasmic reticulum (SER) and polyribosomes, subcellular resources important for synapse enlargement and spine outgrowth. Compared to control stimulation, synapses were enlarged two hours after LTP on resource-rich spines containing polyribosomes (4% larger than control) or SER (15% larger). SER in spines shifted from a single tubule to complex spine apparatus after LTP. Negligible synapse enlargement (0.6%) occurred on resource-poor spines lacking SER and polyribosomes. Dendrites were divided into discrete synaptic clusters surrounded by asynaptic segments. Spine density was lowest in clusters having only resource-poor spines, especially following LTP. In contrast, resource-rich spines preserved neighboring resource-poor spines and formed larger clusters with elevated total synaptic weight following LTP. These clusters also had more shaft SER branches, which could sequester cargo locally to support synapse growth and spinogenesis. Thus, resources appear to be redistributed to synaptic clusters with LTP-related synapse enlargement while homeostatic regulation suppressed spine outgrowth in resource-poor synaptic clusters.

Stimulation Modeling on Three-Dimensional Anisotropic Diffusion of MRI Tracer in the Brain Interstitial Space

Purpose: To build a mathematical model based magnetic resonance (MR) method to simulate drug anisotropic distribution in vivo in the interstitial space (ISS) of the brain.

Materials and Methods: An injection of signal intensity-related gadolinium-diethylenetriaminepentaacetic acid (Gd-DTPA), which is an exogenous drug, was administered, and its diffusion was traced in the ISS of the brain using MRI. Dynamic MRI scans were performed to monitor and record the changes in signal intensity in each pixel of the region of interest. The transport parameters were calculated using the modified equation to simulate three-dimensional anisotropic diffusion, which was resolved using a Laplace transform and a linear regressive model.

Results: After Gd-DTPA was introduced into the caudate nucleus, its distribution was demonstrated in real time. As the Gd-DTPA gradually cleared, the associated hyperintensity attenuated over time. The average diffusion coefficient (D) and the clearance rate constant (k) were (1.305 ± 0.364) × 10⁻⁴ mm²/s and (1.40 ± 0.206) × 10⁻⁵ s⁻¹, respectively.

Discussion: The combination of trace-based MRI and modified diffusion mathematical models can visualize and measure the three-dimensional anisotropic distribution of drugs in the ISS of the brain.

Dendritic mechanisms of hippocampal place field formation

Place cells in the hippocampus are thought to form a cognitive map of space and a memory of places. How this map forms when animals are exposed to novel environments has been the subject of a great deal of research. Numerous technical advances over the past decade greatly increased our understanding of the precise mechanisms underlying place field formation. In particular, it is now possible to connect cellular and circuit mechanisms of integration, firing, and plasticity discovered in brain slices, to processes taking place in vivo as animals learn and encode novel environments. Here, we focus on recent results and describe the dendritic mechanisms most likely responsible for the formation of place fields. We also discuss key open questions that are likely to be answered in the coming years.

Primed to Sleep: The Dynamics of Synaptic Plasticity Across Brain States

It is commonly accepted that brain plasticity occurs in wakefulness and sleep. However, how these different brain states work in concert to create long-lasting changes in brain circuitry is unclear. Considering that wakefulness and sleep are profoundly different brain states on multiple levels (e.g., cellular, molecular and network activation), it is unlikely that they operate exactly the same way. Rather it is probable that they engage different, but coordinated, mechanisms. In this article we discuss how plasticity may be divided across the sleep-wake cycle, and how synaptic changes in each brain state are linked. Our working model proposes that waking experience triggers short-lived synaptic events that are necessary for transient plastic changes and mark (i.e., 'prime') circuits and synapses for further processing in sleep. During sleep, synaptic protein synthesis at primed synapses leads to structural changes necessary for long-term information storage.

