Activity-dependent clustering of functional synaptic inputs on developing hippocampal dendrites

Modeling the Emergence of Circuit Organization and Function during Development

Developing neural circuits show unique patterns of spontaneous activity and structured network connectivity shaped by diverse activity-dependent plasticity mechanisms. Based on extensive experimental work characterizing patterns of spontaneous activity in different brain regions over development, theoretical and computational models have played an important role in delineating the generation and function of individual features of spontaneous activity and their role in the plasticity-driven formation of circuit connectivity. Here, we review recent modeling efforts that explore how the developing cortex and hippocampus generate spontaneous activity, focusing on specific connectivity profiles and the gradual strengthening of inhibition as the key drivers behind the observed developmental changes in spontaneous activity. We then discuss computational models that mechanistically explore how different plasticity mechanisms use this spontaneous activity to instruct the formation and refinement of circuit connectivity, from the formation of single neuron receptive fields to sensory feature maps and recurrent architectures. We end by highlighting several open challenges regarding the functional implications of the discussed circuit changes, wherein models could provide the missing step linking immature developmental and mature adult information processing capabilities.

Dendritic growth and synaptic organization from activity-independent cues and local activity-dependent plasticity

Dendritic branching and synaptic organization shape single-neuron and network computations. How they emerge simultaneously during brain development as neurons become integrated into functional networks is still not mechanistically understood. Here, we propose a mechanistic model in which dendrite growth and the organization of synapses arise from the interaction of activity-independent cues from potential synaptic partners and local activity-dependent synaptic plasticity. Consistent with experiments, three phases of dendritic growth - overshoot, pruning, and stabilization - emerge naturally in the model. The model generates stellate-like dendritic morphologies that capture several morphological features of biological neurons under normal and perturbed learning rules, reflecting biological variability. Model-generated dendrites have approximately optimal wiring length consistent with experimental measurements. In addition to establishing dendritic morphologies, activity-dependent plasticity rules organize synapses into spatial clusters according to the correlated activity they experience. We demonstrate that a trade-off between activity-dependent and -independent factors influences dendritic growth and synaptic location throughout development, suggesting that early developmental variability can affect mature morphology and synaptic function. Therefore, a single mechanistic model can capture dendritic growth and account for the synaptic organization of correlated inputs during development. Our work suggests concrete mechanistic components underlying the emergence of dendritic morphologies and synaptic formation and removal in function and dysfunction, and provides experimentally testable predictions for the role of individual components.

Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks

Co-active or temporally ordered neural ensembles are a signature of salient sensory, motor, and cognitive events. Local convergence of such patterned activity as synaptic clusters on dendrites could help single neurons harness the potential of dendritic nonlinearities to decode neural activity patterns. We combined theory and simulations to assess the likelihood of whether projections from neural ensembles could converge onto synaptic clusters even in networks with random connectivity. Using rat hippocampal and cortical network statistics, we show that clustered convergence of axons from three to four different co-active ensembles is likely even in randomly connected networks, leading to representation of arbitrary input combinations in at least 10 target neurons in a 100,000 population. In the presence of larger ensembles, spatiotemporally ordered convergence of three to five axons from temporally ordered ensembles is also likely. These active clusters result in higher neuronal activation in the presence of strong dendritic nonlinearities and low background activity. We mathematically and computationally demonstrate a tight interplay between network connectivity, spatiotemporal scales of subcellular electrical and chemical mechanisms, dendritic nonlinearities, and uncorrelated background activity. We suggest that dendritic clustered and sequence computation is pervasive, but its expression as somatic selectivity requires confluence of physiology, background activity, and connectomics.

Local changes in potassium ions regulate input integration in active dendrites

During neuronal activity, the extracellular concentration of potassium ions ([K+]o) increases substantially above resting levels, yet it remains unclear what role these [K+]o changes play in the dendritic integration of synaptic inputs. We here used mathematical formulations and biophysical modeling to explore the role of synaptic activity-dependent K+ changes in dendritic segments of a visual cortex pyramidal neuron, receiving inputs tuned to stimulus orientation. We found that the spatial arrangement of inputs dictates the magnitude of [K+]o changes in the dendrites: Dendritic segments receiving similarly tuned inputs can attain substantially higher [K+]o increases than segments receiving diversely tuned inputs. These [K+]o elevations in turn increase dendritic excitability, leading to more robust and prolonged dendritic spikes. Ultimately, these local effects amplify the gain of neuronal input-output transformations, causing higher orientation-tuned somatic firing rates without compromising orientation selectivity. Our results suggest that local, activity-dependent [K+]o changes in dendrites may act as a "volume knob" that determines the impact of synaptic inputs on feature-tuned neuronal firing.

Clustering of synaptic engram: Functional and structural basis of memory

Studies on memory engram have demonstrated how experience and learning can be allocated at a neuronal level for centuries. Recently emerging evidence narrowed down further to the synaptic connections and their patterned allocation on dendrites. Notably, groups of synapses within a specific range within dendrites known as 'synaptic clusters' have been revealed in association with learning and memory. Previous investigations have shown that a variety of factors mediated by both presynaptic inputs and postsynaptic dendrites contribute to clustering. Here, we review the neural mechanism of synaptic clustering and its correlation with memory. We highlight the recent findings about the clustering of synaptic engrams and memory formation and discuss future directions.

Excitatory and inhibitory synapses show a tight subcellular correlation that weakens over development

Neurons receive correlated levels of excitation and inhibition, a feature that is important for proper brain function. However, how this relationship between excitatory and inhibitory inputs is established during the dynamic period of circuit wiring remains unexplored. Using multiple techniques, including in utero electroporation, electron microscopy, and electrophysiology, we reveal a tight correlation in the distribution of excitatory and inhibitory synapses along the dendrites of developing CA1 hippocampal neurons. This correlation was present within short dendritic stretches (<20 μm) and, surprisingly, was most pronounced during early development, sharply declining with maturity. The tight matching between excitation and inhibition was unexpected, as inhibitory synapses lacked an active zone when formed and exhibited compromised evoked release. We propose that inhibitory synapses form as a stabilizing scaffold to counterbalance growing excitation levels. This relationship diminishes over time, suggesting a critical role for a subcellular balance in early neuronal function and circuit formation.

