Impermanence of dendritic spines in live adult CA1 hippocampus

Repeated stress exposure leads to structural synaptic instability prior to disorganization of hippocampal coding and impairments in learning

Stress exposure impairs brain structure and function, resulting in cognitive deficits and increased risk for psychiatric disorders such as depression, schizophrenia, anxiety and post-traumatic stress disorder. In particular, stress exposure affects function and structure of hippocampal CA1 leading to impairments in episodic memory. Here, we applied longitudinal deep-brain optical imaging to investigate the link between changes in activity patterns and structural plasticity of dorsal CA1 pyramidal neurons and hippocampal-dependent learning and memory in mice exposed to stress. We found that several days of repeated stress led to a substantial increase in neuronal activity followed by disruption of the temporal structure of this activity and spatial coding. We then tracked dynamics of structural excitatory connectivity as a potential underlying cause of the changes in activity induced by repeated stress. We thus discovered that exposure to repeated stress leads to an immediate decrease in spinogenesis followed by decrease in spine stability. By comparison, acute stress led to stabilization of the spines born in temporal proximity to the stressful event. Importantly, the temporal relationship between changes in activity levels, structural connectivity and activity patterns, suggests that loss of structural connectivity mediates the transition between increased activity and impairment of temporal organization and spatial information content in dorsal CA1 upon repeated stress exposure.

CaMKII: a central molecular organizer of synaptic plasticity, learning and memory

Calcium-calmodulin (CaM)-dependent protein kinase II (CaMKII) is the most abundant protein in excitatory synapses and is central to synaptic plasticity, learning and memory. It is activated by intracellular increases in calcium ion levels and triggers molecular processes necessary for synaptic plasticity. CaMKII phosphorylates numerous synaptic proteins, thereby regulating their structure and functions. This leads to molecular events crucial for synaptic plasticity, such as receptor trafficking, localization and activity; actin cytoskeletal dynamics; translation; and even transcription through synapse-nucleus shuttling. Several new tools affording increasingly greater spatiotemporal resolution have revealed the link between CaMKII activity and downstream signalling processes in dendritic spines during synaptic and behavioural plasticity. These technologies have provided insights into the function of CaMKII in learning and memory.

Representational drift: Emerging theories for continual learning and experimental future directions

Recent work has revealed that the neural activity patterns correlated with sensation, cognition, and action often are not stable and instead undergo large scale changes over days and weeks-a phenomenon called representational drift. Here, we highlight recent observations of drift, how drift is unlikely to be explained by experimental confounds, and how the brain can likely compensate for drift to allow stable computation. We propose that drift might have important roles in neural computation to allow continual learning, both for separating and relating memories that occur at distinct times. Finally, we present an outlook on future experimental directions that are needed to further characterize drift and to test emerging theories for drift's role in computation.

Stochastic consolidation of lifelong memory

Humans have the remarkable ability to continually store new memories, while maintaining old memories for a lifetime. How the brain avoids catastrophic forgetting of memories due to interference between encoded memories is an open problem in computational neuroscience. Here we present a model for continual learning in a recurrent neural network combining Hebbian learning, synaptic decay and a novel memory consolidation mechanism: memories undergo stochastic rehearsals with rates proportional to the memory's basin of attraction, causing self-amplified consolidation. This mechanism gives rise to memory lifetimes that extend much longer than the synaptic decay time, and retrieval probability of memories that gracefully decays with their age. The number of retrievable memories is proportional to a power of the number of neurons. Perturbations to the circuit model cause temporally-graded retrograde and anterograde deficits, mimicking observed memory impairments following neurological trauma.

Voltage-Gated Ion Channels and the Variability in Information Transfer

The prerequisites for neurons to function within a circuit and be able to contain and transfer information efficiently and reliably are that they need to be homeostatically stable and fire within a reasonable range, characteristics that are governed, among others, by voltage-gated ion channels (VGICs). Nonetheless, neurons entail large variability in the expression levels of VGICs and their corresponding intrinsic properties, but the role of this variability in information transfer is not fully known. In this study, we aimed to investigate how this variability of VGICs affects information transfer. For this, we used a previously derived population of neuronal model neurons, each with the variable expression of five types of VGICs, fast Na+, delayed rectifier K+, A-type K+, T-type Ca++, and HCN channels. These analyses showed that the model neurons displayed variability in mutual information transfer, measured as the capability of neurons to successfully encode incoming synaptic information in output firing frequencies. Likewise, variability in the expression of VGICs caused variability in EPSPs and IPSPs amplitudes, reflected in the variability of output firing frequencies. Finally, using the virtual knockout methodology, we show that among the ion channels tested, the A-type K+ channel is the major regulator of information processing and transfer.

An interactive time series image analysis software for dendritic spines

Live fluorescence imaging has demonstrated the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine correlates with its functional efficacy. Learning and memory studies have shown that a great deal of the information stored by a neuron is contained in the synapses. High precision tracking of synaptic structures can give hints about the dynamic nature of memory and help us understand how memories evolve both in biological and artificial neural networks. Experiments that aim to investigate the dynamics behind the structural changes of dendritic spines require the collection and analysis of large time-series datasets. In this paper, we present an open-source software called SpineS for automatic longitudinal structural analysis of dendritic spines with additional features for manual intervention to ensure optimal analysis. We have tested the algorithm on in-vitro, in-vivo, and simulated datasets to demonstrate its performance in a wide range of possible experimental scenarios.

Long-term transverse imaging of the hippocampus with glass microperiscopes

The hippocampus consists of a stereotyped neuronal circuit repeated along the septal-temporal axis. This transverse circuit contains distinct subfields with stereotyped connectivity that support crucial cognitive processes, including episodic and spatial memory. However, comprehensive measurements across the transverse hippocampal circuit in vivo are intractable with existing techniques. Here, we developed an approach for two-photon imaging of the transverse hippocampal plane in awake mice via implanted glass microperiscopes, allowing optical access to the major hippocampal subfields and to the dendritic arbor of pyramidal neurons. Using this approach, we tracked dendritic morphological dynamics on CA1 apical dendrites and characterized spine turnover. We then used calcium imaging to quantify the prevalence of place and speed cells across subfields. Finally, we measured the anatomical distribution of spatial information, finding a non-uniform distribution of spatial selectivity along the DG-to-CA1 axis. This approach extends the existing toolbox for structural and functional measurements of hippocampal circuitry.