Susceptibility of consolidated procedural memory to interference is independent of its active task-based retrieval

Reconsolidation theory posits that upon retrieval, consolidated memories are destabilized and need to be restabilized in order to persist. It has been suggested that experience with a competitive task immediately after memory retrieval may interrupt these restabilization processes leading to memory loss. Indeed, using a motor sequence learning paradigm, we have recently shown that, in humans, interference training immediately after active task-based retrieval of the consolidated motor sequence knowledge may negatively affect its performance levels. Assessing changes in tapping pattern before and after interference training, we also demonstrated that this performance deficit more likely indicates a genuine memory loss rather than an initial failure of memory retrieval. Here, applying a similar approach, we tested the necessity of the hypothetical retrieval-induced destabilization of motor memory to allow its impairment. The impact of memory retrieval on performance of a new motor sequence knowledge acquired during the interference training was also evaluated. Similar to the immediate post-retrieval interference, interference training alone without the preceding active task-based memory retrieval was also associated with impairment of the pre-established motor sequence memory. Performance levels of the sequence trained during the interference training, on the other hand, were impaired only if this training was given immediately after memory retrieval. Noteworthy, an 8-hour interval between memory retrieval and interference allowed to express intact performance levels for both sequences. The current results suggest that susceptibility of the consolidated motor memory to behavioral interference is independent of its active task-based retrieval. Differential effects of memory retrieval on performance levels of the new motor sequence encoded during the interference training further suggests that memory retrieval may influence the way new information is stored by facilitating its integration within the retrieved memory trace. Thus, impairment of the pre-established motor memory may reflect interference from a competing memory trace rather than involve interruption of reconsolidation.

Synaptic topography - Converging connections and emerging function

Brain circuits are constituted of individual neurons that are interconnected with a vast array of synapses. In order to understand how brain function emerges from this complex synaptic network, immense efforts have been made to trace the synaptic topography, i.e. arrangement of synaptic connections, of the network. In addition to anatomically elaborating the synaptic layout at multiple levels across brain regions, recent studies have attempted to elucidate the fundamental wiring principles that govern neural information processing in the brain, establishing a link between anatomy and function. In this review, I will discuss recent discoveries on the topographical organization of synaptic connections at the cell-to-cell and subcellular levels in the cortex and hippocampus. Accumulating evidence leads us to acknowledge the highly structured, non-random synaptic connectivity that emerges together with sensory feature preferences of neurons and synchronous neuronal activity.

Bridging Synaptic and Epigenetic Maintenance Mechanisms of the Engram

How memories are maintained, and how memories are lost during aging or disease, are intensely investigated issues. Arguably, the reigning theory is that synaptic modifications allow for the formation of engrams during learning, and sustaining engrams sustains memory. Activity-regulated gene expression profiles have been shown to be critical to these processes, and their control by the epigenome has begun to be investigated in earnest. Here, we propose a novel theory as to how engrams are sustained. We propose that many of the genes that are currently believed to underlie long-term memory are actually part of a “plasticity transcriptome” that underpins structural and functional modifications to neuronal connectivity during the hours to days following learning. Further, we hypothesize that a “maintenance transcriptome” is subsequently induced that includes epigenetic negative regulators of gene expression, particularly histone deacetylases. The maintenance transcriptome negatively regulates the plasticity transcriptome, and thus the plastic capability of a neuron, after learning. In this way, the maintenance transcriptome would act as a metaplasticity mechanism that raises the threshold for change in neurons within an engram, helping to ensure the connectivity is stabilized and memory is maintained.

PKCα integrates spatiotemporally distinct Ca2+ and autocrine BDNF signaling to facilitate synaptic plasticity

The Protein Kinase C (PKC) enzymes have long been established as critical for synaptic plasticity. However, it is unknown whether Ca²⁺-dependent PKC isozymes are activated in dendritic spines during plasticity, and if so, how this synaptic activity is encoded by PKC. Here, using newly-developed, isozyme-specific sensors, we demonstrate that classic isozymes are activated to varying degrees and with unique kinetics. PKCα is activated robustly and rapidly in stimulated spines and is the only isozyme required for structural plasticity. This specificity, depends on a PDZ-binding domain present only in PKCα. The activation of PKCα during plasticity requires both NMDAR Ca²⁺-flux and autocrine BDNF-TrkB signaling, two pathways that differ vastly in their spatiotemporal scales of signaling. Our results suggest that by integrating these signals, PKCα combines a measure of recent, nearby synaptic plasticity with local synaptic input, enabling complex cellular computations such as heterosynaptic facilitation of plasticity necessary for efficient hippocampal-dependent learning.