Neuroprotective effects of riluzole in Alzheimer's disease: A comprehensive review

BACKGROUND

Despite several hundred clinical trials of drugs that initially showed promise, there has been limited clinical improvement in Alzheimer's disease (AD). This may be attributed to the existence of at least 25 abnormal cellular pathways that underlie the disease. It is improbable for a single drug to address all or most of these pathways, thus even drugs that show promise when administered alone are unlikely to produce significant results. According to previous studies, eight drugs, namely, dantrolene, erythropoietin, lithium, memantine, minocycline, piracetam, riluzole, and silymarin, have been found to target multiple pathways that are involved in the development of AD. Among these drugs, riluzole is currently indicated for the treatment of medical conditions in both adult patients and children and has gained increased attention from scientists due to its potential in the excitotoxic hypothesis of neurodegenerative diseases.

OBJECTIVE

The aim of this study was to investigate the effects of drugs on AD based on cellular and molecular mechanisms.

METHODS

The literature search for this study utilized the Scopus, ScienceDirect, PubMed, and Google Scholar databases to identify relevant articles.

RESULTS

Riluzole exerts its effects in AD through diverse pathways including the inhibition of voltage-dependent sodium and calcium channels, blocking AMPA and NMDA receptors and inhibiting the release of glutamic acid release and stimulation of EAAT1-EAAT2.

CONCLUSION

In this review article, we aimed to review the neuroprotective properties of riluzole, a glutamate modulator, in AD, which could benefit patients with the disease.

Context association in pyramidal neurons through local synaptic plasticity in apical dendrites

The unique characteristics of neocortical pyramidal neurons are thought to be crucial for many aspects of information processing and learning in the brain. Experimental data suggests that their segregation into two distinct compartments, the basal dendrites close to the soma and the apical dendrites branching out from the thick apical dendritic tuft, plays an essential role in cortical organization. A recent hypothesis states that layer 5 pyramidal cells associate top-down contextual information arriving at their apical tuft with features of the sensory input that predominantly arrives at their basal dendrites. It has however remained unclear whether such context association could be established by synaptic plasticity processes. In this work, we formalize the objective of such context association learning through a mathematical loss function and derive a plasticity rule for apical synapses that optimizes this loss. The resulting plasticity rule utilizes information that is available either locally at the synapse, through branch-local NMDA spikes, or through global Ca2+events, both of which have been observed experimentally in layer 5 pyramidal cells. We show in computer simulations that the plasticity rule enables pyramidal cells to associate top-down contextual input patterns with high somatic activity. Furthermore, it enables networks of pyramidal neuron models to perform context-dependent tasks and enables continual learning by allocating new dendritic branches to novel contexts.

VAP spatially stabilizes dendritic mitochondria to locally support synaptic plasticity

Synapses are pivotal sites of plasticity and memory formation. Consequently, synapses are energy consumption hotspots susceptible to dysfunction when their energy supplies are perturbed. Mitochondria are stabilized near synapses via the cytoskeleton and provide the local energy required for synaptic plasticity. However, the mechanisms that tether and stabilize mitochondria to support synaptic plasticity are unknown. We identified proteins exclusively tethering mitochondria to actin near postsynaptic spines. We find that VAP, the vesicle-associated membrane protein-associated protein implicated in amyotrophic lateral sclerosis, stabilizes mitochondria via actin near the spines. To test if the VAP-dependent stable mitochondrial compartments can locally support synaptic plasticity, we used two-photon glutamate uncaging for spine plasticity induction and investigated the induced and adjacent uninduced spines. We find VAP functions as a spatial stabilizer of mitochondrial compartments for up to ~60 min and as a spatial ruler determining the ~30 μm dendritic segment supported during synaptic plasticity.

Early life stress lastingly alters the function and AMPA-receptor composition of glutamatergic synapses in the hippocampus of male mice

Early postnatal life is a sensitive period of development that shapes brain structure and function later in life. Exposure to stress during this critical time window can alter brain development and may enhance the susceptibility to psychopathology and neurodegenerative disorders later in life. The developmental effects of early life stress (ELS) on synaptic function are not fully understood, but could provide mechanistic insights into how ELS modifies later brain function and disease risk. We here assessed the effects of ELS on synaptic function and composition in the hippocampus of male mice. Mice were subjected to ELS by housing dams and pups with limited bedding and nesting material from postnatal days (P) 2-9. Synaptic strength was measured in terms of miniature excitatory postsynaptic currents (mEPSCs) in the hippocampal dentate gyrus at three different developmental stages: the early postnatal phase (P9), preadolescence (P21, at weaning) and adulthood at 3 months of age (3MO). Hippocampal synaptosome fractions were isolated from P9 and 3MO tissue and analyzed for protein content to assess postsynaptic composition. Finally, dendritic spine density was assessed in the DG at 3MO. At P9, ELS increased mEPSC frequency and amplitude. In parallel, synaptic composition was altered as PSD-95, GluA3 and GluN2B content were significantly decreased. The increased mEPSC frequency was sustained up to 3MO, at which age, GluA3 content was significantly increased. No differences were found in dendritic spine density. These findings highlight how ELS affects the development of hippocampal synapses, which could provide valuable insight into mechanisms how ELS alters brain function later in life.

GABAergic-like dopamine synapses in the brain

Dopamine synapses play a crucial role in volitional movement and reward-related behaviors, while dysfunction of dopamine synapses causes various psychiatric and neurological disorders. Despite this significance, the true biological nature of dopamine synapses remains poorly understood. Here, we show that dopamine transmission is strongly correlated with GABA co-transmission across the brain and dopamine synapses are structured and function like GABAergic synapses with marked regional heterogeneity. In addition, GABAergic-like dopamine synapses are clustered on the dendrites, and GABA transmission at dopamine synapses has distinct physiological properties. Interestingly, the knockdown of neuroligin-2, a key postsynaptic protein at GABAergic synapses, unexpectedly does not weaken GABA co-transmission but instead facilitates it at dopamine synapses in the striatal neurons. More importantly, the attenuation of GABA co-transmission precedes deficits in dopaminergic transmission in animal models of Parkinson's disease. Our findings reveal the spatial and functional nature of GABAergic-like dopamine synapses in health and disease.

Stress and the risk of Alzheimer dementia: Can deconstructed engrams be rebuilt?