Region-Specific Characteristics of Astrocytes and Microglia: A Possible Involvement in Aging and Diseases

Although different regions of the brain are dedicated to specific functions, the intra- and inter-regional heterogeneity of astrocytes and microglia in these regions has not yet been fully understood. Recently, an advancement in various technologies, such as single-cell RNA sequencing, has allowed for the discovery of astrocytes and microglia with distinct molecular fingerprints and varying functions in the brain. In addition, the regional heterogeneity of astrocytes and microglia exhibits different functions in several situations, such as aging and neurodegenerative diseases. Therefore, investigating the region-specific astrocytes and microglia is important in understanding the overall function of the brain. In this review, we summarize up-to-date research on various intra- and inter-regional heterogeneities of astrocytes and microglia, and provide information on how they can be applied to aging and neurodegenerative diseases.

Geometric framework to predict structure from function in neural networks

Neural computation in biological and artificial networks relies on the nonlinear summation of many inputs. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function, but quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of threshold-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate the solution space of all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. A generalization accounting for noise further reveals that the solution space geometry can undergo topological transitions as the allowed error increases, which could provide insight into both neuroscience and machine learning. We ultimately use this geometric characterization to derive certainty conditions guaranteeing a nonzero synapse between neurons. Our theoretical framework could thus be applied to neural activity data to make rigorous anatomical predictions that follow generally from the model architecture.

Drifting neuronal representations: Bug or feature?

The brain displays a remarkable ability to sustain stable memories, allowing animals to execute precise behaviors or recall stimulus associations years after they were first learned. Yet, recent long-term recording experiments have revealed that single-neuron representations continuously change over time, contravening the classical assumption that learned features remain static. How do unstable neural codes support robust perception, memories, and actions? Here, we review recent experimental evidence for such representational drift across brain areas, as well as dissections of its functional characteristics and underlying mechanisms. We emphasize theoretical proposals for how drift need not only be a form of noise for which the brain must compensate. Rather, it can emerge from computationally beneficial mechanisms in hierarchical networks performing robust probabilistic computations.

Keeping synapses in shape: degradation pathways in the healthy and aging brain

Synapses maintain their molecular composition, plasticity and function through the concerted action of protein synthesis and removal. The complex and polarized neuronal architecture poses specific challenges to the logistics of protein and organelle turnover since protein synthesis and degradation mainly happen in the cell soma. In addition, post-mitotic neurons accumulate damage over a lifetime, challenging neuronal degradative pathways and making them particularly susceptible to the effects of aging. This review will summarize the current knowledge on neuronal protein turnover mechanisms with a particular focus on the presynapse, including the proteasome, autophagy and the endolysosomal route and their roles in regulating presynaptic proteostasis and function. In addition, the author will discuss how physiological brain aging, which entails a progressive decline in cognitive functions, affects synapses and the degradative machinery.

A Long-Term Clearing Cranial Window for Longitudinal Imaging of Cortical and Calvarial Ischemic Injury through the Intact Skull

Skull is a reservoir for supplying immune cells that mediate brain immune surveillance. However, during intravital optical imaging of brain, conventional cranial windows requiring skull thinning or removal disrupt brain immunity integrity. Here, a novel long-term clearing cranial window (LCCW) based on the intact skull, dedicated to chronic skull transparency maintenance, is proposed. It significantly improves optical imaging resolution and depth, by which the cortical and calvarial vascular injury and regeneration processes after ischemic injury are longitudinally monitored in awake mice. Results show that calvarial blood vessels recover earlier than the cortex. And the transcriptome analysis reveals that gene expression patterns and immune cells abundances exist substantial differences between brain and skull after ischemic injury, which may be one of the causes for the time lag between their vascular recovery. These findings bring great enlightenment to vascular regeneration and reconstruction. Moreover, LCCW provides a minimally invasive approach for imaging the brain and skull bone marrow.

Training a spiking neuronal network model of visual-motor cortex to play a virtual racket-ball game using reinforcement learning

Recent models of spiking neuronal networks have been trained to perform behaviors in static environments using a variety of learning rules, with varying degrees of biological realism. Most of these models have not been tested in dynamic visual environments where models must make predictions on future states and adjust their behavior accordingly. The models using these learning rules are often treated as black boxes, with little analysis on circuit architectures and learning mechanisms supporting optimal performance. Here we developed visual/motor spiking neuronal network models and trained them to play a virtual racket-ball game using several reinforcement learning algorithms inspired by the dopaminergic reward system. We systematically investigated how different architectures and circuit-motifs (feed-forward, recurrent, feedback) contributed to learning and performance. We also developed a new biologically-inspired learning rule that significantly enhanced performance, while reducing training time. Our models included visual areas encoding game inputs and relaying the information to motor areas, which used this information to learn to move the racket to hit the ball. Neurons in the early visual area relayed information encoding object location and motion direction across the network. Neuronal association areas encoded spatial relationships between objects in the visual scene. Motor populations received inputs from visual and association areas representing the dorsal pathway. Two populations of motor neurons generated commands to move the racket up or down. Model-generated actions updated the environment and triggered reward or punishment signals that adjusted synaptic weights so that the models could learn which actions led to reward. Here we demonstrate that our biologically-plausible learning rules were effective in training spiking neuronal network models to solve problems in dynamic environments. We used our models to dissect the circuit architectures and learning rules most effective for learning. Our model shows that learning mechanisms involving different neural circuits produce similar performance in sensory-motor tasks. In biological networks, all learning mechanisms may complement one another, accelerating the learning capabilities of animals. Furthermore, this also highlights the resilience and redundancy in biological systems.

Understanding the physical basis of memory: Molecular mechanisms of the engram

Memory, defined as the storage and use of learned information in the brain, is necessary to modulate behavior and critical for animals to adapt to their environments and survive. Despite being a cornerstone of brain function, questions surrounding the molecular and cellular mechanisms of how information is encoded, stored, and recalled remain largely unanswered. One widely held theory is that an engram is formed by a group of neurons that are active during learning, which undergoes biochemical and physical changes to store information in a stable state, and that are later reactivated during recall of the memory. In the past decade, the development of engram labeling methodologies has proven useful to investigate the biology of memory at the molecular and cellular levels. Engram technology allows the study of individual memories associated with particular experiences and their evolution over time, with enough experimental resolution to discriminate between different memory processes: learning (encoding), consolidation (the passage from short-term to long-term memories), and storage (the maintenance of memory in the brain). Here, we review the current understanding of memory formation at a molecular and cellular level by focusing on insights provided using engram technology.