AMPA receptor trafficking and its role in heterosynaptic plasticity

Historically, long-term potentiation (LTP) and long-term depression (LTD), the best-characterized forms of long-term synaptic plasticity, are viewed as experience-dependent and input-specific processes. However, cumulative experimental and theoretical data have demonstrated that LTP and LTD can promote compensatory alterations in non-stimulated synapses. In this work, we have developed a computational model of a tridimensional spiny dendritic segment to investigate the role of AMPA receptor (AMPAR) trafficking during synaptic plasticity at specific synapses and its consequences for the populations of AMPAR at nearby synapses. Our results demonstrated that the mechanisms of AMPAR trafficking involved with LTP and LTD can promote heterosynaptic plasticity at non-stimulated synapses. These alterations are compensatory and arise from molecular competition. Moreover, the heterosynaptic changes observed in our model can modulate further activity-driven inductions of synaptic plasticity.

Intracortical Network Effects Preserve Thalamocortical Input Efficacy in a Cortex Without Layers

Layer IV (LIV) of the rodent somatosensory cortex contains the somatotopic barrel field. Barrels receive much of the sensory input to the cortex through innervation by thalamocortical axons from the ventral posteromedial nucleus. In the reeler mouse, the absence of cortical layers results in the formation of mispositioned barrel-equivalent clusters of LIV fated neurons. Although functional imaging suggests that sensory input activates the cortex, little is known about the cellular and synaptic properties of identified excitatory neurons of the reeler cortex. We examined the properties of thalamic input to spiny stellate (SpS) neurons in the reeler cortex with in vitro electrophysiology, optogenetics, and subcellular channelrhodopsin-2-assisted circuit mapping (sCRACM). Our results indicate that reeler SpS neurons receive direct but weakened input from the thalamus, with a dispersed spatial distribution along the somatodendritic arbor. These results further document subtle alterations in functional connectivity concomitant of absent layering in the reeler mutant. We suggest that intracortical amplification mechanisms compensate for this weakening in order to allow reliable sensory transmission to the mutant neocortex.

Quantitative Analysis of the Spatial Organization of Synaptic Inputs on the Postsynaptic Dendrite

The spatial organization of synaptic inputs on the dendritic tree of cortical neurons is considered to play an important role in the dendritic integration of synaptic activity. Active electrical properties of dendrites and mechanisms of dendritic integration have been studied for a long time. New technological developments are now enabling the characterization of the spatial organization of synaptic inputs on dendrites. However, quantitative methods for the analysis of such data are lacking. In order to place cluster parameters into the framework of dendritic integration and synaptic summation, these parameters need to be assessed rigorously in a quantitative manner. Here I present an approach for the analysis of synaptic input clusters on the dendritic tree that is based on combinatorial analysis of the likelihoods to observe specific input arrangements. This approach is superior to the commonly applied analysis of nearest neighbor distances between synaptic inputs comparing their distribution to simulations with random reshuffling or bootstrapping. First, the new approach yields exact likelihood values rather than approximate numbers obtained from simulations. Second and more importantly, the new approach identifies individual clusters and thereby allows to quantify and characterize individual cluster properties.

Two-photon imaging of neuronal activity in motor cortex of marmosets during upper-limb movement tasks

Two-photon imaging in behaving animals has revealed neuronal activities related to behavioral and cognitive function at single-cell resolution. However, marmosets have posed a challenge due to limited success in training on motor tasks. Here we report the development of protocols to train head-fixed common marmosets to perform upper-limb movement tasks and simultaneously perform two-photon imaging. After 2–5 months of training sessions, head-fixed marmosets can control a manipulandum to move a cursor to a target on a screen. We conduct two-photon calcium imaging of layer 2/3 neurons in the motor cortex during this motor task performance, and detect task-relevant activity from multiple neurons at cellular and subcellular resolutions. In a two-target reaching task, some neurons show direction-selective activity over the training days. In a short-term force-field adaptation task, some neurons change their activity when the force field is on. Two-photon calcium imaging in behaving marmosets may become a fundamental technique for determining the spatial organization of the cortical dynamics underlying action and cognition.