The exact neuropathological mechanism by which the dementia process unfolds is under intense scrutiny. The disease affects about 38 million people worldwide, 70% of which are clinically diagnosed with Alzheimer's disease (AD). If the destruction of synapses essential for learning, planning and decision-making is part of the problem, must the restoration of previously lost synapses be part of the solution? It is plausible that neuronal capacity to restitute information corresponds with the adaptive capacity of its connectivity reserve. A challenge will be to promote the functional connectivity that can compensate for the lost one. This will require better clarification of the remodeling of functional connectivity during the progression of AD dementia and its reversal upon experimental treatment. A major difficulty is to promote the neural pathways that are atrophied in AD dementia while suppressing others that are bolstered. Therapeutic strategies should aim at scaling functional connectivity to a just balance between the atrophic and hypertrophic systems. However, the exact factors that can help reach this objective are still unclear. Similarities between the effects of chronic stress and some neuropathological mechanisms underlying AD dementia support the idea that common components deserve prime attention as therapeutic targets.

The dendritic engram

Accumulating evidence from a wide range of studies, including behavioral, cellular, molecular and computational findings, support a key role of dendrites in the encoding and recall of new memories. Dendrites can integrate synaptic inputs in non-linear ways, provide the substrate for local protein synthesis and facilitate the orchestration of signaling pathways that regulate local synaptic plasticity. These capabilities allow them to act as a second layer of computation within the neuron and serve as the fundamental unit of plasticity. As such, dendrites are integral parts of the memory engram, namely the physical representation of memories in the brain and are increasingly studied during learning tasks. Here, we review experimental and computational studies that support a novel, dendritic view of the memory engram that is centered on non-linear dendritic branches as elementary memory units. We highlight the potential implications of dendritic engrams for the learning and memory field and discuss future research directions.

Asymmetric Voltage Attenuation in Dendrites Can Enable Hierarchical Heterosynaptic Plasticity

Long-term synaptic plasticity is mediated via cytosolic calcium concentrations ([Ca2+]). Using a synaptic model that implements calcium-based long-term plasticity via two sources of Ca2+ - NMDA receptors and voltage-gated calcium channels (VGCCs) - we show in dendritic cable simulations that the interplay between these two calcium sources can result in a diverse array of heterosynaptic effects. When spatially clustered synaptic input produces a local NMDA spike, the resulting dendritic depolarization can activate VGCCs at nonactivated spines, resulting in heterosynaptic plasticity. NMDA spike activation at a given dendritic location will tend to depolarize dendritic regions that are located distally to the input site more than dendritic sites that are proximal to it. This asymmetry can produce a hierarchical effect in branching dendrites, where an NMDA spike at a proximal branch can induce heterosynaptic plasticity primarily at branches that are distal to it. We also explored how simultaneously activated synaptic clusters located at different dendritic locations synergistically affect the plasticity at the active synapses, as well as the heterosynaptic plasticity of an inactive synapse "sandwiched" between them. We conclude that the inherent electrical asymmetry of dendritic trees enables sophisticated schemes for spatially targeted supervision of heterosynaptic plasticity.

Rapid learning of spatial representations for goal-directed navigation based on a novel model of hippocampal place fields

The discovery of place cells and other spatially modulated neurons in the hippocampal complex of rodents has been crucial to elucidating the neural basis of spatial cognition. More recently, the replay of neural sequences encoding previously experienced trajectories has been observed during consummatory behavior-potentially with implications for rapid learning, quick memory consolidation, and behavioral planning. Several promising models for robotic navigation and reinforcement learning have been proposed based on these and previous findings. Most of these models, however, use carefully engineered neural networks, and sometimes require long learning periods. In this paper, we present a self-organizing model incorporating place cells and replay, and demonstrate its utility for rapid one-shot learning in non-trivial environments with obstacles.

Organizing principles of astrocytic nanoarchitecture in the mouse cerebral cortex

Astrocytes are increasingly understood to be important regulators of central nervous system (CNS) function in health and disease; yet, we have little quantitative understanding of their complex architecture. While broad categories of astrocytic structures are known, the discrete building blocks that compose them, along with their geometry and organizing principles, are poorly understood. Quantitative investigation of astrocytic complexity is impeded by the absence of high-resolution datasets and robust computational approaches to analyze these intricate cells. To address this, we produced four ultra-high-resolution datasets of mouse cerebral cortex using serial electron microscopy and developed astrocyte-tailored computer vision methods for accurate structural analysis. We unearthed specific anatomical building blocks, structural motifs, connectivity hubs, and hierarchical organizations of astrocytes. Furthermore, we found that astrocytes interact with discrete clusters of synapses and that astrocytic mitochondria are distributed to lie closer to larger clusters of synapses. Our findings provide a geometrically principled, quantitative understanding of astrocytic nanoarchitecture and point to an unexpected level of complexity in how astrocytes interact with CNS microanatomy.

Introducing the Dendrify framework for incorporating dendrites to spiking neural networks

Computational modeling has been indispensable for understanding how subcellular neuronal features influence circuit processing. However, the role of dendritic computations in network-level operations remains largely unexplored. This is partly because existing tools do not allow the development of realistic and efficient network models that account for dendrites. Current spiking neural networks, although efficient, are usually quite simplistic, overlooking essential dendritic properties. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations. To bridge the gap between these two extremes and facilitate the adoption of dendritic features in spiking neural networks, we introduce Dendrify, an open-source Python package based on Brian 2. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more powerful neuromorphic systems.

Unraveling the mysteries of dendritic spine dynamics: Five key principles shaping memory and cognition

Recent research extends our understanding of brain processes beyond just action potentials and chemical transmissions within neural circuits, emphasizing the mechanical forces generated by excitatory synapses on dendritic spines to modulate presynaptic function. From in vivo and in vitro studies, we outline five central principles of synaptic mechanics in brain function: P1: Stability - Underpinning the integral relationship between the structure and function of the spine synapses. P2: Extrinsic dynamics - Highlighting synapse-selective structural plasticity which plays a crucial role in Hebbian associative learning, distinct from pathway-selective long-term potentiation (LTP) and depression (LTD). P3: Neuromodulation - Analyzing the role of G-protein-coupled receptors, particularly dopamine receptors, in time-sensitive modulation of associative learning frameworks such as Pavlovian classical conditioning and Thorndike's reinforcement learning (RL). P4: Instability - Addressing the intrinsic dynamics crucial to memory management during continual learning, spotlighting their role in "spine dysgenesis" associated with mental disorders. P5: Mechanics - Exploring how synaptic mechanics influence both sides of synapses to establish structural traces of short- and long-term memory, thereby aiding the integration of mental functions. We also delve into the historical background and foresee impending challenges.