Dendritic Excitability and Synaptic Plasticity In Vitro and In Vivo

Much of our understanding of dendritic and synaptic physiology comes from in vitro experimentation, where the afforded mechanical stability and convenience of applying drugs allowed patch-clamping based recording techniques to investigate ion channel distributions, their gating kinetics, and to uncover dendritic integrative and synaptic plasticity rules. However, with current efforts to study these questions in vivo, there is a great need to translate existing knowledge between in vitro and in vivo experimental conditions. In this review, we identify discrepancies between in vitro and in vivo ionic composition of extracellular media and discuss how changes in ionic composition alter dendritic excitability and plasticity induction. Here, we argue that under physiological in vivo ionic conditions, dendrites are expected to be more excitable and the threshold for synaptic plasticity induction to be lowered. Consequently, the plasticity rules described in vitro vary significantly from those implemented in vivo.

Deletion of Calsyntenin-3, an atypical cadherin, suppresses inhibitory synapses but increases excitatory parallel-fiber synapses in cerebellum

Cadherins contribute to the organization of nearly all tissues, but the functions of several evolutionarily conserved cadherins, including those of calsyntenins, remain enigmatic. Puzzlingly, two distinct, non-overlapping functions for calsyntenins were proposed: As postsynaptic neurexin ligands in synapse formation, or as presynaptic kinesin adaptors in vesicular transport. Here, we show that, surprisingly, acute CRISPR-mediated deletion of calsyntenin-3 in mouse cerebellum in vivo causes a large decrease in inhibitory synapse, but a robust increase in excitatory parallel-fiber synapses in Purkinje cells. As a result, inhibitory synaptic transmission was suppressed, whereas parallel-fiber synaptic transmission was enhanced in Purkinje cells by the calsyntenin-3 deletion. No changes in the dendritic architecture of Purkinje cells or in climbing-fiber synapses were detected. Sparse selective deletion of calsyntenin-3 only in Purkinje cells recapitulated the synaptic phenotype, indicating that calsyntenin-3 acts by a cell-autonomous postsynaptic mechanism in cerebellum. Thus, by inhibiting formation of excitatory parallel-fiber synapses and promoting formation of inhibitory synapses in the same neuron, calsyntenin-3 functions as a postsynaptic adhesion molecule that regulates the excitatory/inhibitory balance in Purkinje cells.

A subpopulation of cortical VIP-expressing interneurons with highly dynamic spines

Structural synaptic plasticity may underlie experience and learning-dependent changes in cortical circuits. In contrast to excitatory pyramidal neurons, insight into the structural plasticity of inhibitory neurons remains limited. Interneurons are divided into various subclasses, each with specialized functions in cortical circuits. Further knowledge of subclass-specific structural plasticity of interneurons is crucial to gaining a complete mechanistic understanding of their contribution to cortical plasticity overall. Here, we describe a subpopulation of superficial cortical multipolar interneurons expressing vasoactive intestinal peptide (VIP) with high spine densities on their dendrites located in layer (L) 1, and with the electrophysiological characteristics of bursting cells. Using longitudinal imaging in vivo, we found that the majority of the spines are highly dynamic, displaying lifetimes considerably shorter than that of spines on pyramidal neurons. Using correlative light and electron microscopy, we confirmed that these VIP spines are sites of excitatory synaptic contacts, and are morphologically distinct from other spines in L1.

Beyond dimension reduction: Stable electric fields emerge from and allow representational drift

It is known that the exact neurons maintaining a given memory (the neural ensemble) change from trial to trial. This raises the question of how the brain achieves stability in the face of this representational drift. Here, we demonstrate that this stability emerges at the level of the electric fields that arise from neural activity. We show that electric fields carry information about working memory content. The electric fields, in turn, can act as "guard rails" that funnel higher dimensional variable neural activity along stable lower dimensional routes. We obtained the latent space associated with each memory. We then confirmed the stability of the electric field by mapping the latent space to different cortical patches (that comprise a neural ensemble) and reconstructing information flow between patches. Stable electric fields can allow latent states to be transferred between brain areas, in accord with modern engram theory.

Complementary encoding of spatial information in hippocampal astrocytes

Calcium dynamics into astrocytes influence the activity of nearby neuronal structures. However, because previous reports show that astrocytic calcium signals largely mirror neighboring neuronal activity, current information coding models neglect astrocytes. Using simultaneous two-photon calcium imaging of astrocytes and neurons in the hippocampus of mice navigating a virtual environment, we demonstrate that astrocytic calcium signals encode (i.e., statistically reflect) spatial information that could not be explained by visual cue information. Calcium events carrying spatial information occurred in topographically organized astrocytic subregions. Importantly, astrocytes encoded spatial information that was complementary and synergistic to that carried by neurons, improving spatial position decoding when astrocytic signals were considered alongside neuronal ones. These results suggest that the complementary place dependence of localized astrocytic calcium signals may regulate clusters of nearby synapses, enabling dynamic, context-dependent variations in population coding within brain circuits.

Imaging neural circuit pathology of autism spectrum disorders: autism-associated genes, animal models and the application of in vivo two-photon imaging

Recent advances in human genetics identified genetic variants involved in causing autism spectrum disorders (ASDs). Mouse models that mimic mutations found in patients with ASD exhibit behavioral phenotypes consistent with ASD symptoms. These mouse models suggest critical biological factors of ASD etiology. Another important implication of ASD genetics is the enrichment of ASD risk genes in molecules involved in developing synapses and regulating neural circuit function. Sophisticated in vivo imaging technologies applied to ASD mouse models identify common synaptic impairments in the neocortex, with genetic-mutation-specific defects in local neural circuits. In this article, we review synapse- and circuit-level phenotypes identified by in vivo two-photon imaging in multiple mouse models of ASD and discuss the contributions of altered synapse properties and neural circuit activity to ASD pathogenesis.

