Axons and synaptic boutons are highly dynamic in adult visual cortex

A dendritic mechanism for balancing synaptic flexibility and stability

Biological and artificial neural networks learn by modifying synaptic weights, but it is unclear how these systems retain previous knowledge and also acquire new information. Here, we show that cortical pyramidal neurons can solve this plasticity-versus-stability dilemma by differentially regulating synaptic plasticity at distinct dendritic compartments. Oblique dendrites of adult mouse layer 5 cortical pyramidal neurons selectively receive monosynaptic thalamic input, integrate linearly, and lack burst-timing synaptic potentiation. In contrast, basal dendrites, which do not receive thalamic input, exhibit conventional NMDA receptor (NMDAR)-mediated supralinear integration and synaptic potentiation. Congruently, spiny synapses on oblique branches show decreased structural plasticity in vivo. The selective decline in NMDAR activity and expression at synapses on oblique dendrites is controlled by a critical period of visual experience. Our results demonstrate a biological mechanism for how single neurons can safeguard a set of inputs from ongoing plasticity by altering synaptic properties at distinct dendritic domains.

Synaptic promiscuity in brain development

Precise synaptic connectivity is a prerequisite for the function of neural circuits, yet individual neurons, taken out of their developmental context, readily form unspecific synapses. How does the genome encode brain wiring in light of this apparent contradiction? Synaptic specificity is the outcome of a long series of developmental processes and mechanisms before, during and after synapse formation. How much promiscuity is permissible or necessary at the moment of synaptic partner choice depends on the extent to which prior development restricts available partners or subsequent development corrects initially made synapses. Synaptic promiscuity at the moment of choice can thereby play important roles in the development of precise connectivity, but also facilitate developmental flexibility and robustness. In this review, we assess the experimental evidence for the prevalence and roles of promiscuous synapse formation during brain development. Many well-established experimental approaches are based on developmental genetic perturbation and an assessment of synaptic connectivity only in the adult; this can make it difficult to pinpoint when a given defect or mechanism occurred. In many cases, such studies reveal mechanisms that restrict partner availability already prior to synapse formation. Subsequently, at the moment of choice, factors including synaptic competency, interaction dynamics and molecular recognition further restrict synaptic partners. The discussion of the development of synaptic specificity through the lens of synaptic promiscuity suggests an algorithmic process based on neurons capable of promiscuous synapse formation that are continuously prevented from making the wrong choices, with no single mechanism or developmental time point sufficient to explain the outcome.

Single-neuron analysis of axon arbors reveals distinct presynaptic organizations between feedforward and feedback projections

The morphology and spatial distribution of axon arbors and boutons are crucial for neuron presynaptic functions. However, the principles governing their whole-brain organization at the single-neuron level remain unclear. We developed a machine-learning method to separate axon arbors from passing axons in single-neuron reconstruction from fluorescence micro-optical sectioning tomography imaging data and obtained 62,374 axon arbors that displayed distinct morphology, spatial patterns, and scaling laws dependent on neuron types and targeted brain areas. Focusing on the axon arbors in the thalamus and cortex, we revealed the segregated spatial distributions and distinct morphology but shared topographic gradients between feedforward and feedback projections. Furthermore, we uncovered an association between arbor complexity and microglia density. Finally, we found that the boutons on terminal arbors show branch-specific clustering with a log-normal distribution that again differed between feedforward and feedback terminal arbors. Together, our study revealed distinct presynaptic structural organizations underlying diverse functional innervation of single projection neurons.

Rapid eye movement sleep contributes to the formation of new axonal varicosities in mouse cerebellar parallel fibers after motor training

Synaptic structural plasticity is essential for the development, learning and memory. It is well established that sleep plays important roles in synaptic plasticity after motor learning. In cerebellar cortex, parallel fibers of granule cells make excitatory synapses to the dendrites of Purkinje cells. However, the synaptic structural dynamics between parallel and Purkinje cells after motor training and the function of sleep in cerebellar synaptic plasticity remain unclear. Here, we used two-photon microscopy to examine presynaptic axonal structural dynamics at parallel fiber-Purkinje cell synapses and investigated the effect of REM sleep in synaptic plasticity of mouse cerebellar cortex following motor training. We found that motor training induces higher formation of new axonal varicosities in cerebellar parallel fibers. Our results also indicate that calcium activities of granule cells significantly increase during REM sleep, and REM sleep deprivation prevents motor training-induced formation of axonal varicosities in parallel fibers, suggesting that higher calcium activity of granule cells was crucial for promoting newly formed axonal varicosities after motor training. Together, these findings reveal the effect of motor training on parallel fiber presynaptic structural modification and the important role of REM sleep in synaptic plasticity in cerebellar cortex.

A 3D atlas of functional human brain energetic connectome based on neuropil distribution

The human brain is energetically expensive, yet the key factors governing its heterogeneous energy distributions across cortical regions to support its diversity of functions remain unexplored. Here, we built up a 3D digital cortical energy atlas based on the energetic costs of all neuropil activities into a high-resolution stereological map of the human cortex with cellular and synaptic densities derived, respectively, from ex vivo histological staining and in vivo PET imaging. The atlas was validated with PET-measured glucose oxidation at the voxel level. A 3D cortical activity map was calculated to predict the heterogeneous activity rates across all cortical regions, which revealed that resting brain is indeed active with heterogeneous neuronal activity rates averaging around 1.2 Hz, comprising around 70% of the glucose oxidation of the cortex. Additionally, synaptic density dominates spatial patterns of energetics, suggesting that the cortical energetics rely heavily on the distribution of synaptic connections. Recent evidence from functional imaging studies suggests that some cortical areas act as hubs (i.e., interconnecting distinct and functionally active regions). An inverse allometric relationship was observed between hub metabolic rates versus hub volumes. Hubs with smaller volumes have higher synapse density, metabolic rate, and activity rates compared to nonhubs. The open-source BrainEnergyAtlas provides a granular framework for exploring revealing design principles in energy-constrained human cortical circuits across multiple spatial scales.

