Structured Synaptic Connectivity between Hippocampal Regions

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

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

Half a century legacy of long-term potentiation

In 1973, two papers from Bliss and Lømo and from Bliss and Gardner-Medwin reported that high-frequency synaptic stimulation in the dentate gyrus of rabbits resulted in a long-lasting increase in synaptic strength. This form of synaptic plasticity, commonly referred to as long-term potentiation (LTP), was immediately considered as an attractive mechanism accounting for the ability of the brain to store information. In this historical piece looking back over the past 50 years, we discuss how these two landmark contributions directly motivated a colossal research effort and detail some of the resulting milestones that have shaped our evolving understanding of the molecular and cellular underpinnings of LTP. We highlight the main features of LTP, cover key experiments that defined its induction and expression mechanisms, and outline the evidence supporting a potential role of LTP in learning and memory. We also briefly explore some ramifications of LTP on network stability, consider current limitations of LTP as a model of associative memory, and entertain future research orientations.

Engram mechanisms of memory linking and identity

Memories are thought to be stored in neuronal ensembles referred to as engrams. Studies have suggested that when two memories occur in quick succession, a proportion of their engrams overlap and the memories become linked (in a process known as prospective linking) while maintaining their individual identities. In this Review, we summarize the key principles of memory linking through engram overlap, as revealed by experimental and modelling studies. We describe evidence of the involvement of synaptic memory substrates, spine clustering and non-linear neuronal capacities in prospective linking, and suggest a dynamic somato-synaptic model, in which memories are shared between neurons yet remain separable through distinct dendritic and synaptic allocation patterns. We also bring into focus retrospective linking, in which memories become associated after encoding via offline reactivation, and discuss key temporal and mechanistic differences between prospective and retrospective linking, as well as the potential differences in their cognitive outcomes.

Higher-order thalamocortical circuits are specified by embryonic cortical progenitor types in the mouse brain

The sensory cortex receives synaptic inputs from both first-order and higher-order thalamic nuclei. First-order inputs relay simple stimulus properties from the periphery, whereas higher-order inputs relay more complex response properties, provide contextual feedback, and modulate plasticity. Here, we reveal that a cortical neuron's higher-order input is determined by the type of progenitor from which it is derived during embryonic development. Within layer 4 (L4) of the mouse primary somatosensory cortex, neurons derived from intermediate progenitors receive stronger higher-order thalamic input and exhibit greater higher-order sensory responses. These effects result from differences in dendritic morphology and levels of the transcription factor Lhx2, which are specified by the L4 neuron's progenitor type. When this mechanism is disrupted, cortical circuits exhibit altered higher-order responses and sensory-evoked plasticity. Therefore, by following distinct trajectories, progenitor types generate diversity in thalamocortical circuitry and may provide a general mechanism for differentially routing information through the cortex.

Environmental toxicology of bisphenol A: Mechanistic insights and clinical implications on the neuroendocrine system

Bisphenol A (BPA) is a widely used environmental estrogen found in a variety of products, including food packaging, canned goods, baby bottle soothers, reusable cups, medical devices, tableware, dental sealants, and other consumer goods. This substance has been found to have detrimental effects on both the environment and human health, particularly on the reproductive, immune, embryonic development, nervous, endocrine, and respiratory systems. This paper aims to provide a comprehensive review of the effects of BPA on the neuroendocrine system, with a primary focus on its impact on the brain, neurons, oligodendrocytes, neural stem cell proliferation, DNA damage, and behavioral development. Additionally, the review explores the clinical implications of BPA, specifically examining its role in the onset and progression of various diseases associated with the neuroendocrine metabolic system. By delving into the mechanistic analysis and clinical implications, this review aims to serve as a valuable resource for studying the impacts of BPA exposure on organisms.