Regulation of circuit organization and function through inhibitory synaptic plasticity

Diverse inhibitory neurons in the mammalian brain shape circuit connectivity and dynamics through mechanisms of synaptic plasticity. Inhibitory plasticity can establish excitation/inhibition (E/I) balance, control neuronal firing, and affect local calcium concentration, hence regulating neuronal activity at the network, single neuron, and dendritic level. Computational models can synthesize multiple experimental results and provide insight into how inhibitory plasticity controls circuit dynamics and sculpts connectivity by identifying phenomenological learning rules amenable to mathematical analysis. We highlight recent studies on the role of inhibitory plasticity in modulating excitatory plasticity, forming structured networks underlying memory formation and recall, and implementing adaptive phenomena and novelty detection. We conclude with experimental and modeling progress on the role of interneuron-specific plasticity in circuit computation and context-dependent learning.

Astrocytes Learn to Detect and Signal Deviations from Critical Brain Dynamics

Astrocytes are nonneuronal brain cells that were recently shown to actively communicate with neurons and are implicated in memory, learning, and regulation of cognitive states. Interestingly, these information processing functions are also closely linked to the brain's ability to self-organize at a critical phase transition. Investigating the mechanistic link between astrocytes and critical brain dynamics remains beyond the reach of cellular experiments, but it becomes increasingly approachable through computational studies. We developed a biologically plausible computational model of astrocytes to analyze how astrocyte calcium waves can respond to changes in underlying network dynamics. Our results suggest that astrocytes detect synaptic activity and signal directional changes in neuronal network dynamics using the frequency of their calcium waves. We show that this function may be facilitated by receptor scaling plasticity by enabling astrocytes to learn the approximate information content of input synaptic activity. This resulted in a computationally simple, information-theoretic model, which we demonstrate replicating the signaling functionality of the biophysical astrocyte model with receptor scaling. Our findings provide several experimentally testable hypotheses that offer insight into the regulatory role of astrocytes in brain information processing.

Loss of glucocorticoid receptor phosphorylation contributes to cognitive and neurocentric damages of the amyloid-β pathway

Aberrant cortisol and activation of the glucocorticoid receptor (GR) play an essential role in age-related progression of Alzheimer's disease (AD). However, the GR pathways required for influencing the pathobiology of AD dementia remain unknown. To address this, we studied an early phase of AD-like progression in the well-established APP/PS1 mouse model combined with targeted mutations in the BDNF-dependent GR phosphorylation sites (serines 134/267) using molecular, behavioral and neuroimaging approaches. We found that disrupting GR phosphorylation (S134A/S267A) in mice exacerbated the deleterious effects of the APP/PS1 genotype on mortality, neuroplasticity and cognition, without affecting either amyloid-β deposition or vascular pathology. The dynamics, maturation and retention of task-induced new dendritic spines of cortical excitatory neurons required GR phosphorylation at the BDNF-dependent sites that amyloid-β compromised. Parallel studies in postmortem human prefrontal cortex revealed AD subjects had downregulated BDNF signaling and concomitant upregulated cortisol pathway activation, which correlated with cognitive decline. These results provide key evidence that the loss of neurotrophin-mediated GR phosphorylation pathway promotes the detrimental effects of the brain cortisol response that contributes to the onset and/or progression of AD dementia. These findings have important translational implications as they provide a novel approach to treating AD dementia by identifying drugs that increase GR phosphorylation selectively at the neurotrophic sites to improve memory and cognition.

Learning binds new inputs into functional synaptic clusters via spinogenesis

Learning induces the formation of new excitatory synapses in the form of dendritic spines, but their functional properties remain unknown. Here, using longitudinal in vivo two-photon imaging and correlated electron microscopy of dendritic spines in the motor cortex of mice during motor learning, we describe a framework for the formation, survival and resulting function of new, learning-related spines. Specifically, our data indicate that the formation of new spines during learning is guided by the potentiation of functionally clustered preexisting spines exhibiting task-related activity during earlier sessions of learning. We present evidence that this clustered potentiation induces the local outgrowth of multiple filopodia from the nearby dendrite, locally sampling the adjacent neuropil for potential axonal partners, likely via targeting preexisting presynaptic boutons. Successful connections are then selected for survival based on co-activity with nearby task-related spines, ensuring that the new spine preserves functional clustering. The resulting locally coherent activity of new spines signals the learned movement. Furthermore, we found that a majority of new spines synapse with axons previously unrepresented in these dendritic domains. Thus, learning involves the binding of new information streams into functional synaptic clusters to subserve learned behaviors.

Epigenetic and Transcriptional Regulation of Spontaneous and Sensory Activity Dependent Programs During Neuronal Circuit Development

Spontaneous activity generated before the onset of sensory transduction has a key role in wiring developing sensory circuits. From axonal targeting, to synapse formation and elimination, to the balanced integration of neurons into developing circuits, this type of activity is implicated in a variety of cellular processes. However, little is known about its molecular mechanisms of action, especially at the level of genome regulation. Conversely, sensory experience-dependent activity implements well-characterized transcriptional and epigenetic chromatin programs that underlie heterogeneous but specific genomic responses that shape both postnatal circuit development and neuroplasticity in the adult. In this review, we focus on our knowledge of the developmental processes regulated by spontaneous activity and the underlying transcriptional mechanisms. We also review novel findings on how chromatin regulates the specificity and developmental induction of the experience-dependent program, and speculate their relevance for our understanding of how spontaneous activity may act at the genomic level to instruct circuit assembly and prepare developing neurons for sensory-dependent connectivity refinement and processing.

Compartment-specific tuning of dendritic feature selectivity by intracellular Ca2+ release

Dendritic calcium signaling is central to neural plasticity mechanisms that allow animals to adapt to the environment. Intracellular calcium release (ICR) from the endoplasmic reticulum has long been thought to shape these mechanisms. However, ICR has not been investigated in mammalian neurons in vivo. We combined electroporation of single CA1 pyramidal neurons, simultaneous imaging of dendritic and somatic activity during spatial navigation, optogenetic place field induction, and acute genetic augmentation of ICR cytosolic impact to reveal that ICR supports the establishment of dendritic feature selectivity and shapes integrative properties determining output-level receptive fields. This role for ICR was more prominent in apical than in basal dendrites. Thus, ICR cooperates with circuit-level architecture in vivo to promote the emergence of behaviorally relevant plasticity in a compartment-specific manner.