A model for generating differences in microtubules between axonal branches depending on the distance from terminals

In the remodeling of axonal arbor, the growth and retraction of branches are differentially regulated within a single axon. Although cell-autonomously generated differences in microtubule (MT) turnover are thought to be involved in selective branch regulation, the cellular system whereby neurons generate differences of MTs between axonal branches has not been clarified. Because MT turnover tends to be slower in longer branches compared with neighboring shorter branches, feedback regulation depending on branch length is thought to be involved. In the present study, we generated a model of MT lifetime in axonal terminal branches by adapting a length-dependent model in which parameters for MT dynamics were constant in the arbor. The model predicted that differences in MT lifetime between neighboring branches could be generated depending on the distance from terminals. In addition, the following points were predicted. Firstly, destabilization of MTs throughout the arbor decreased the differences in MT lifetime between branches. Secondly, differences of MT lifetime existed even before MTs entered the branch point. In axonal MTs in primary neurons, treatment with a low concentration of nocodazole significantly decreased the differences of detyrosination (deTyr) and tyrosination (Tyr) of tubulins, indicators of MT turnover. Expansion microscopy of the axonal shaft before the branch point revealed differences in deTyr/Tyr modification on MTs. Our model recapitulates the differences in MT turnover between branches and provides a feedback mechanism for MT regulation that depends on the axonal arbor geometry.

The geometry of representational drift in natural and artificial neural networks

Neurons in sensory areas encode/represent stimuli. Surprisingly, recent studies have suggested that, even during persistent performance, these representations are not stable and change over the course of days and weeks. We examine stimulus representations from fluorescence recordings across hundreds of neurons in the visual cortex using in vivo two-photon calcium imaging and we corroborate previous studies finding that such representations change as experimental trials are repeated across days. This phenomenon has been termed "representational drift". In this study we geometrically characterize the properties of representational drift in the primary visual cortex of mice in two open datasets from the Allen Institute and propose a potential mechanism behind such drift. We observe representational drift both for passively presented stimuli, as well as for stimuli which are behaviorally relevant. Across experiments, the drift differs from in-session variance and most often occurs along directions that have the most in-class variance, leading to a significant turnover in the neurons used for a given representation. Interestingly, despite this significant change due to drift, linear classifiers trained to distinguish neuronal representations show little to no degradation in performance across days. The features we observe in the neural data are similar to properties of artificial neural networks where representations are updated by continual learning in the presence of dropout, i.e. a random masking of nodes/weights, but not other types of noise. Therefore, we conclude that a potential reason for the representational drift in biological networks is driven by an underlying dropout-like noise while continuously learning and that such a mechanism may be computational advantageous for the brain in the same way it is for artificial neural networks, e.g. preventing overfitting.

Stability and dynamics of dendritic spines in macaque prefrontal cortex

Formation and elimination of synapses reflect structural plasticity of neuronal connectivity. Here we performed high-resolution two-photon imaging of dendritic spines in the prefrontal cortex of four macaque monkeys and found that spines were in general highly stable, with low percentages undergoing synaptic turnover. By observing the same spines at weekly intervals, we found that newly formed spines were more susceptible to elimination, with only 40% persisting over a period of months. Analyses of spatial distribution of large numbers of spines revealed that spine distribution was neither uniform nor random, favoring inter-spine distances of 2–4 μm. Furthermore, spine formation and elimination occurred more often in low- and high-density dendritic segments, respectively, and preferentially within a hot zone of ∼4 μm from existing spines. Our results demonstrate long-term stability and spatially regulated spine dynamics in the macaque cortex and provide a structural basis for understanding neural circuit plasticity in the primate brain.

Fluorescence imaging of large-scale neural ensemble dynamics

Recent progress in fluorescence imaging allows neuroscientists to observe the dynamics of thousands of individual neurons, identified genetically or by their connectivity, across multiple brain areas and for extended durations in awake behaving mammals. We discuss advances in fluorescent indicators of neural activity, viral and genetic methods to express these indicators, chronic animal preparations for long-term imaging studies, and microscopes to monitor and manipulate the activity of large neural ensembles. Ca2+ imaging studies of neural activity can track brain area interactions and distributed information processing at cellular resolution. Across smaller spatial scales, high-speed voltage imaging reveals the distinctive spiking patterns and coding properties of targeted neuron types. Collectively, these innovations will propel studies of brain function and dovetail with ongoing neuroscience initiatives to identify new neuron types and develop widely applicable, non-human primate models. The optical toolkit's growing sophistication also suggests that "brain observatory" facilities would be useful open resources for future brain-imaging studies.

Axonal spheroids in neurodegeneration

Axonal spheroids are bubble-like biological features that form on most degenerating axons, yet little is known about their influence on degenerative processes. Their formation and growth has been observed in response to various degenerative triggers such as injury, oxidative stress, inflammatory factors, and neurotoxic molecules. They often contain cytoskeletal elements and organelles, and, depending on the pathological insult, can colocalize with disease-related proteins such as amyloid precursor protein (APP), ubiquitin, and motor proteins. Initial formation of axonal spheroids depends on the disruption of axonal and membrane tension governed by cytoskeleton structure and calcium levels. Shortly after spheroid formation, the engulfment signal phosphatidylserine (PS) is exposed on the outer leaflet of spheroid plasma membrane, suggesting an important role for axonal spheroids in phagocytosis and debris clearance during degeneration. Spheroids can grow until they rupture, allowing pro-degenerative factors to exit the axon into extracellular space and accelerating neurodegeneration. Though much remains to be discovered in this area, axonal spheroid research promises to lend insight into the etiologies of neurodegenerative disease, and may be an important target for therapeutic intervention. This review summarizes over 100 years of work, describing what is known about axonal spheroid structure, regulation and function.

Dendritic Computing: Branching Deeper into Machine Learning

In this paper, we discuss the nonlinear computational power provided by dendrites in biological and artificial neurons. We start by briefly presenting biological evidence about the type of dendritic nonlinearities, respective plasticity rules and their effect on biological learning as assessed by computational models. Four major computational implications are identified as improved expressivity, more efficient use of resources, utilizing internal learning signals, and enabling continual learning. We then discuss examples of how dendritic computations have been used to solve real-world classification problems with performance reported on well known data sets used in machine learning. The works are categorized according to the three primary methods of plasticity used-structural plasticity, weight plasticity, or plasticity of synaptic delays. Finally, we show the recent trend of confluence between concepts of deep learning and dendritic computations and highlight some future research directions.