Marmosets are an important model organism in neuroscience but there has only been limited success in training them on behavioral tasks. Here the authors report their ability to train marmosets in various motor tasks and simultaneously image neural dynamics in motor cortex with 2-photon imaging.

Dendritic Spine Elimination: Molecular Mechanisms and Implications

Dynamic modification of synaptic connectivity in response to sensory experience is a vital step in the refinement of brain circuits as they are established during development and modified during learning. In addition to the well-established role for new spine growth and stabilization in the experience-dependent plasticity of neural circuits, dendritic spine elimination has been linked to improvements in learning, and dysregulation of spine elimination has been associated with intellectual disability and behavioral impairment. Proper brain function requires a tightly regulated balance between spine formation and spine elimination. Although most studies have focused on the mechanisms of spine formation, considerable progress has been made recently in delineating the neural activity patterns and downstream molecular mechanisms that drive dendritic spine elimination. Here, we review the current state of knowledge concerning the signaling pathways that drive dendritic spine shrinkage and elimination in the cerebral cortex and we discuss their implication in neuropsychiatric and neurodegenerative disease.

Embedded ensemble encoding hypothesis: The role of the "Prepared" cell

We here reconsider current theories of neural ensembles in the context of recent discoveries about neuronal dendritic physiology. The key physiological observation is that the dendritic plateau potential produces sustained depolarization of the cell body (amplitude 10-20 mV, duration 200-500 ms). Our central hypothesis is that synaptically-evoked dendritic plateau potentials lead to a prepared state of a neuron that favors spike generation. The plateau both depolarizes the cell toward spike threshold, and provides faster response to inputs through a shortened membrane time constant. As a result, the speed of synaptic-to-action potential (AP) transfer is faster during the plateau phase. Our hypothesis relates the changes from "resting" to "depolarized" neuronal state to changes in ensemble dynamics and in network information flow. The plateau provides the Prepared state (sustained depolarization of the cell body) with a time window of 200-500 ms. During this time, a neuron can tune into ongoing network activity and synchronize spiking with other neurons to provide a coordinated Active state (robust firing of somatic APs), which would permit "binding" of signals through coordination of neural activity across a population. The transient Active ensemble of neurons is embedded in the longer-lasting Prepared ensemble of neurons. We hypothesize that "embedded ensemble encoding" may be an important organizing principle in networks of neurons.

Behavioral, cellular, and synaptic tagging frameworks

Behavioral tagging is the transformation of a short-term memory induced by a weak experience into a long-term memory through temporal association with a novel experience. This phenomenon was discovered to recapitulate synaptic tagging and capture at the behavioral level. Significant progress has been made in determining the molecular machinery associated with synaptic tagging and capture and behavioral tagging theories. However, the tag setting and recruitment of plasticity-related proteins that occur within the spatiotemporally constrained cell ensemble at the network level (cellular tagging) in the brain where multimodal sensory information is input are just beginning to be understood. Here, we review the evidence for behavioral tagging and the mechanism underlying memory allocation at the network level leading to the overlap of cell ensembles. We also discuss the functional significance of overlapping cell ensembles in association of standard Pavlovian conditioning and distinct memories. Finally, we describe the role of neuronal ensemble overlap in behavioral tagging.

Memory allocation mechanisms underlie memory linking across time

Memories are dynamic in nature. A cohesive representation of the world requires memories to be altered over time, linked with other memories and eventually integrated into a larger framework of sematic knowledge. Although there is a considerable literature on how single memories are encoded, retrieved and updated, little is known about the mechanisms that govern memory linking, e.g., linking and integration of various memories across hours or days. In this review, we present evidence that specific memory allocation mechanisms, such as changes in CREB and intrinsic excitability, ensure memory storage in ways that facilitate effective recall and linking at a later time. Beyond CREB and intrinsic excitability, we also review a number of other phenomena with potential roles in memory linking.