Learning as filtering: Implications for spike-based plasticity

Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as optimisation fails to account for a time-varying environment during the learning process and the resulting point estimate in parameter space does not account for uncertainty. Here, we frame learning as filtering, i.e., a principled method for including time and parameter uncertainty. We derive the filtering-based learning rule for a spiking neuronal network—the Synaptic Filter—and show its computational and biological relevance. For the computational relevance, we show that filtering improves the weight estimation performance compared to a gradient learning rule with optimal learning rate. The dynamics of the mean of the Synaptic Filter is consistent with spike-timing dependent plasticity (STDP) while the dynamics of the variance makes novel predictions regarding spike-timing dependent changes of EPSP variability. Moreover, the Synaptic Filter explains experimentally observed negative correlations between homo- and heterosynaptic plasticity.

The task of learning is commonly framed as parameter optimisation. Here, we adopt the framework of learning as filtering where the task is to continuously estimate the uncertainty about the parameters to be learned. We apply this framework to synaptic plasticity in a spiking neuronal network. Filtering includes a time-varying environment and parameter uncertainty on the level of the learning task. We show that learning as filtering can qualitatively explain two biological experiments on synaptic plasticity that cannot be explained by learning as optimisation. Moreover, we make a new prediction and improve performance with respect to a gradient learning rule. Thus, learning as filtering is a promising candidate for learning models.

Emergence of synaptic organization and computation in dendrites

Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons' dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.

Spatial regulation of coordinated excitatory and inhibitory synaptic plasticity at dendritic synapses

The induction of synaptic plasticity at an individual dendritic glutamatergic spine can affect neighboring spines. This local modulation generates dendritic plasticity microdomains believed to expand the neuronal computational capacity. Here, we investigate whether local modulation of plasticity can also occur between glutamatergic synapses and adjacent GABAergic synapses. We find that the induction of long-term potentiation at an individual glutamatergic spine causes the depression of nearby GABAergic inhibitory synapses (within 3 μm), whereas more distant ones are potentiated. Notably, L-type calcium channels and calpain are required for this plasticity spreading. Overall, our data support a model whereby input-specific glutamatergic postsynaptic potentiation induces a spatially regulated rearrangement of inhibitory synaptic strength in the surrounding area through short-range heterosynaptic interactions. Such local coordination of excitatory and inhibitory synaptic plasticity is expected to influence dendritic information processing and integration.

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LTP of individual dendritic spines causes iLTD at neighboring GABAergic synapses

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Interaction between single-spine LTP and iLTD occurs in the spatial range of ±3 μm

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This iLTD depends on the local dendritic calcium increase and calpain activation

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iLTD is associated with reduced gephyrin clustering and increased GABAAR mobility

Heterosynaptic Plasticity and the Experience-Dependent Refinement of Developing Neuronal Circuits

Neurons remodel the structure and strength of their synapses during critical periods of development in order to optimize both perception and cognition. Many of these developmental synaptic changes are thought to occur through synapse-specific homosynaptic forms of experience-dependent plasticity. However, homosynaptic plasticity can also induce or contribute to the plasticity of neighboring synapses through heterosynaptic interactions. Decades of research in vitro have uncovered many of the molecular mechanisms of heterosynaptic plasticity that mediate local compensation for homosynaptic plasticity, facilitation of further bouts of plasticity in nearby synapses, and cooperative induction of plasticity by neighboring synapses acting in concert. These discoveries greatly benefited from new tools and technologies that permitted single synapse imaging and manipulation of structure, function, and protein dynamics in living neurons. With the recent advent and application of similar tools for in vivo research, it is now feasible to explore how heterosynaptic plasticity contribute to critical periods and the development of neuronal circuits. In this review, we will first define the forms heterosynaptic plasticity can take and describe our current understanding of their molecular mechanisms. Then, we will outline how heterosynaptic plasticity may lead to meaningful refinement of neuronal responses and observations that suggest such mechanisms are indeed at work in vivo. Finally, we will use a well-studied model of cortical plasticity—ocular dominance plasticity during a critical period of visual cortex development—to highlight the molecular overlap between heterosynaptic and developmental forms of plasticity, and suggest potential avenues of future research.

Local dendritic balance enables learning of efficient representations in networks of spiking neurons

How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity works only under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex high-dimensional inputs and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo and suggest that both are crucial for representation learning.

Dendritic Branch-constrained N-Methyl-d-Aspartate Receptor-mediated Spikes Drive Synaptic Plasticity in Hippocampal CA3 Pyramidal Cells

N-methyl-d-aspartate receptor-mediated ( spikes can be causally linked to the induction of synaptic long-term potentiation (LTP) in hippocampal and cortical pyramidal cells. However, it is unclear if they regulate plasticity at a local or global scale in the dendritic tree. Here, we used dendritic patch-clamp recordings and calcium imaging to investigate the integrative properties of single dendrites of hippocampal CA3 cells. We show that local hyperpolarization of a single dendritic segment prevents NMDA spikes, their associated calcium transients, as well as LTP in a branch-specific manner. This result provides direct, causal evidence that the single dendritic branch can operate as a functional unit in regulating CA3 pyramidal cell plasticity.

Dual imaging of dendritic spines and mitochondria in vivo reveals hotspots of plasticity and metabolic adaptation to stress

Metabolic adaptation is a critical feature of synaptic plasticity. Indeed, synaptic plasticity requires the utilization and resupply of metabolites, in particular when the turnover is high and fast such as in stress conditions. What accounts for the localized energy burden of the post-synaptic compartment to the build up of chronic stress is currently not understood. We used in vivo microscopy of genetically encoded fluorescent probes to track changes of mitochondria, dendritic spines, ATP and H2O2 levels in pyramidal neurons of cortex before and after chronic unpredictable mild stress. Data revealed hotspots of postsynaptic mitochondria and dendritic spine turnover. Pharmacogenetic approach to force expression of the metabolic stress gene NR4A1 caused the fragmentation of postsynaptic mitochondria and loss of proximal dendritic spine clusters, whereas a dominant-negative mutant counteracted the effect of chronic stress. When fragmented, dendritic mitochondria produced lesser ATP at resting state and more on acute demand. This corresponded with significant production of mitochondrial H2O2 oxidative species in the dendritic compartment. Together, data indicate that pyramidal neurons adjust proximal dendritic spine turnover and mitochondria functions in keeping with synaptic demands.