Marmosets: a promising model for probing the neural mechanisms underlying complex visual networks such as the frontal-parietal network

The technology, methodology and models used by visual neuroscientists have provided great insights into the structure and function of individual brain areas. However, complex cognitive functions arise in the brain due to networks comprising multiple interacting cortical areas that are wired together with precise anatomical connections. A prime example of this phenomenon is the frontal–parietal network and two key regions within it: the frontal eye fields (FEF) and lateral intraparietal area (area LIP). Activity in these cortical areas has independently been tied to oculomotor control, motor preparation, visual attention and decision-making. Strong, bidirectional anatomical connections have also been traced between FEF and area LIP, suggesting that the aforementioned visual functions depend on these inter-area interactions. However, advancements in our knowledge about the interactions between area LIP and FEF are limited with the main animal model, the rhesus macaque, because these key regions are buried in the sulci of the brain. In this review, we propose that the common marmoset is the ideal model for investigating how anatomical connections give rise to functionally-complex cognitive visual behaviours, such as those modulated by the frontal–parietal network, because of the homology of their cortical networks with humans and macaques, amenability to transgenic technology, and rich behavioural repertoire. Furthermore, the lissencephalic structure of the marmoset brain enables application of powerful techniques, such as array-based electrophysiology and optogenetics, which are critical to bridge the gaps in our knowledge about structure and function in the brain.

Eavesdropping wires: Recording activity in axons using genetically encoded calcium indicators

Neurons broadcast electrical signals to distal brain regions through extensive axonal arbors. Genetically encoded calcium sensors permit the direct observation of action potential activity at axonal terminals, providing unique insights on the organization and function of neural projections. Here, we consider what information can be gleaned from axonal recordings made at scales ranging from the summed activity extracted from multi-cell axon projections to single boutons. In particular, we discuss the application of different recently developed multi photon and fiber photometry methods for recording neural activity in axons of rodents. We define experimental difficulties associated with imaging approaches in the axonal compartment and highlight the latest methodological advances for addressing these issues. Finally, we reflect on ways in which new technologies can be used in conjunction with axon calcium imaging to address current questions in neurobiology.

Transcriptional Control of Parallel-Acting Pathways That Remove Specific Presynaptic Proteins in Remodeling Neurons

Synapses are actively dismantled to mediate circuit refinement, but the developmental pathways that regulate synaptic disassembly are largely unknown. We have previously shown that the epithelial sodium channel ENaC/UNC-8 triggers an activity-dependent mechanism that drives the removal of presynaptic proteins liprin-α/SYD-2, Synaptobrevin/SNB-1, RAB-3, and Endophilin/UNC-57 in remodeling GABAergic neurons in Caenorhabditis elegans (Miller-Fleming et al., 2016). Here, we report that the conserved transcription factor Iroquois/IRX-1 regulates UNC-8 expression as well as an additional pathway, independent of UNC-8, that functions in parallel to dismantle functional presynaptic terminals. We show that the additional IRX-1-regulated pathway is selectively required for the removal of the presynaptic proteins, Munc13/UNC-13 and ELKS, which normally mediate synaptic vesicle (SV) fusion and neurotransmitter release. Our findings are notable because they highlight the key role of transcriptional regulation in synapse elimination during development and reveal parallel-acting pathways that coordinate synaptic disassembly by removing specific active zone proteins.SIGNIFICANCE STATEMENT Synaptic pruning is a conserved feature of developing neural circuits but the mechanisms that dismantle the presynaptic apparatus are largely unknown. We have determined that synaptic disassembly is orchestrated by parallel-acting mechanisms that target distinct components of the active zone. Thus, our finding suggests that synaptic disassembly is not accomplished by en masse destruction but depends on mechanisms that dismantle the structure in an organized process.

Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions

Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model of peripheral lesioning and accurately reproduced the characteristics of network repair after deafferentation that are reported in experiments to study the activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we model deafferentation in a biologically realistic balanced network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex. Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed structural plasticity growth rules and the inhibitory synaptic plasticity mechanism that also balances our AI network both contribute to the restoration of the network to pre-deafferentation stable activity levels.

An accumulating body of evidence suggests that our brain can compensate for peripheral lesions by adaptive rewiring of its neuronal circuitry. The underlying process, structural plasticity, can modify the connectivity of neuronal networks in the brain, thus affecting their function. To better understand the mechanisms of structural plasticity in the brain, we have developed a novel model of peripheral lesions and the resulting activity-dependent rewiring in a simplified balanced cortical network model that exhibits biologically realistic Asynchronous Irregular (AI) activity. In order to accurately reproduce the directionality and course of network rewiring after injury that is observed in peripheral lesion experiments, we derive activity dependent growth rules for different synaptic elements: dendritic and axonal contacts. Our simulation results suggest that excitatory and inhibitory synaptic elements have to react to changes in neuronal activity in opposite ways. We show that these rules result in a homeostatic stabilisation of activity in individual neurons. In our simulations, both synaptic and structural plasticity mechanisms contribute to network repair. Furthermore, our simulations indicate that while activity is restored in neurons deprived by the peripheral lesion, the temporal firing characteristics of the network may not be retained by the rewiring process.

Learnable Heterogeneous Convolution: Learning both topology and strength

Existing convolution techniques in artificial neural networks suffer from huge computation complexity, while the biological neural network works in a much more powerful yet efficient way. Inspired by the biological plasticity of dendritic topology and synaptic strength, our method, Learnable Heterogeneous Convolution, realizes joint learning of kernel shape and weights, which unifies existing handcrafted convolution techniques in a data-driven way. A model based on our method can converge with structural sparse weights and then be accelerated by devices of high parallelism. In the experiments, our method either reduces VGG16/19 and ResNet34/50 computation by nearly 5× on CIFAR10 and 2× on ImageNet without harming the performance, where the weights are compressed by 10× and 4× respectively; or improves the accuracy by up to 1.0% on CIFAR10 and 0.5% on ImageNet with slightly higher efficiency. The code will be available on www.github.com/Genera1Z/LearnableHeterogeneousConvolution.