Long-term potentiation expands information content of hippocampal dentate gyrus synapses

An approach combining signal detection theory and precise 3D reconstructions from serial section electron microscopy (3DEM) was used to investigate synaptic plasticity and information storage capacity at medial perforant path synapses in adult hippocampal dentate gyrus in vivo. Induction of long-term potentiation (LTP) markedly increased the frequencies of both small and large spines measured 30 minutes later. This bidirectional expansion resulted in heterosynaptic counterbalancing of total synaptic area per unit length of granule cell dendrite. Control hemispheres exhibited 6.5 distinct spine sizes for 2.7 bits of storage capacity while LTP resulted in 12.9 distinct spine sizes (3.7 bits). In contrast, control hippocampal CA1 synapses exhibited 4.7 bits with much greater synaptic precision than either control or potentiated dentate gyrus synapses. Thus, synaptic plasticity altered total capacity, yet hippocampal subregions differed dramatically in their synaptic information storage capacity, reflecting their diverse functions and activation histories.

Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory

Modeling studies suggest that clustered structural plasticity of dendritic spines is an efficient mechanism of information storage in cortical circuits. However, why new clustered spines occur in specific locations and how their formation relates to learning and memory (L&M) remain unclear. Using in vivo two-photon microscopy, we track spine dynamics in retrosplenial cortex before, during, and after two forms of episodic-like learning and find that spine turnover before learning predicts future L&M performance, as well as the localization and rates of spine clustering. Consistent with the idea that these measures are causally related, a genetic manipulation that enhances spine turnover also enhances both L&M and spine clustering. Biophysically inspired modeling suggests turnover increases clustering, network sparsity, and memory capacity. These results support a hotspot model where spine turnover is the driver for localization of clustered spine formation, which serves to modulate network function, thus influencing storage capacity and L&M.

Structural remodeling of dendritic spines is thought to be a mechanism of memory storage. Here, the authors look at how spine turnover and clustering predict future learning and memory performance, and see that a genetically modified mouse with enhanced spine turnover has enhanced learning.

Linking Memories across Time via Neuronal and Dendritic Overlaps in Model Neurons with Active Dendrites

Memories are believed to be stored in distributed neuronal assemblies through activity-induced changes in synaptic and intrinsic properties. However, the specific mechanisms by which different memories become associated or linked remain a mystery. Here, we develop a simplified, biophysically inspired network model that incorporates multiple plasticity processes and explains linking of information at three different levels: (1) learning of a single associative memory, (2) rescuing of a weak memory when paired with a strong one, and (3) linking of multiple memories across time. By dissecting synaptic from intrinsic plasticity and neuron-wide from dendritically restricted protein capture, the model reveals a simple, unifying principle: linked memories share synaptic clusters within the dendrites of overlapping populations of neurons. The model generates numerous experimentally testable predictions regarding the cellular and sub-cellular properties of memory engrams as well as their spatiotemporal interactions.

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Network model with active dendrites and synaptic, somatic, homeostatic plasticity

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Linked memories are stored in overlapping populations of neurons

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Linked memories share synaptic clusters in common dendritic branches

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The locus of protein synthesis or capture shapes the structure of the memory trace

Activity-dependent expression of Channelrhodopsin at neuronal synapses

Increasing evidence points to the importance of dendritic spines in the formation and allocation of memories, and alterations of spine number and physiology are associated to memory and cognitive disorders. Modifications of the activity of subsets of synapses are believed to be crucial for memory establishment. However, the development of a method to directly test this hypothesis, by selectively controlling the activity of potentiated spines, is currently lagging. Here we introduce a hybrid RNA/protein approach to regulate the expression of a light-sensitive membrane channel at activated synapses, enabling selective tagging of potentiated spines following the encoding of a novel context in the hippocampus. This approach can be used to map potentiated synapses in the brain and will make it possible to re-activate the neuron only at previously activated synapses, extending current neuron-tagging technologies in the investigation of memory processes.