Engineered synaptic tools reveal localized cAMP signaling in synapse assembly

The physiological mechanisms driving synapse formation are elusive. Although numerous signals are known to regulate synapses, it remains unclear which signaling mechanisms organize initial synapse assembly. Here, we describe new tools, referred to as "SynTAMs" for synaptic targeting molecules, that enable localized perturbations of cAMP signaling in developing postsynaptic specializations. We show that locally restricted suppression of postsynaptic cAMP levels or of cAMP-dependent protein-kinase activity severely impairs excitatory synapse formation without affecting neuronal maturation, dendritic arborization, or inhibitory synapse formation. In vivo, suppression of postsynaptic cAMP signaling in CA1 neurons prevented formation of both Schaffer-collateral and entorhinal-CA1/temporoammonic-path synapses, suggesting a general principle. Retrograde trans-synaptic rabies virus tracing revealed that postsynaptic cAMP signaling is required for continuous replacement of synapses throughout life. Given that postsynaptic latrophilin adhesion-GPCRs drive synapse formation and produce cAMP, we suggest that spatially restricted postsynaptic cAMP signals organize assembly of postsynaptic specializations during synapse formation.

Regional synapse gain and loss accompany memory formation in larval zebrafish

Defining the structural and functional changes in the nervous system underlying learning and memory represents a major challenge for modern neuroscience. Although changes in neuronal activity following memory formation have been studied [B. F. Grewe et al., Nature 543, 670-675 (2017); M. T. Rogan, U. V. Stäubli, J. E. LeDoux, Nature 390, 604-607 (1997)], the underlying structural changes at the synapse level remain poorly understood. Here, we capture synaptic changes in the midlarval zebrafish brain that occur during associative memory formation by imaging excitatory synapses labeled with recombinant probes using selective plane illumination microscopy. Imaging the same subjects before and after classical conditioning at single-synapse resolution provides an unbiased mapping of synaptic changes accompanying memory formation. In control animals and animals that failed to learn the task, there were no significant changes in the spatial patterns of synapses in the pallium, which contains the equivalent of the mammalian amygdala and is essential for associative learning in teleost fish [M. Portavella, J. P. Vargas, B. Torres, C. Salas, Brain Res. Bull 57, 397-399 (2002)]. In zebrafish that formed memories, we saw a dramatic increase in the number of synapses in the ventrolateral pallium, which contains neurons active during memory formation and retrieval. Concurrently, synapse loss predominated in the dorsomedial pallium. Surprisingly, we did not observe significant changes in the intensity of synaptic labeling, a proxy for synaptic strength, with memory formation in any region of the pallium. Our results suggest that memory formation due to classical conditioning is associated with reciprocal changes in synapse numbers in the pallium.

Imaging deeper than the transport mean free path with multiphoton microscopy

Multiphoton fluorescence microscopy enables deep in vivo imaging by using long excitation wavelengths to increase the penetration depth of ballistic photons and nonlinear excitation to suppress the out-of-focus fluorescence. However, the imaging depth of multiphoton microscopy is limited by tissue scattering and absorption. This fundamental depth limit for two-photon microscopy has been studied theoretically and experimentally. Long wavelength three-photon fluorescence microscopy was developed to image beyond the depth limit of two-photon microscopy and has achieved unprecedented in vivo imaging depth. Here we extend the theoretical framework for characterizing the depth limit of two-photon microscopy to three-photon microscopy. We further verify the theoretical predictions with experimental results from tissue phantoms. We demonstrate experimentally that high spatial resolution diffraction-limited imaging at a depth of 10 scattering mean free paths, which is nearly twice the transport mean free path, is possible with multiphoton microscopy. Our results indicate that the depth limit of three-photon microscopy is significantly beyond what has been achieved in biological tissues so far, and further technological development is required to reach the full potential of three-photon microscopy.

Neurophotonic tools for microscopic measurements and manipulation: status report

Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.

Long-term effect of neonatal antagonism of ionotropic glutamate receptors on dendritic spines and cognitive function in rats

Glutamate is the most abundant excitatory neurotransmitter in the hippocampus where mediates its actions by activating glutamate receptors. The activation of these receptors is essential for the maintenance and dynamics of dendritic spines and plasticity that correlate with learning and memory processes during neurodevelopment and adulthood. We studied in adults the effect of blocking ionotropic glutamate receptors (NMDAR, AMPAR, and KAR) functions at neonatal age (PD1-PD15) with their respective antagonists D-AP5, GYKI-53655 and UBP-302. We first evaluated memory using a new object recognition test in adults. Second, we evaluated the levels of glial fibrillary acidic protein, synaptophysin and actin with immunohistochemistry in the CA1, CA3, and dentate gyrus regions of the hippocampus and, finally, the number of dendritic spines and their dynamics using Golgi-Cox staining. We found that ionotropic glutamate receptor function blockade at neonatal age causes a reduction in short and long-term memory in adulthood and a reduction in the expression of synaptophysin and actin protein levels in the hippocampus regions studied. This blockade also reduced the number of dendritic spines and modified dendritic dynamics in the CA1 region. The antagonism of the three types of ionotropic glutamate receptors reduced the mushrooms and bifurcated types of spines and increased the thin spines. The number of stubby spines was reduced by D-AP5, increased by UPB-302, and not affected by GYKI-53655. Our results indicate that the blockade of neonatal ionotropic glutamate receptors produces alterations that persist until adulthood.

The Hippocampal Horizon: Constructing and Segmenting Experience for Episodic Memory

How do we recollect specific events that have occurred during continuous ongoing experience? There is converging evidence from non-human animals that spatially modulated cellular activity of the hippocampal formation supports the construction of ongoing events. On the other hand, recent human oriented event cognition models have outlined that our experience is segmented into discrete units, and that such segmentation can operate on shorter or longer timescales. Here, we describe a unification of how these dynamic physiological mechanisms of the hippocampus relate to ongoing externally and internally driven event segmentation, facilitating the demarcation of specific moments during experience. Our cross-species interdisciplinary approach offers a novel perspective in the way we construct and remember specific events, leading to the generation of many new hypotheses for future research.