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.

Star power: the emerging role of astrocytes as neuronal partners during cortical plasticity

Plasticity is a fundamental property of neuronal circuits, allowing them to adapt to alterations in activation. Generally speaking, plasticity has been viewed from a 'neuron-centric' perspective, with changes in circuit function attributed to alterations in neuronal excitability, synaptic strength or neuronal connectivity. However, it is now clear that glial cells, in particular astrocytes, are key regulators of neuronal plasticity. This article reviews recent progress made in understanding astrocyte function and attempts to summarize these functions into a coherent framework that positions astrocytes as central players in the plasticity process.

Neuroplasticity of the visual cortex: in sickness and in health

Brain plasticity refers to the ability of synaptic connections to adapt their function and structure in response to experience, including environmental changes, sensory deprivation and injuries. Plasticity is a distinctive, but not exclusive, property of the developing nervous system. This review introduces the concept of neuroplasticity and describes classic paradigms to illustrate cellular and molecular mechanisms underlying synapse modifiability. Then, we summarize a growing number of studies showing that the adult cerebral cortex retains a significant degree of plasticity highlighting how the identification of strategies to enhance the plastic potential of the adult brain could pave the way for the development of novel therapeutic approaches aimed at treating amblyopia and other neurodevelopmental disorders. Finally, we analyze how the visual system adjusts to neurodegenerative conditions leading to blindness and we discuss the crucial role of spared plasticity in the visual system for sight recovery.

PEO-CYTOP Fluoropolymer Nanosheets as a Novel Open-Skull Window for Imaging of the Living Mouse Brain

In vivo two-photon deep imaging with a broad field of view has revealed functional connectivity among brain regions. Here, we developed a novel observation method that utilizes a polyethylene-oxide-coated CYTOP (PEO-CYTOP) nanosheet with a thickness of ∼130 nm that exhibited a water retention effect and a hydrophilized adhesive surface. PEO-CYTOP nanosheets firmly adhered to brain surfaces, which suppressed bleeding from superficial veins. By taking advantage of the excellent optical properties of PEO-CYTOP nanosheets, we performed in vivo deep imaging in mouse brains at high resolution. Moreover, PEO-CYTOP nanosheets enabled to prepare large cranial windows, achieving in vivo imaging of neural structure and Ca²⁺ elevation in a large field of view. Furthermore, the PEO-CYTOP nanosheets functioned as a sealing material, even after the removal of the dura. These results indicate that this method would be suitable for the investigation of neural functions that are composed of interactions among multiple regions.

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PEO-CYTOP nanosheet enables in vivo deep brain imaging in a vast field of view

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The 130 nm thickness and the hydrophilized surface realize the strong adhesiveness

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Suppressions of bleeding from the surface and inflammation in long-term are achieved

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The vast and transparent cranial window with natural curvature of the surface

Synaptic remodeling, lessons from C. elegans

Sydney Brenner's choice of Caenorhabditis elegans as a model organism for understanding the nervous system has accelerated discoveries of gene function in neural circuit development and behavior. In this review, we discuss a striking example of synaptic remodeling in the C. elegans motor circuit in which DD class motor neurons effectively reverse polarity as presynaptic and postsynaptic domains at opposite ends of the DD neurite switch locations. Originally revealed by EM reconstruction conducted over 40 years ago, DD remodeling has since been investigated by live cell imaging methods that exploit the power of C. elegans genetics to reveal key effectors of synaptic plasticity. Although synapses are also extensively rewired in developing mammalian circuits, the underlying remodeling mechanisms are largely unknown. Here, we highlight the possibility that studies in C. elegans can reveal pathways that orchestrate synaptic remodeling in more complex organisms. Specifically, we describe (1) transcription factors that regulate DD remodeling, (2) the cellular and molecular cascades that drive synaptic remodeling and (3) examples of circuit modifications in vertebrate neurons that share some similarities with synaptic remodeling in C. elegans DD neurons.

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.

Memory Engrams in the Neocortex

While in the past much of our knowledge about memory representations in the brain has relied on loss-of-function studies in which whole brain regions were temporarily inactivated or permanently lesioned, the recent development of new methods has ushered in a new era of downright "engram excitement." Animal research is now able to specifically label, track, and manipulate engram cells in the brain. While early studies have mostly focused on single brain regions like the hippocampus, recently more and more evidence for brain-wide distributed engram networks is emerging. Memory research in humans has also picked up pace, fueled by promising magnetic resonance imaging (MRI)-based methods like diffusion-weighted MRI (DW-MRI) and brain decoding. In this review, we will outline recent advancements in engram research, with a focus on human data and neocortical representations. We will illustrate the available noninvasive methods for the detection of engrams in different neocortical regions like the medial prefrontal cortex and the posterior parietal cortex and discuss evidence for systems consolidation and parallel memory encoding. Finally, we will explore how reactivation and prior knowledge can lead to and enhance engram formation in the neocortex.

Structural dynamics and stability of corticocortical and thalamocortical axon terminals during motor learning

Synaptic plasticity is the cellular basis of learning and memory. When animals learn a novel motor skill, synaptic modifications are induced in the primary motor cortex (M1), and new postsynaptic dendritic spines relevant to motor memory are formed in the early stage of learning. However, it is poorly understood how presynaptic axonal boutons are formed, eliminated, and maintained during motor learning, and whether long-range corticocortical and thalamocortical axonal boutons show distinct structural changes during learning. In this study, we conducted two-photon imaging of presynaptic boutons of long-range axons in layer 1 (L1) of the mouse M1 during the 7-day learning of an accelerating rotarod task. The training-period-averaged rate of formation of boutons on axons projecting from the secondary motor cortical area increased, while the average rate of elimination of those from the motor thalamus (thalamic boutons) decreased. In particular, the elimination rate of thalamic boutons during days 4-7 was lower than that in untrained mice, and the fraction of pre-existing thalamic boutons that survived until day 7 was higher than that in untrained mice. Our results suggest that the late stabilization of thalamic boutons in M1 contributes to motor skill learning.