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Addition of dendritic spine clusters match with more proximal mitochondria coverage.

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Loss of dendritic spine clusters match with less proximal mitochondria coverage.

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Dendrites alter spine dynamics, ATP and H202 production in keeping with excitation.

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In excess, the transcription factor NR4A1 promotes cross-clustering losses.

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Blocking NR4A1 prevents net cross-clustering losses mediated by chronic stress.

Axonal CB1 Receptors Mediate Inhibitory Bouton Formation via cAMP Increase and PKA

Experience-dependent formation and removal of inhibitory synapses are essential throughout life. For instance, GABAergic synapses are removed to facilitate learning, and strong excitatory activity is accompanied by the formation of inhibitory synapses to maintain coordination between excitation and inhibition. We recently discovered that active dendrites trigger the growth of inhibitory synapses via CB1 receptor-mediated endocannabinoid signaling, but the underlying mechanism remained unclear. Using two-photon microscopy to monitor the formation of individual inhibitory boutons in hippocampal organotypic slices from mice (both sexes), we found that CB1 receptor activation mediated the formation of inhibitory boutons and promoted their subsequent stabilization. Inhibitory bouton formation did not require neuronal activity and was independent of G_i/o-protein signaling, but was directly induced by elevating cAMP levels using forskolin and by activating G_s-proteins using DREADDs. Blocking PKA activity prevented CB1 receptor-mediated inhibitory bouton formation. Our findings reveal that axonal CB1 receptors signal via unconventional downstream pathways and that inhibitory bouton formation is triggered by an increase in axonal cAMP levels. Our results demonstrate an unexpected role for axonal CB1 receptors in axon-specific, and context-dependent, inhibitory synapse formation.SIGNIFICANCE STATEMENT Coordination between excitation and inhibition is required for proper brain function throughout life. It was previously shown that new inhibitory synapses can be formed in response to strong excitation to maintain this coordination, and this was mediated by endocannabinoid signaling via CB1 receptors. As activation of CB1 receptors generally results in the suppression of synaptic transmission, it remained unclear how CB1 receptors can mediate the formation of inhibitory synapses. Here we show that CB1 receptors on inhibitory axons signal via unconventional intracellular pathways and that inhibitory bouton formation is triggered by an increase in axonal cAMP levels and requires PKA activity. Our findings point to a central role for axonal cAMP signaling in activity-dependent inhibitory synapse formation.

Computation, wiring, and plasticity in synaptic clusters

Synaptic clusters on dendrites are extraordinarily compact computational building blocks. They contribute to key local computations through biophysical and biochemical signaling that utilizes convergence in space and time as an organizing principle. However, these computations can only arise in very special contexts. Dendritic cluster computations, their highly organized input connectivity, and the mechanisms for their formation are closely linked, yet these have not been analyzed as parts of a single process. Here, we examine these linkages. The sheer density of axonal and dendritic arborizations means that there are far more potential connections (close enough for a spine to reach an axon) than actual ones. We see how dendritic clusters draw upon electrical, chemical, and mechano-chemical signaling to implement the rules for formation of connections and subsequent computations. Crucially, the same mechanisms that underlie their functions also underlie their formation.

Activity-dependent regulation of mitochondrial motility in developing cortical dendrites

Developing neurons form synapses at a high rate. Synaptic transmission is very energy-demanding and likely requires ATP production by mitochondria nearby. Mitochondria might be targeted to active synapses in young dendrites, but whether such motility regulation mechanisms exist is unclear. We investigated the relationship between mitochondrial motility and neuronal activity in the primary visual cortex of young mice in vivo and in slice cultures. During the first 2 postnatal weeks, mitochondrial motility decreases while the frequency of neuronal activity increases. Global calcium transients do not affect mitochondrial motility. However, individual synaptic transmission events precede local mitochondrial arrest. Pharmacological stimulation of synaptic vesicle release, but not focal glutamate application alone, stops mitochondria, suggesting that an unidentified factor co-released with glutamate is required for mitochondrial arrest. A computational model of synaptic transmission-mediated mitochondrial arrest shows that the developmental increase in synapse number and transmission frequency can contribute substantially to the age-dependent decrease of mitochondrial motility.

Synapses from the Motor Cortex and a High-Order Thalamic Nucleus are Spatially Clustered in Proximity to Each Other in the Distal Tuft Dendrites of Mouse Somatosensory Cortex

The posterior medial nucleus of the thalamus (POm) and vibrissal primary motor cortex (vM1) convey essential information to the barrel cortex (S1BF) regarding whisker position and movement. Therefore, understanding the relative spatial relationship of these two inputs is a critical prerequisite for acquiring insights into how S1BF synthesizes information to interpret the location of an object. Using array tomography, we identified the locations of synapses from vM1 and POm on distal tuft dendrites of L5 pyramidal neurons where the two inputs are combined. Synapses from vM1 and POm did not show a significant branchlet preference and impinged on the same set of dendritic branchlets. Within dendritic branches, on the other hand, the two inputs formed robust spatial clusters of their own type. Furthermore, we also observed POm clusters in proximity to vM1 clusters. This work constitutes the first detailed description of the relative distribution of synapses from POm and vM1, which is crucial to elucidate the synaptic integration of whisker-based sensory information.

Stress-primed secretory autophagy promotes extracellular BDNF maturation by enhancing MMP9 secretion

The stress response is an essential mechanism for maintaining homeostasis, and its disruption is implicated in several psychiatric disorders. On the cellular level, stress activates, among other mechanisms, autophagy that regulates homeostasis through protein degradation and recycling. Secretory autophagy is a recently described pathway in which autophagosomes fuse with the plasma membrane rather than with lysosomes. Here, we demonstrate that glucocorticoid-mediated stress enhances secretory autophagy via the stress-responsive co-chaperone FK506-binding protein 51. We identify the matrix metalloproteinase 9 (MMP9) as one of the proteins secreted in response to stress. Using cellular assays and in vivo microdialysis, we further find that stress-enhanced MMP9 secretion increases the cleavage of pro-brain-derived neurotrophic factor (proBDNF) to its mature form (mBDNF). BDNF is essential for adult synaptic plasticity and its pathway is associated with major depression and posttraumatic stress disorder. These findings unravel a cellular stress adaptation mechanism that bears the potential of opening avenues for the understanding of the pathophysiology of stress-related disorders.