Silent Synapses in Cocaine-Associated Memory and Beyond

Glutamatergic synapses are key cellular sites where cocaine experience creates memory traces that subsequently promote cocaine craving and seeking. In addition to making across-the-board synaptic adaptations, cocaine experience also generates a discrete population of new synapses that selectively encode cocaine memories. These new synapses are glutamatergic synapses that lack functionally stable AMPARs, often referred to as AMPAR-silent synapses or, simply, silent synapses. They are generated de novo in the NAc by cocaine experience. After drug withdrawal, some of these synapses mature by recruiting AMPARs, contributing to the consolidation of cocaine-associated memory. After cue-induced retrieval of cocaine memories, matured silent synapses alternate between two dynamic states (AMPAR-absent vs AMPAR-containing) that correspond with the behavioral manifestations of destabilization and reconsolidation of these memories. Here, we review the molecular mechanisms underlying silent synapse dynamics during behavior, discuss their contributions to circuit remodeling, and analyze their role in cocaine-memory-driven behaviors. We also propose several mechanisms through which silent synapses can form neuronal ensembles as well as cross-region circuit engrams for cocaine-specific behaviors. These perspectives lead to our hypothesis that cocaine-generated silent synapses stand as a distinct set of synaptic substrates encoding key aspects of cocaine memory that drive cocaine relapse.

Super-resolution microscopy: a closer look at synaptic dysfunction in Alzheimer disease

The synapse has emerged as a critical neuronal structure in the degenerative process of Alzheimer disease (AD), in which the pathogenic signals of two key players - amyloid-β (Aβ) and tau - converge, thereby causing synaptic dysfunction and cognitive deficits. The synapse presents a dynamic, confined microenvironment in which to explore how key molecules travel, localize, interact and assume different levels of organizational complexity, thereby affecting neuronal function. However, owing to their small size and the diffraction-limited resolution of conventional light microscopic approaches, investigating synaptic structure and dynamics has been challenging. Super-resolution microscopy (SRM) techniques have overcome the resolution barrier and are revolutionizing our quantitative understanding of biological systems in unprecedented spatio-temporal detail. Here we review critical new insights provided by SRM into the molecular architecture and dynamic organization of the synapse and, in particular, the interactions between Aβ and tau in this compartment. We further highlight how SRM can transform our understanding of the molecular pathological mechanisms that underlie AD. The application of SRM for understanding the roles of synapses in AD pathology will provide a stepping stone towards a broader understanding of dysfunction in other subcellular compartments and at cellular and circuit levels in this disease.

Consciousness as a Product of Evolution: Contents, Selector Circuits, and Trajectories in Experience Space

Conscious experience can be treated as a complex unified whole, but to do so is problematic from an evolutionary perspective if, like other products of evolution, consciousness had simple beginnings, and achieved complexity only secondarily over an extended period of time as new categories of subjective experience were added and refined. The premise here is twofold, first that these simple beginnings can be investigated regardless of whether the ultimate source of subjective experience is known or understood, and second, that of the contents known to us, the most accessible for investigation will be those that are, or appear, most fundamental, in the sense that they resist further deconstruction or analysis. This would include qualia as they are usually defined, but excludes more complex experiences (here, formats) that are structured, or depend on algorithmic processes and/or memory. Vision and language for example, would by this definition be formats. More formally, qualia, but not formats, can be represented as points, lines, or curves on a topological experience space, and as domains in a configuration space representing a subset of neural correlates of consciousness, the selector circuits (SCs), responsible for ensuring that a particular experience is evoked rather than some other. It is a matter of conjecture how points in SC-space map to experience space, but both will exhibit divergence, insuring that a minimal distance separates points in experience space representing different qualia and the SCs that evoke them. An analysis of how SCs evolve over time is used to highlight the importance of understanding patterns of descent among putative qualia, i.e., their homology across species, and whether this implies descent from an ancestral experience, or ur-quale, that combines modes of experience that later came to be experienced separately. The analysis also provides insight into the function of consciousness as viewed from an evolutionary perspective, defined here in terms of the access it allows to regions of SC-space that would otherwise be unavailable to real brains, to produce consciously controlled behaviors that could otherwise not occur.

Optimal plasticity for memory maintenance during ongoing synaptic change

Synaptic connections in many brain circuits fluctuate, exhibiting substantial turnover and remodelling over hours to days. Surprisingly, experiments show that most of this flux in connectivity persists in the absence of learning or known plasticity signals. How can neural circuits retain learned information despite a large proportion of ongoing and potentially disruptive synaptic changes? We address this question from first principles by analysing how much compensatory plasticity would be required to optimally counteract ongoing fluctuations, regardless of whether fluctuations are random or systematic. Remarkably, we find that the answer is largely independent of plasticity mechanisms and circuit architectures: compensatory plasticity should be at most equal in magnitude to fluctuations, and often less, in direct agreement with previously unexplained experimental observations. Moreover, our analysis shows that a high proportion of learning-independent synaptic change is consistent with plasticity mechanisms that accurately compute error gradients.

Neurogenesis-dependent transformation of hippocampal engrams

In humans and other mammals, memories of events are encoded by neuronal ensembles (or engrams) in the hippocampus. The mnemonic information stored in these engrams can then be used to guide future behavior, including prediction- and decision-making in dynamic environments. While some hippocampal engrams may be persistently stored, others are modified over time, suggesting that the represented memories may also be transformed. How might hippocampal engrams be modified through time? Adult hippocampal neurogenesis represents one process that continuously rewires hippocampal circuitry, presumably including stored hippocampal engrams. At intermediate stages, we propose that neurogenesis-mediated rewiring of hippocampal engram circuitry induces forgetting of specific stimulus attributes, and this less precise engram allows for generalization. At more advanced stages, we propose that neurogenesis-mediated rewiring of hippocampal engram circuitry leads to silencing of hippocampal engrams, rendering them no longer accessible by natural retrieval cues.

Clear optically matched panoramic access channel technique (COMPACT) for large-volume deep brain imaging

To understand neural circuit mechanisms underlying behavior, it is crucial to observe the dynamics of neuronal structure and function in different regions of the brain. Since current noninvasive imaging technologies allow cellular-resolution imaging of neurons only within ~1 mm below the cortical surface, the majority of mouse brain tissue remains inaccessible. While miniature optical imaging probes allow access to deep brain regions, cellular-resolution imaging is typically restricted to a small tissue volume. To increase the tissue access volume, we developed a clear optically matched panoramic access channel technique (COMPACT). With probe dimensions comparable to those of common gradient-index lenses, COMPACT enables a two to three orders of magnitude greater tissue access volume. We demonstrated the capabilities of COMPACT by multiregional calcium imaging in mice during sleep. We believe that large-volume in vivo imaging with COMPACT will be valuable to a variety of deep tissue imaging applications.