Spatial and Spectral Mapping and Decomposition of Neural Dynamics and Organization of the Mouse Brain with Multispectral Optoacoustic Tomography

In traditional optical imaging, limited light penetration constrains high-resolution interrogation to tissue surfaces. Optoacoustic imaging combines the superb contrast of optical imaging with deep penetration of ultrasound, enabling a range of new applications. We used multispectral optoacoustic tomography (MSOT) for functional and structural neuroimaging in mice at resolution, depth, and specificity unattainable by other neuroimaging modalities. Based on multispectral readouts, we computed hemoglobin gradient and oxygen saturation changes related to processing of somatosensory signals in different structures along the entire subcortical-cortical axis. Using temporal correlation analysis and seed-based maps, we reveal the connectivity between cortical, thalamic, and sub-thalamic formations. With the same modality, high-resolution structural tomography of intact mouse brain was achieved based on endogenous contrasts, demonstrating near-perfect matches with anatomical features revealed by histology. These results extend the limits of noninvasive observations beyond the reach of standard high-resolution neuroimaging, verifying the suitability of MSOT for small-animal studies.

Expression patterns of SLIT/ROBO mRNAs reveal a characteristic feature in the entorhinal-hippocampal area of macaque monkeys

OBJECTIVE

SLITs are secreted glycoproteins that bind to Roundabouts (ROBOs) which are a family member of transmembrane receptors. SLIT signaling has well-conserved roles in mediating axon repulsion in a developing nervous system. We previously reported that SLIT1 mRNA is enriched in middle layers of the prefrontal cortex of macaque monkeys in a developmentally regulated manner. Other SLIT (SLIT2 and SLIT3) mRNAs showed preferential expressions in the prefrontal cortex with a distinct laminar pattern. To obtain further clues to the role of SLIT signaling in the organization of the primate brain, we performed ISH analysis of SLIT and ROBO mRNAs using adult macaque brain tissues.

RESULTS

In this study, we examined the expression patterns of SLITs and ROBOs (ROBO1 and ROBO2) in other brain regions, and found intense and characteristic expression patterns of these genes in the entorhinal-hippocampal area. In situ hybridization analysis revealed that SLIT1 and SLIT2 mRNAs showed marked complementary distribution in the entorhinal cortex. SLIT and ROBO mRNAs were widely expressed in the hippocampus with modest regional preference. These findings suggest that each SLIT gene has a specialized role that is particularly important for prefrontal as well as hippocampal connectivity in the primate cortex.

Autophagy lipidation machinery regulates axonal microtubule dynamics but is dispensable for survival of mammalian neurons

Neurons maintain axonal homeostasis via employing a unique organization of the microtubule (MT) cytoskeleton, which supports axonal morphology and provides tracks for intracellular transport. Abnormal MT-based trafficking hallmarks the pathology of neurodegenerative diseases, but the exact mechanism regulating MT dynamics in axons remains enigmatic. Here we report on a regulation of MT dynamics by AuTophaGy(ATG)-related proteins, which previously have been linked to the autophagy pathway. We find that ATG proteins required for LC3 lipid conjugation are dispensable for survival of excitatory neurons and instead regulate MT stability via controlling the abundance of the MT-binding protein CLASP2. This function of ATGs is independent of their role in autophagy and requires the active zone protein ELKS1. Our results highlight a non-canonical role of ATG proteins in neurons and suggest that pharmacological activation of autophagy may not only promote the degradation of cytoplasmic material, but also impair axonal integrity via altering MT stability.

In neurons, the microtubule cytoskeleton provides support and directionality of axons. Here, the authors report that microtubule dynamics in axons may be regulated by the autophagy proteins (ATGs) independently of their known role in autophagy.

Stable memory and computation in randomly rewiring neural networks

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

Identifying Weak Signals in Inhomogeneous Neuronal Images for Large-Scale Tracing of Sparsely Distributed Neurites

Tracing neurites constitutes the core of neuronal morphology reconstruction, a key step toward neuronal circuit mapping. Modern optical-imaging techniques allow observation of nearly complete mouse neuron morphologies across brain regions or even the whole brain. However, high-level automation reconstruction of neurons, i.e., the reconstruction with a few of manual edits requires discrimination of weak foreground points from the inhomogeneous background. We constructed an identification model, where empirical observations made from neuronal images were summarized into rules for designing feature vectors that to classify foreground and background, and a support vector machine (SVM) was used to learn these feature vectors. We embedded this constructed SVM classifier into a previously developed tool, SparseTracer, to obtain SparseTracer-Learned Feature Vector (ST-LFV). ST-LFV can trace sparsely distributed neurites with weak signals (contrast-to-noise ratio < 1.5) against an inhomogeneous background in datasets imaged by widely used light-microscopy techniques like confocal microscopy and two-photon microscopy. Moreover, 12 sub-blocks were extracted from different brain regions. The average recall and precision rates were 99% and 97%, respectively. These results indicated that ST-LFV is well suited for weak signal identification with varying image characteristics. We also applied ST-LFV to trace long-range neurites from images where neurites are sparsely distributed but their image intensities are weak in some cases. When tracing this long-range neurites, manual edit was required once to obtain results equivalent to the ground truth, compared with 20 times of manual edits required by SparseTracer. This improvement in the level of automatic reconstruction indicates that ST-LFV has the potential to rapidly reconstruct sparsely distributed neurons at the large scale.