Emergence of local and global synaptic organization on cortical dendrites

Synaptic inputs on cortical dendrites are organized with remarkable subcellular precision at the micron level. This organization emerges during early postnatal development through patterned spontaneous activity and manifests both locally where nearby synapses are significantly correlated, and globally with distance to the soma. We propose a biophysically motivated synaptic plasticity model to dissect the mechanistic origins of this organization during development and elucidate synaptic clustering of different stimulus features in the adult. Our model captures local clustering of orientation in ferret and receptive field overlap in mouse visual cortex based on the receptive field diameter and the cortical magnification of visual space. Including action potential back-propagation explains branch clustering heterogeneity in the ferret and produces a global retinotopy gradient from soma to dendrite in the mouse. Therefore, by combining activity-dependent synaptic competition and species-specific receptive fields, our framework explains different aspects of synaptic organization regarding stimulus features and spatial scales.

Dendritic Spine Density and Dynamics of Layer 5 Pyramidal Neurons of the Primary Motor Cortex Are Elevated With Aging

It is well established that motor impairment often occurs alongside healthy aging, leading to problems with fine motor skills and coordination. Although previously thought to be caused by neuronal death accumulating across the lifespan, it is now believed that the source of this impairment instead stems from more subtle changes in neural connectivity. The dendritic spine is a prime target for exploration of this problem because it is the postsynaptic partner of most excitatory synapses received by the pyramidal neuron, a cortical cell that carries much of the information processing load in the cerebral cortex. We repeatedly imaged the same dendrites in young adult and aged mouse motor cortex over the course of 1 month to look for differences in the baseline state of the dendritic spine population. These experiments reveal increased dendritic spine density, without obvious changes in spine clustering, occurring at the aged dendrite. Additionally, aged dendrites exhibit elevated spine turnover and stabilization alongside decreased long-term spine survival. These results suggest that at baseline the aged motor cortex may exist in a perpetual state of relative instability and attempts at compensation. This phenotype of aging may provide clues for future targets of aging-related motor impairment remediation.

The gradient clusteron: A model neuron that learns to solve classification tasks via dendritic nonlinearities, structural plasticity, and gradient descent

Synaptic clustering on neuronal dendrites has been hypothesized to play an important role in implementing pattern recognition. Neighboring synapses on a dendritic branch can interact in a synergistic, cooperative manner via nonlinear voltage-dependent mechanisms, such as NMDA receptors. Inspired by the NMDA receptor, the single-branch clusteron learning algorithm takes advantage of location-dependent multiplicative nonlinearities to solve classification tasks by randomly shuffling the locations of "under-performing" synapses on a model dendrite during learning ("structural plasticity"), eventually resulting in synapses with correlated activity being placed next to each other on the dendrite. We propose an alternative model, the gradient clusteron, or G-clusteron, which uses an analytically-derived gradient descent rule where synapses are "attracted to" or "repelled from" each other in an input- and location-dependent manner. We demonstrate the classification ability of this algorithm by testing it on the MNIST handwritten digit dataset and show that, when using a softmax activation function, the accuracy of the G-clusteron on the all-versus-all MNIST task (~85%) approaches that of logistic regression (~93%). In addition to the location update rule, we also derive a learning rule for the synaptic weights of the G-clusteron ("functional plasticity") and show that a G-clusteron that utilizes the weight update rule can achieve ~89% accuracy on the MNIST task. We also show that a G-clusteron with both the weight and location update rules can learn to solve the XOR problem from arbitrary initial conditions.

Synaptic modifications in learning and memory - A dendritic spine story

Synapses are specialized sites where neurons connect and communicate with each other. Activity-dependent modification of synaptic structure and function provides a mechanism for learning and memory. The advent of high-resolution time-lapse imaging in conjunction with fluorescent biosensors and actuators enables researchers to monitor and manipulate the structure and function of synapses both in vitro and in vivo. This review focuses on recent imaging studies on the synaptic modification underlying learning and memory.

Freeze-Frame Imaging of Dendritic Calcium Signals With TubuTag

The extensive dendritic arbor of neurons is thought to be actively involved in the processing of information. Dendrites contain a rich diversity of ligand- and voltage-activated ion channels as well as metabotropic receptors. In addition, they are capable of releasing calcium from intracellular stores. Under specific conditions, large neurons produce calcium spikes that are locally restricted to a dendritic section. To investigate calcium signaling in dendrites, we introduce TubuTag, a genetically encoded ratiometric calcium sensor anchored to the cytoskeleton. TubuTag integrates cytoplasmic calcium signals by irreversible photoconversion from green to red fluorescence when illuminated with violet light. We used a custom two-photon microscope with a large field of view to image pyramidal neurons in CA1 at subcellular resolution. Photoconversion was strongest in the most distal parts of the apical dendrite, suggesting a gradient in the amplitude of dendritic calcium signals. As the read-out of fluorescence can be performed several hours after photoconversion, TubuTag will help investigating dendritic signal integration and calcium homeostasis in large populations of neurons.

Anatomy and activity patterns in a multifunctional motor neuron and its surrounding circuits

Dorsal Excitor motor neuron DE-3 in the medicinal leech plays three very different dynamical roles in three different behaviors. Without rewiring its anatomical connectivity, how can a motor neuron dynamically switch roles to play appropriate roles in various behaviors? We previously used voltage-sensitive dye imaging to record from DE-3 and most other neurons in the leech segmental ganglion during (fictive) swimming, crawling, and local-bend escape (Tomina and Wagenaar, 2017). Here, we repeated that experiment, then re-imaged the same ganglion using serial blockface electron microscopy and traced DE-3's processes. Further, we traced back the processes of DE-3's presynaptic partners to their respective somata. This allowed us to analyze the relationship between circuit anatomy and the activity patterns it sustains. We found that input synapses important for all the behaviors were widely distributed over DE-3's branches, yet that functional clusters were different during (fictive) swimming vs. crawling.