The cell biology of synapse formation

In a neural circuit, synapses transfer information rapidly between neurons and transform this information during transfer. The diverse computational properties of synapses are shaped by the interactions between pre- and postsynaptic neurons. How synapses are assembled to form a neural circuit, and how the specificity of synaptic connections is achieved, is largely unknown. Here, I posit that synaptic adhesion molecules (SAMs) organize synapse formation. Diverse SAMs collaborate to achieve the astounding specificity and plasticity of synapses, with each SAM contributing different facets. In orchestrating synapse assembly, SAMs likely act as signal transduction devices. Although many candidate SAMs are known, only a few SAMs appear to have a major impact on synapse formation. Thus, a limited set of collaborating SAMs likely suffices to account for synapse formation. Strikingly, several SAMs are genetically linked to neuropsychiatric disorders, suggesting that impairments in synapse assembly are instrumental in the pathogenesis of neuropsychiatric disorders.

Ucp2-dependent microglia-neuronal coupling controls ventral hippocampal circuit function and anxiety-like behavior

Microglia have been implicated in synapse remodeling by phagocytosis of synaptic elements in the adult brain, but the mechanisms involved in the regulation of this process are ill-defined. By examining microglia-neuronal interaction in the ventral hippocampus, we found a significant reduction in spine synapse number during the light phase of the light/dark cycle accompanied by increased microglia-synapse contacts and an elevated amount of microglial phagocytic inclusions. This was followed by a transient rise in microglial production of reactive oxygen species (ROS) and a concurrent increase in expression of uncoupling protein 2 (Ucp2), a regulator of mitochondrial ROS generation. Conditional ablation of Ucp2 from microglia hindered phasic elimination of spine synapses with consequent accumulations of ROS and lysosome-lipid droplet complexes, which resulted in hippocampal neuronal circuit dysfunctions assessed by electrophysiology, and altered anxiety-like behavior. These observations unmasked a novel and chronotypical interaction between microglia and neurons involved in the control of brain functions.

Aberration correction in stimulated emission depletion microscopy to increase imaging depth in living brain tissue

Significance: Stimulated emission depletion (STED) microscopy enables nanoscale imaging of live samples, but it requires a specific spatial beam shaping that is highly sensitive to optical aberrations, limiting its depth penetration. Therefore, there is a need for methods to reduce optical aberrations and improve the spatial resolution of STED microscopy inside thick biological tissue. Aim: The aim of our work was to develop and validate a method based on adaptive optics to achieve an a priori correction of spherical aberrations as a function of imaging depth. Approach: We first measured the aberrations in a phantom sample of gold and fluorescent nanoparticles suspended in an agarose gel with a refractive index closely matching living brain tissue. We then used a spatial light modulator to apply corrective phase shifts and validate this calibration approach by imaging neurons in living brain slices. Results: After quantifying the spatial resolution in depth in phantom samples, we demonstrated that the corrections can substantially increase image quality in living brain slices. Specifically, we could measure structures as small as 80 nm at a depth of 90    μ m inside the biological tissue and obtain a 60% signal increase after correction. Conclusion: We propose a simple and robust approach to calibrate and compensate the distortions of the STED beam profile introduced by spherical aberrations with increasing imaging depth and demonstrated that this method offers significant improvements in microscopy performance for nanoscale cellular imaging in live tissue.

Dendrite enlightenment

Neuronal dendrites acquire complex morphologies during development. These are not just the product of cell-intrinsic developmental programs; rather they are defined in close interaction with the cellular environment. Thus, to understand the molecular cascades that yield appropriate morphologies, it is essential to investigate them in vivo, in the actual complex tissue environment encountered by the differentiating neuron in the developing animal. Particularly, genetic approaches have pointed to factors controlling dendrite differentiation in vivo. These suggest that localized and transient molecular cascades might underlie the formation and stabilization of dendrite branches with neuron type-specific characteristics. Here, I highlight the need for studies of neuronal dendrite differentiation in the animal, the challenges provided by such an approach, and the promising pathways that have recently opened.

Super-resolution STED microscopy in live brain tissue

STED microscopy is one of several fluorescence microscopy techniques that permit imaging at higher spatial resolution than what the diffraction-limit of light dictates. STED imaging is unique among these super-resolution modalities in being a beam-scanning microscopy technique based on confocal or 2-photon imaging, which provides the advantage of superior optical sectioning in thick samples. Compared to the other super-resolution techniques that are based on widefield microscopy, this makes STED particularly suited for imaging inside live brain tissue, such as in slices or in vivo. Notably, the 50 nm resolution provided by STED microscopy enables analysis of neural morphologies that conventional confocal and 2-photon microscopy approaches cannot resolve, including all-important synaptic structures. Over the course of the last 20 years, STED microscopy has undergone extensive developments towards ever more versatile use, and has facilitated remarkable neurophysiological discoveries. The technique is still not widely adopted for live tissue imaging, even though one of its particular strengths is exactly in resolving the nanoscale dynamics of synaptic structures in brain tissue, as well as in addressing the complex morphologies of glial cells, and revealing the intricate structure of the brain extracellular space. Not least, live tissue STED microscopy has so far hardly been applied in settings of pathophysiology, though also here it shows great promise for providing new insights. This review outlines the technical advantages of STED microscopy for imaging in live brain tissue, and highlights key neurobiological findings brought about by the technique.