Axonal plasticity associated with perceptual learning in adult macaque primary visual cortex

Perceptual learning is associated with changes in the functional properties of neurons even in primary sensory areas. In macaque monkeys trained to perform a contour detection task, we have observed changes in contour-related facilitation of neuronal responses in primary visual cortex that track their improvement in performance on a contour detection task. We have previously explored the anatomical substrate of experience-dependent changes in the visual cortex based on a retinal lesion model, where we find sprouting and pruning of the axon collaterals in the cortical lesion projection zone. Here, we attempted to determine whether similar changes occur under normal visual experience, such as that associated with perceptual learning. We labeled the long-range horizontal connections in visual cortex by virally mediated transfer of genes expressing fluorescent probes, which enabled us to do longitudinal two-photon imaging of axonal arbors over the period during which animals improve in contour detection performance. We found that there are substantial changes in the axonal arbors of neurons in cortical regions representing the trained part of the visual field, with sprouting of new axon collaterals and pruning of preexisting axon collaterals. Our findings indicate that changes in the structure of axonal arbors are part of the circuit-level mechanism of perceptual learning, and further support the idea that the learned information is encoded at least in part in primary visual cortex.

A transgenic zebrafish model for in vivo long-term imaging of retinotectal synaptogenesis

The retinotectal synapse in larval zebrafish, combined with live time-lapse imaging, provides an advantageous model for study of the development and remodelling of central synapses in vivo. In previous studies, these synapses were labelled by transient expression of fluorescence-tagged synaptic proteins, which resulted in the dramatic variation of labelling patterns in each larva. Here, using GAL4-Upstream Activating Sequence (GAL4-UAS) methodology, we generated stable transgenic lines, which express EGFP-tagged synaptophysin (a presynaptic protein) in retinal ganglion cells (RGCs), to reliably label the pre-synaptic site of retinotectal synapses. This tool avoids the variable labelling of RGCs that occurs in transient transgenic larvae. We obtained several stable transgenic lines that differ consistently in the number of labelled RGCs. Using stable lines that consistently had a single labelled RGC, we could trace synaptogenic dynamics on an individual RGC axonal arbor across different developmental stages. In the stable lines that consistently had multiple labelled RGCs, we could simultaneously monitor both pre- and post-synaptic compartments by combining transient labelling of post-synaptic sites on individual tectal neurons. These tools allowed us to investigate molecular events underlying synaptogenesis and found that the microRNA-132 (miR-132) is required for developmental synaptogenesis. Thus, these transgenic zebrafish stable lines provide appropriate tools for studying central synaptogenesis and underlying molecular mechanisms in intact vertebrate brain.

Relationships in Ongoing Structural Maintenances of the Two Cerebral Cortices of an Individual Brain

A human brain has separate left and right cerebral cortices, each of which must be continuously structurally maintained during adulthood. There is no understanding of how ongoing structural maintenances of separate parts of a mature individual brain, including the 2 cortices, are related. To explore this issue, this study used an unconventional N-of-1 magnetic resonance imaging time-series paradigm to identify relationships between maintenances of structural thicknesses of the 2 cortices in an adult human brain over week intervals for 6 months. The results suggest that maintenances of left and right cortical thicknesses were symmetrically related in some, but asymmetrically related in other, respects. For matched times, thickness magnitudes and variations on the 2 sides were positively correlated and appeared to reflect maintenance symmetry. Maintenance relationships also extended from earlier to later times with temporal continuity and apparent “if-then” contingencies which were reflected in symmetry and asymmetry dynamics spanning 1- to 2-week periods. The findings suggest concepts of individual brain cortical maintenance symmetry, asymmetry, and temporal continuity dynamics that have not been previously recognized. They have implications for defining cortical maintenance traits or states and for development of N-of-1 precision medicine paradigms that can contribute to understanding individual brain health.

Increased Axonal Bouton Stability during Learning in the Mouse Model of MECP2 Duplication Syndrome

MECP2 duplication syndrome is an X-linked form of syndromic autism caused by genomic duplication of the region encoding methyl-CpG-binding protein 2 (MECP2). Mice overexpressing MECP2 demonstrate social impairment, behavioral inflexibility, and altered patterns of learning and memory. Previous work showed abnormally increased stability of dendritic spines formed during motor training in the apical tuft of primary motor cortex (area M1) corticospinal neurons in the MECP2 duplication mouse model. In the current study, we measure the structural plasticity of axonal boutons in layer 5 pyramidal neuron projections to layer 1 of area M1 during motor training. In wild-type littermate control mice, we find that during rotarod training the bouton formation rate changes minimally, if at all, while the bouton elimination rate more than doubles. Notably, the observed upregulation in bouton elimination with training is absent in MECP2 duplication mice. This result provides further evidence of an imbalance between structural stability and plasticity in this form of syndromic autism. Furthermore, the observation that axonal bouton elimination more than doubles with motor training in wild-type animals contrasts with the increase of dendritic spine consolidation observed in corticospinal neurons at the same layer. This dissociation suggests that different area M1 microcircuits may manifest different patterns of structural synaptic plasticity during motor training.

Axon Collaterals and Brain States

Multiple mechanisms have been identified as relevant to plasticity, functional stability, and reliable processing across brain states. In the context of stability under "ever-changing conditions" (this Topic), the role of axons has been relatively under-investigated. The highly branched topologies of many axons, however, seem well designed to differentially recruit and regulate distributed postsynaptic groups, possibly in a state-dependent fashion. In this Perspective, I briefly discuss several examples of axon collateralization, and then some of the branch-specific features that might subserve differential recruitment and whole brain activation. An emerging principle is that the number of collaterals and number of target structures are not stereotyped. Rather, axons originating from one defined source typically send branches to diversified subsets of target areas. This could achieve heterogeneous inputs, with different degrees of synchronicity. Variability of neuronal responses has been suggested as inversely proportional to the degree of temporally correlated input. Increased input homogeneity, driven by sensory stimulation or behavioral conditions, is reported to reduce neuronal variability, with axon collateralization potentially having an important role.

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning

Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.