Photoactivatable CaMKII induces synaptic plasticity in single synapses

Optogenetic approaches for studying neuronal functions have proven their utility in the neurosciences. However, optogenetic tools capable of inducing synaptic plasticity at the level of single synapses have been lacking. Here, we engineered a photoactivatable (pa)CaMKII by fusing a light-sensitive domain, LOV2, to CaMKIIα. Blue light or two-photon excitation reversibly activated paCaMKII. Activation in single spines was sufficient to induce structural long-term potentiation (sLTP) in vitro and in vivo. paCaMKII activation was also sufficient for the recruitment of AMPA receptors and functional LTP in single spines. By combining paCaMKII with protein activity imaging by 2-photon FLIM-FRET, we demonstrate that paCaMKII activation in clustered spines induces robust sLTP via a mechanism that involves the actin-regulatory small GTPase, Cdc42. This optogenetic tool for dissecting the function of CaMKII activation (i.e., the sufficiency of CaMKII rather than necessity) and for manipulating synaptic plasticity will find many applications in neuroscience and other fields.

Optogenetic control of molecules is important in cell biology and neuroscience. Here, the authors describe an optogenetic tool to control the Ca^²+/calmodulin-dependent protein kinase II and use it to control plasticity at the single synapse level.

Heterosynaptic cross-talk of pre- and postsynaptic strengths along segments of dendrites

Dendrites are crucial for integrating incoming synaptic information. Individual dendritic branches are thought to constitute a signal processing unit, yet how neighboring synapses shape the boundaries of functional dendritic units is not well understood. Here, we address the cellular basis underlying the organization of the strengths of neighboring Schaffer collateral-CA1 synapses by optical quantal analysis and spine size measurements. Inducing potentiation at clusters of spines produces NMDA-receptor-dependent heterosynaptic plasticity. The direction of postsynaptic strength change shows distance dependency to the stimulated synapses where proximal synapses predominantly depress, whereas distal synapses potentiate; potentiation and depression are regulated by CaMKII and calcineurin, respectively. In contrast, heterosynaptic presynaptic plasticity is confined to weakening of presynaptic strength of nearby synapses, which requires CaMKII and the retrograde messenger nitric oxide. Our findings highlight the parallel engagement of multiple signaling pathways, each with characteristic spatial dynamics in shaping the local pattern of synaptic strengths.

Local glutamate-mediated dendritic plateau potentials change the state of the cortical pyramidal neuron

Dendritic spikes in thin dendritic branches (basal and oblique dendrites) are traditionally inferred from spikelets measured in the cell body. Here, we used laser-spot voltage-sensitive dye imaging in cortical pyramidal neurons (rat brain slices) to investigate the voltage waveforms of dendritic potentials occurring in response to spatially restricted glutamatergic inputs. Local dendritic potentials lasted 200-500 ms and propagated to the cell body, where they caused sustained 10- to 20-mV depolarizations. Plateau potentials propagating from dendrite to soma and action potentials propagating from soma to dendrite created complex voltage waveforms in the middle of the thin basal dendrite, comprised of local sodium spikelets, local plateau potentials, and backpropagating action potentials, superimposed on each other. Our model replicated these voltage waveforms across a gradient of glutamatergic stimulation intensities. The model then predicted that somatic input resistance (Rin) and membrane time constant (tau) may be reduced during dendritic plateau potential. We then tested these model predictions in real neurons and found that the model correctly predicted the direction of Rin and tau change but not the magnitude. In summary, dendritic plateau potentials occurring in basal and oblique branches put pyramidal neurons into an activated neuronal state ("prepared state"), characterized by depolarized membrane potential and smaller but faster membrane responses. The prepared state provides a time window of 200-500 ms, during which cortical neurons are particularly excitable and capable of following afferent inputs. At the network level, this predicts that sets of cells with simultaneous plateaus would provide cellular substrate for the formation of functional neuronal ensembles.NEW & NOTEWORTHY In cortical pyramidal neurons, we recorded glutamate-mediated dendritic plateau potentials with voltage imaging and created a computer model that recreated experimental measures from dendrite and cell body. Our model made new predictions, which were then tested in experiments. Plateau potentials profoundly change neuronal state: a plateau potential triggered in one basal dendrite depolarizes the soma and shortens membrane time constant, making the cell more susceptible to firing triggered by other afferent inputs.

My Neighbour Hetero-deconstructing the mechanisms underlying heterosynaptic plasticity

Synapses change in strength following patterns of activity, but in many cases seemingly inactive neighbouring synapses also undergo changes in strength. These heterosynaptic changes occur across developmental time-points in various brain circuits in different species, but their precise molecular mechanisms are not well understood. Additionally, heterosynaptic changes can mirror homosynaptic plasticity or occur in opposition to homosynaptic changes. In this review we consider what useful functionality heterosynaptic dynamics could potentially endow the circuit with, and the underlying signalling events that implement heterosynaptic changes. We discuss what unanswered questions remain, and what the future looks like for understanding the logic of synaptic plasticity.

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.

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.

Synergistic excitability plasticity in cerebellar functioning

The cerebellum, a universal processor for sensory acquisition and internal models, and its association with synaptic and nonsynaptic plasticity have been envisioned as the biological correlates of learning, perception, and even thought. Indeed, the cerebellum is no longer considered merely as the locus of motor coordination and its learning. Here, we introduce the mechanisms underlying the induction of multiple types of plasticity in cerebellar circuit and give an overview focusing on the plasticity of nonsynaptic intrinsic excitability. The discovery of long-term potentiation of synaptic responsiveness in hippocampal neurons led investigations into changes of their intrinsic excitability. This activity-dependent potentiation of neuronal excitability is distinct from that of synaptic efficacy. Systematic examination of excitability plasticity has indicated that the modulation of various types of Ca²⁺ - and voltage-dependent K⁺ channels underlies the phenomenon, which is also triggered by immune activity. Intrinsic plasticity is expressed specifically on dendrites and modifies the integrative processing and filtering effect. In Purkinje cells, modulation of the discordance of synaptic current on soma and dendrite suggested a novel type of cellular learning mechanism. This property enables a plausible synergy between synaptic efficacy and intrinsic excitability, by amplifying electrical conductivity and influencing the polarity of bidirectional synaptic plasticity. Furthermore, the induction of intrinsic plasticity in the cerebellum correlates with motor performance and cognitive processes, through functional connections from the cerebellar nuclei to neocortex and associated regions: for example, thalamus and midbrain. Taken together, recent advances in neuroscience have begun to shed light on the complex functioning of nonsynaptic excitability and the synergy.