A computational grid-to-place-cell transformation model indicates a synaptic driver of place cell impairment in early-stage Alzheimer's Disease

Alzheimer's Disease (AD) is characterized by progressive neurodegeneration and cognitive impairment. Synaptic dysfunction is an established early symptom, which correlates strongly with cognitive decline, and is hypothesised to mediate the diverse neuronal network abnormalities observed in AD. However, how synaptic dysfunction contributes to network pathology and cognitive impairment in AD remains elusive. Here, we present a grid-cell-to-place-cell transformation model of long-term CA1 place cell dynamics to interrogate the effect of synaptic loss on network function and environmental representation. Synapse loss modelled after experimental observations in the APP/PS1 mouse model was found to induce firing rate alterations and place cell abnormalities that have previously been observed in AD mouse models, including enlarged place fields and lower across-session stability of place fields. Our results support the hypothesis that synaptic dysfunction underlies cognitive deficits, and demonstrate how impaired environmental representation may arise in the early stages of AD. We further propose that dysfunction of excitatory and inhibitory inputs to CA1 pyramidal cells may cause distinct impairments in place cell function, namely reduced stability and place map resolution.

Spine dynamics in the brain, mental disorders and artificial neural networks

In the brain, most synapses are formed on minute protrusions known as dendritic spines. Unlike their artificial intelligence counterparts, spines are not merely tuneable memory elements: they also embody algorithms that implement the brain's ability to learn from experience and cope with new challenges. Importantly, they exhibit structural dynamics that depend on activity, excitatory input and inhibitory input (synaptic plasticity or 'extrinsic' dynamics) and dynamics independent of activity ('intrinsic' dynamics), both of which are subject to neuromodulatory influences and reinforcers such as dopamine. Here we succinctly review extrinsic and intrinsic dynamics, compare these with parallels in machine learning where they exist, describe the importance of intrinsic dynamics for memory management and adaptation, and speculate on how disruption of extrinsic and intrinsic dynamics may give rise to mental disorders. Throughout, we also highlight algorithmic features of spine dynamics that may be relevant to future artificial intelligence developments.

The importance of ultrastructural analysis of memory

Plasticity of glutamatergic synapses in the hippocampus is believed to underlie learning and memory processes. Surprisingly, very few studies report long-lasting structural changes of synapses induced by behavioral training. It remains, therefore, unclear which synaptic changes in the hippocampus contribute to memory storage. Here, we systematically compare how long-term potentiation of synaptic transmission (LTP) (a primary form of synaptic plasticity and cellular model of memory) and behavioral training affect hippocampal glutamatergic synapses at the ultrastructural level enabled by electron microscopy. The review of the literature indicates that while LTP induces growth of dendritic spines and post-synaptic densities (PSD), that represent postsynaptic part of a glutamatergic synapse, after behavioral training there is transient (< 6 h) synaptogenesis and long-lasting (> 24 h) increase in PSD volume (without a significant change of dendritic spine volume), indicating that training-induced PSD growth may reflect long-term enhancement of synaptic functions. Additionally, formation of multi-innervated spines (MIS), is associated with long-term memory in aged mice and LTP-deficient mutant mice. Since volume of PSD, as well as atypical synapses, can be reliably observed only with electron microscopy, we argue that the ultrastructural level of analysis is required to reveal synaptic changes that are associated with long-term storage of information in the brain.

Through the looking glass: A review of cranial window technology for optical access to the brain

Deciphering neurologic function is a daunting task, requiring understanding the neuronal networks and emergent properties that arise from the interactions among single neurons. Mechanistic insights into neuronal networks require tools that simultaneously assess both single neuron activity and the consequent mesoscale output. The development of cranial window technologies, in which the skull is thinned or replaced with a synthetic optical interface, has enabled monitoring neuronal activity from subcellular to mesoscale resolution in awake, behaving animals when coupled with advanced microscopy techniques. Here we review recent achievements in cranial window technologies, appraise the relative merits of each design and discuss the future research in cranial window design.

Quantification of Dendritic Spines Remodeling under Physiological Stimuli and in Pathological Conditions

Numerous brain diseases are associated with abnormalities in morphology and density of dendritic spines, small membranous protrusions whose structural geometry correlates with the strength of synaptic connections. Thus, the quantitative analysis of dendritic spines remodeling in microscopic images is one of the key elements towards understanding mechanisms of structural neuronal plasticity and bases of brain pathology. In the following article, we review experimental approaches designed to assess quantitative features of dendritic spines under physiological stimuli and in pathological conditions. We compare various methodological pipelines of biological models, sample preparation, data analysis, image acquisition, sample size, and statistical analysis. The methodology and results of relevant experiments are systematically summarized in a tabular form. In particular, we focus on quantitative data regarding the number of animals, cells, dendritic spines, types of studied parameters, size of observed changes, and their statistical significance.

Anesthetics fragment hippocampal network activity, alter spine dynamics, and affect memory consolidation

General anesthesia is characterized by reversible loss of consciousness accompanied by transient amnesia. Yet, long-term memory impairment is an undesirable side effect. How different types of general anesthetics (GAs) affect the hippocampus, a brain region central to memory formation and consolidation, is poorly understood. Using extracellular recordings, chronic 2-photon imaging, and behavioral analysis, we monitor the effects of isoflurane (Iso), medetomidine/midazolam/fentanyl (MMF), and ketamine/xylazine (Keta/Xyl) on network activity and structural spine dynamics in the hippocampal CA1 area of adult mice. GAs robustly reduced spiking activity, decorrelated cellular ensembles, albeit with distinct activity signatures, and altered spine dynamics. CA1 network activity under all 3 anesthetics was different to natural sleep. Iso anesthesia most closely resembled unperturbed activity during wakefulness and sleep, and network alterations recovered more readily than with Keta/Xyl and MMF. Correspondingly, memory consolidation was impaired after exposure to Keta/Xyl and MMF, but not Iso. Thus, different anesthetics distinctly alter hippocampal network dynamics, synaptic connectivity, and memory consolidation, with implications for GA strategy appraisal in animal research and clinical settings.

Nanoscale 3D EM reconstructions reveal intrinsic mechanisms of structural diversity of chemical synapses

Chemical synapses of shared cellular origins have remarkably heterogeneous structures, but how this diversity is generated is unclear. Here, we use three-dimensional (3D) electron microscopy and artificial intelligence algorithms for image processing to reconstruct functional excitatory microcircuits in the mouse hippocampus and microcircuits in which neurotransmitter signaling is permanently suppressed with genetic tools throughout the lifespan. These nanoscale analyses reveal that experience is dispensable for morphogenesis of synapses with different geometric shapes and contents of membrane organelles and that arrangement of morphologically distinct connections in local networks is stochastic. Moreover, loss of activity increases the variability in sizes of opposed pre- and postsynaptic structures without disrupting their alignments, suggesting that inherently variable weights of naive connections become progressively matched with repetitive use. These results demonstrate that mechanisms for the structural diversity of neuronal synapses are intrinsic and provide insights into how circuits essential for memory storage assemble and integrate information.