Spiking Neural Classifier with Lumped Dendritic Nonlinearity and Binary Synapses: A Current Mode VLSI Implementation and Analysis

We present a neuromorphic current mode implementation of a spiking neural classifier with lumped square law dendritic nonlinearity. It has been shown previously in software simulations that such a system with binary synapses can be trained with structural plasticity algorithms to achieve comparable classification accuracy with fewer synaptic resources than conventional algorithms. We show that even in real analog systems with manufacturing imperfections (CV of 23.5% and 14.4% for dendritic branch gains and leaks respectively), this network is able to produce comparable results with fewer synaptic resources. The chip fabricated in [Formula: see text]m complementary metal oxide semiconductor has eight dendrites per cell and uses two opposing cells per class to cancel common-mode inputs. The chip can operate down to a [Formula: see text] V and dissipates 19 nW of static power per neuronal cell and [Formula: see text] 125 pJ/spike. For two-class classification problems of high-dimensional rate encoded binary patterns, the hardware achieves comparable performance as software implementation of the same with only about a 0.5% reduction in accuracy. On two UCI data sets, the IC integrated circuit has classification accuracy comparable to standard machine learners like support vector machines and extreme learning machines while using two to five times binary synapses. We also show that the system can operate on mean rate encoded spike patterns, as well as short bursts of spikes. To the best of our knowledge, this is the first attempt in hardware to perform classification exploiting dendritic properties and binary synapses.

Intracellular Ca2+ stores control in vivo neuronal hyperactivity in a mouse model of Alzheimer's disease

Neuronal hyperactivity is the emerging functional hallmark of Alzheimer's disease (AD) in both humans and different mouse models, mediating an impairment of memory and cognition. The mechanisms underlying neuronal hyperactivity remain, however, elusive. In vivo Ca²⁺ imaging of somatic, dendritic, and axonal activity patterns of cortical neurons revealed that both healthy aging and AD-related mutations augment neuronal hyperactivity. The AD-related enhancement occurred even without amyloid deposition and neuroinflammation, mainly due to presenilin-mediated dysfunction of intracellular Ca²⁺ stores in presynaptic boutons, likely causing more frequent activation of synaptic NMDA receptors. In mutant but not wild-type mice, store emptying reduced both the frequency and amplitude of presynaptic Ca²⁺ transients and, most importantly, normalized neuronal network activity. Postsynaptically, the store dysfunction was minor and largely restricted to hyperactive cells. These findings identify presynaptic Ca²⁺ stores as a key element controlling AD-related neuronal hyperactivity and as a target for disease-modifying treatments.

Experience-Dependent Structural Plasticity in the Visual System

During development, the environment exerts a profound influence on the wiring of brain circuits. Due to the limited resolution of studies in fixed tissue, this experience-dependent structural plasticity was once thought to be restricted to a specific developmental time window. The recent introduction of two-photon microscopy for in vivo imaging has opened the door to repeated monitoring of individual neurons and the study of structural plasticity mechanisms at a very fine scale. In this review, we focus on recent work showing that synaptic structural rearrangements are a key mechanism mediating neural circuit adaptation and behavioral plasticity in the adult brain. We examine this work in the context of classic studies in the visual systems of model organisms, which have laid much of the groundwork for our understanding of activity-dependent synaptic remodeling and its role in brain plasticity.

Computer assisted detection of axonal bouton structural plasticity in in vivo time-lapse images

The ability to measure minute structural changes in neural circuits is essential for long-term in vivo imaging studies. Here, we propose a methodology for detection and measurement of structural changes in axonal boutons imaged with time-lapse two-photon laser scanning microscopy (2PLSM). Correlative 2PLSM and 3D electron microscopy (EM) analysis, performed in mouse barrel cortex, showed that the proposed method has low fractions of false positive/negative bouton detections (2/0 out of 18), and that 2PLSM-based bouton weights are correlated with their volumes measured in EM (r = 0.93). Next, the method was applied to a set of axons imaged in quick succession to characterize measurement uncertainty. The results were used to construct a statistical model in which bouton addition, elimination, and size changes are described probabilistically, rather than being treated as deterministic events. Finally, we demonstrate that the model can be used to quantify significant structural changes in boutons in long-term imaging experiments.

An Exploration Into Short-Interval Maintenance of Adult Hemispheric Cortical Thickness at an Individual Brain Level

Adult cerebral cortical structure is thought to be statically maintained over short intervals. This view is based on group average findings but has never been studied at the individual level. This issue was examined with an unconventional longitudinal magnetic resonance imaging design which measured hemispheric mean cortical thickness of an adult man repeatedly at week intervals over 6 months. These measures were compared with measurement error estimates to test the current prediction that thickness measures would be statically maintained within measurement error variation. The results did not support this prediction. Thickness underwent incremental and decremental fluctuations which ranged up to 0.12 mm and 5.83% over week and multiweek intervals and which differed from measurement error variation. These exploratory analyses suggest a working hypothesis that short-interval cortical structural maintenance in an individual can involve fluctuations in thickness. If confirmed, this hypothesis has potential implications for cortical maintenance mechanisms and precision medicine approaches.

Dynamic Reorganization of Neuronal Activity Patterns in Parietal Cortex

Neuronal representations change as associations are learned between sensory stimuli and behavioral actions. However, it is poorly understood whether representations for learned associations stabilize in cortical association areas or continue to change following learning. We tracked the activity of posterior parietal cortex neurons for a month as mice stably performed a virtual-navigation task. The relationship between cells' activity and task features was mostly stable on single days but underwent major reorganization over weeks. The neurons informative about task features (trial type and maze locations) changed across days. Despite changes in individual cells, the population activity had statistically similar properties each day and stable information for over a week. As mice learned additional associations, new activity patterns emerged in the neurons used for existing representations without greatly affecting the rate of change of these representations. We propose that dynamic neuronal activity patterns could balance plasticity for learning and stability for memory.

On the 'data stirring' role of the dentate gyrus of the hippocampus

Understanding hippocampal (HC) function, as it is presently known, includes exploring the HC role in episodic memory storage. As pointed out by Teyler and DiScenna in the 1980s, the apparatus needed for recalling a stored episode, and awakening all its components in a coordinated manner, by necessity includes a triggering device able to reach each of the mental entities that must be awakened. In the context of neuronal networks, the triggering device in question takes the form of a large cell assembly, a separate one made for every new episode stored. The present paper deals with the creation and the properties of these cell assemblies ('pointer groups'). To perform the function of episodic memory retrieval, each of these must possess the information capacity (entropy) enabling it to single out an episode and the network connections enabling it to reach all components of it; further, to deal with the unpredictability of the memory items it has to address, it must have its member neurons well distributed through the length of the network (the HC). The requirements imply that the creation of a pointer group must include a randomizing step analogous to 'stirring'. It is argued that many of the known peculiarities of granule cells in the dentate gyrus arise as solutions to the practical problems presented by the creation of the pointer groups and the details of 'stirring', and so do a series of other features of the HC network, some of them only discovered in the last few years.

Activity-dependent axonal plasticity in sensory systems

The rodent whisker-to-barrel cortex pathway is a classic model to study the effects of sensory experience and deprivation on neuronal circuit formation, not only during development but also in the adult. Decades of research have produced a vast body of evidence highlighting the fundamental role of neuronal activity (spontaneous and/or sensory-evoked) for circuit formation and function. In this context, it has become clear that neuronal adaptation and plasticity is not just a function of the neonatal brain, but persists into adulthood, especially after experience-driven modulation of network status. Mechanisms for structural remodeling of the somatodendritic or axonal domain include microscale alterations of neurites or synapses. At the same time, functional alterations at the nanoscale such as expression or activation changes of channels and receptors contribute to the modulation of intrinsic excitability or input-output relationships. However, it remains elusive how these forms of structural and functional plasticity come together to shape neuronal network formation and function. While specifically somatodendritic plasticity has been studied in great detail, the role of axonal plasticity, (e.g. at presynaptic boutons, branches or axonal microdomains), is rather poorly understood. Therefore, this review will only briefly highlight somatodendritic plasticity and instead focus on axonal plasticity. We discuss (i) the role of spontaneous and sensory-evoked plasticity during critical periods, (ii) the assembly of axonal presynaptic sites, (iii) axonal plasticity in the mature brain under baseline and sensory manipulation conditions, and finally (iv) plasticity of electrogenic axonal microdomains, namely the axon initial segment, during development and in the mature CNS.

Microglia in steady state

Microglial cells are the resident tissue macrophages of the CNS and are widely recognized for their immune surveillance of the healthy CNS. In addition to this well-accepted function, recent findings point to major roles for microglia in instructing and regulating the proper function of the neuronal networks in the adult CNS, but these cells are also involved in creating neuronal networks by orchestrating construction of the whole network during development. In this Review, we highlight recent findings about the steady-state functions of microglial cells, the factors that are important for physiological microglial function, and how microglia help to maintain tissue homeostasis in the CNS.

A stable brain from unstable components: Emerging concepts and implications for neural computation

Neuroscientists have often described the adult brain in similar terms to an electronic circuit board- dependent on fixed, precise connectivity. However, with the advent of technologies allowing chronic measurements of neural structure and function, the emerging picture is that neural networks undergo significant remodeling over multiple timescales, even in the absence of experimenter-induced learning or sensory perturbation. Here, we attempt to reconcile the parallel observations that critical brain functions are stably maintained, while synapse- and single-cell properties appear to be reformatted regularly throughout adult life. In this review, we discuss experimental evidence at multiple levels ranging from synapses to neuronal ensembles, suggesting that many parameters are maintained in a dynamic equilibrium. We highlight emerging hypotheses that could explain how stable brain functions may be generated from dynamic elements. Furthermore, we discuss the impact of dynamic circuit elements on neural computations, and how they could provide living neural circuits with computational abilities a fixed structure cannot offer. Taken together, recent evidence indicates that continuous dynamics are a fundamental property of neural circuits compatible with macroscopically stable behaviors. In addition, they may be a unique advantage imparting robustness and flexibility throughout life.

Perturbation of Serotonin Homeostasis during Adulthood Affects Serotonergic Neuronal Circuitry

Growing evidence shows that the neurotransmitter serotonin (5-HT) modulates the fine-tuning of neuron development and the establishment of wiring patterns in the brain. However, whether serotonin is involved in the maintenance of neuronal circuitry in the adult brain remains elusive. Here, we use a Tph2^fl°^x conditional knockout (cKO) mouse line to assess the impact of serotonin depletion during adulthood on serotonergic system organization. Data show that the density of serotonergic fibers is increased in the hippocampus and decreased in the thalamic paraventricular nucleus (PVN) as a consequence of brain serotonin depletion. Strikingly, these defects are rescued following reestablishment of brain 5-HT signaling via administration of the serotonin precursor 5-hydroxytryptophan (5-HTP). Finally, 3D reconstruction of serotonergic fibers reveals that changes in serotonin homeostasis affect axonal branching complexity. These data demonstrate that maintaining proper serotonin homeostasis in the adult brain is crucial to preserve the correct serotonergic axonal wiring.

Application of viral vectors to the study of neural connectivities and neural circuits in the marmoset brain

It is important to study the neural connectivities and functions in primates. For this purpose, it is critical to be able to transfer genes to certain neurons in the primate brain so that we can image the neuronal signals and analyze the function of the transferred gene. Toward this end, our team has been developing gene transfer systems using viral vectors. In this review, we summarize our current achievements as follows. 1) We compared the features of gene transfer using five different AAV serotypes in combination with three different promoters, namely, CMV, mouse CaMKII (CaMKII), and human synapsin 1 (hSyn1), in the marmoset cortex with those in the mouse and macaque cortices. 2) We used target‐specific double‐infection techniques in combination with TET‐ON and TET‐OFF using lentiviral retrograde vectors for enhanced visualization of neural connections. 3) We used an AAV‐mediated gene transfer method to study the transcriptional control for amplifying fluorescent signals using the TET/TRE system in the primate neocortex. We also established systems for shRNA mediated gene targeting in a neocortical region where a gene is significantly expressed and for expressing the gene using the CMV promoter for an unexpressed neocortical area in the primate cortex using AAV vectors to understand the regulation of downstream genes. Our findings have demonstrated the feasibility of using viral vector mediated gene transfer systems for the study of primate cortical circuits using the marmoset as an animal model. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 354–372, 2017