Engram cell connectivity: an evolving substrate for information storage

Understanding memory requires an explanation for how information can be stored in the brain in a stable state. The change in the brain that accounts for a given memory is referred to as an engram. In recent years, the term engram has been operationalized as the cells that are activated by a learning experience, undergoes plasticity, and are sufficient and necessary for memory recall. Using this framework, and a growing toolbox of related experimental techniques, engram manipulation has become a central topic in behavioral, systems, and molecular neuroscience. Recent research on the topic has provided novel insights into the mechanisms of long-term memory storage, and its overlap with instinct. We propose that memory and instinct may be embodied as isomorphic topological structures within the brain's microanatomical circuitry.

A Synaptic Framework for the Persistence of Memory Engrams

The ability to store and retrieve learned information over prolonged periods of time is an essential and intriguing property of the brain. Insight into the neurobiological mechanisms that underlie memory consolidation is of utmost importance for our understanding of memory persistence and how this is affected in memory disorders. Recent evidence indicates that a given memory is encoded by sparsely distributed neurons that become highly activated during learning, so-called engram cells. Research by us and others confirms the persistent nature of cortical engram cells by showing that these neurons are required for memory expression up to at least 1 month after they were activated during learning. Strengthened synaptic connectivity between engram cells is thought to ensure reactivation of the engram cell network during retrieval. However, given the continuous integration of new information into existing neuronal circuits and the relatively rapid turnover rate of synaptic proteins, it is unclear whether a lasting learning-induced increase in synaptic connectivity is mediated by stable synapses or by continuous dynamic turnover of synapses of the engram cell network. Here, we first discuss evidence for the persistence of engram cells and memory-relevant adaptations in synaptic plasticity, and then propose models of synaptic adaptations and molecular mechanisms that may support memory persistence through the maintenance of enhanced synaptic connectivity within an engram cell network.

Recent advances in computational methods for measurement of dendritic spines imaged by light microscopy

Dendritic spines are small protrusions that receive most of the excitatory inputs to the pyramidal neurons in the neocortex and the hippocampus. Excitatory neural circuits in the neocortex and hippocampus are important for experience-dependent changes in brain functions, including postnatal sensory refinement and memory formation. Several lines of evidence indicate that synaptic efficacy is correlated with spine size and structure. Hence, precise and accurate measurement of spine morphology is important for evaluation of neural circuit function and plasticity. Recent advances in light microscopy and image analysis techniques have opened the way toward a full description of spine nanostructure. In addition, large datasets of spine nanostructure can be effectively analyzed using machine learning techniques and other mathematical approaches, and recent advances in super-resolution imaging allow researchers to analyze spine structure at an unprecedented level of precision. This review summarizes computational methods that can effectively identify, segment and quantitate dendritic spines in either 2D or 3D imaging. Nanoscale analysis of spine structure and dynamics, combined with new mathematical approaches, will facilitate our understanding of spine functions in physiological and pathological conditions.

The why and how of sleep-dependent synaptic down-selection

Sleep requires that we disconnect from the environment, losing the ability to promptly respond to stimuli. There must be at least one essential function that justifies why we take this risk every day, and that function must depend on the brain being offline. We have proposed that this function is to renormalize synaptic weights after learning has led to a net increase in synaptic strength in many brain circuits. Without this renormalization, synaptic activity would become energetically too expensive and saturation would prevent new learning. There is converging evidence from molecular, electrophysiological, and ultrastructural experiments showing a net increase in synaptic strength after the major wake phase, and a net decline after sleep. The evidence also suggests that sleep-dependent renormalization is a smart process of synaptic down-selection, comprehensive and yet specific, which could explain the many beneficial effects of sleep on cognition. Recently, a key molecular mechanism that allows broad synaptic weakening during sleep was identified. Other mechanisms still being investigated should eventually explain how sleep can weaken most synapses but afford protection to some, including those directly activated by learning. That synaptic down-selection takes place during sleep is by now established; why it should take place during sleep has a plausible explanation; how it happens is still work in progress.

Hippocampal hyperactivity in a rat model of Alzheimer's disease

Neuronal network dysfunction is a hallmark of Alzheimer's disease (AD). However, the underlying pathomechanisms remain unknown. We analyzed the hippocampal micronetwork in transgenic McGill-R-Thy1-APP rats (APPtg) at the beginning of extracellular amyloid beta (Aβ) deposition. We established two-photon Ca²⁺ -imaging in vivo in the hippocampus of rats and found hyperactivity of CA1 neurons. Patch-clamp recordings in brain slices in vitro revealed increased neuronal input resistance and prolonged action potential width in CA1 pyramidal neurons. We did neither observe changes in synaptic inhibition, nor in excitation. Our data support the view that increased intrinsic excitability of CA1 neurons may precede inhibitory dysfunction at an early stage of Aβ-deposition and disease progression.

Strategies and Tools for Studying Microglial-Mediated Synapse Elimination and Refinement

The role of microglia in controlling synapse homeostasis is becoming increasingly recognized by the scientific community. In particular, the microglia-mediated elimination of supernumerary synapses during development lays the basis for the correct formation of neuronal circuits in adulthood, while the possible reactivation of this process in pathological conditions, such as schizophrenia or Alzheimer's Disease, provides a promising target for future therapeutic strategies. The methodological approaches to investigate microglial synaptic engulfment include different in vitro and in vivo settings. Basic in vitro assays, employing isolated microglia and microbeads, apoptotic membranes, liposomes or synaptosomes allow the quantification of the microglia phagocytic abilities, while co-cultures of microglia and neurons, deriving from either WT or genetically modified mice models, provide a relatively manageable setting to investigate the involvement of specific molecular pathways. Further detailed analysis in mice brain is then mandatory to validate the in vitro assays as representative for the in vivo situation. The present review aims to dissect the main technical approaches to investigate microglia-mediated phagocytosis of neuronal and synaptic substrates in critical developmental time windows.