Receive, Retain and Retrieve: Psychological and Neurobiological Perspectives on Memory Retrieval

Memory and learning are interdependent processes that involve encoding, storage, and retrieval. Especially memory retrieval is a fundamental cognitive ability to recall memory traces and update stored memory with new information. For effective memory retrieval and learning, the memory must be stabilized from short-term memory to long-term memory. Hence, it is necessary to understand the process of memory retention and retrieval that enhances the process of learning. Though previous cognitive neuroscience research has focused on memory acquisition and storage, the neurobiological mechanisms underlying memory retrieval and its role in learning are less understood. Therefore, this article offers the viewpoint that memory retrieval is essential for selecting, reactivating, stabilizing, and storing information in long-term memory. In arguing how memories are retrieved, consolidated, transmitted, and strengthened for the long term, the article will examine the psychological and neurobiological aspects of memory and learning with synaptic plasticity, long-term potentiation, genetic transcription, and theta oscillation in the brain.

Synaptic cell-adhesion molecule latrophilin-2 is differentially directed to dendritic domains of hippocampal neurons

Hippocampal pyramidal cells possess elaborate dendritic arbors with distinct domains that are targeted with input-specific synaptic sites. This synaptic arrangement is facilitated by synaptic cell-adhesion molecules that act as recognition elements to connect presynaptic and postsynaptic neurons. In this study, we investigate the organization of the synaptic recognition molecule latrophilin-2 at the surface of pyramidal neurons classified by spatial positioning and action potential firing patterns. Surveying two hippocampal neurons that highly express latrophilin-2, late-bursting CA1 pyramidal cells and early-bursting subiculum pyramidal cells, we found the molecule to be differentially positioned on their respective dendritic compartments. Investigating this latrophilin-2 positioning at the synaptic level, we found that the molecule is not present within either the pre- or postsynaptic terminal but rather is tightly coupled to synapses at a perisynaptic location. Together these findings indicate that hippocampal latrophilin-2 distribution patterning is cell-type specific, and requires multiple postsynaptic neurons for its synaptic localization.

Genetic context drives age-related disparities in synaptic maintenance and structure across cortical and hippocampal neuronal circuits

The disconnection of neuronal circuitry through synaptic loss is presumed to be a major driver of age-related cognitive decline. Age-related cognitive decline is heterogeneous, yet whether genetic mechanisms differentiate successful from unsuccessful cognitive decline through maintenance or vulnerability of synaptic connections remains unknown. Previous work using rodent and primate models leveraged various techniques to imply that age-related synaptic loss is widespread on pyramidal cells in prefrontal cortex (PFC) circuits but absent on those in area CA1 of the hippocampus. Here, we examined the effect of aging on synapses on projection neurons forming a hippocampal-cortico-thalamic circuit important for spatial working memory tasks from two genetically distinct mouse strains that exhibit susceptibility (C57BL/6J) or resistance (PWK/PhJ) to cognitive decline during aging. Across both strains, synapse density on CA1-to-PFC projection neurons appeared completely intact with age. In contrast, we found synapse loss on PFC-to-nucleus reuniens (RE) projection neurons from aged C57BL/6J but not PWK/PhJ mice. Moreover, synapses from aged PWK/PhJ mice but not from C57BL/6J exhibited altered morphologies that suggest increased efficiency to drive depolarization in the parent dendrite. Our findings suggest resistance to age-related cognitive decline results in part by age-related synaptic adaptations, and identification of these mechanisms in PWK/PhJ mice could uncover new therapeutic targets for promoting successful cognitive aging and extending human health span.

Translating single-neuron axonal reconstructions into meso-scale connectivity statistics in the mouse somatosensory thalamus

Characterizing the connectomic and morphological diversity of thalamic neurons is key for better understanding how the thalamus relays sensory inputs to the cortex. The recent public release of complete single-neuron morphological reconstructions enables the analysis of previously inaccessible connectivity patterns from individual neurons. Here we focus on the Ventral Posteromedial (VPM) nucleus and characterize the full diversity of 257 VPM neurons, obtained by combining data from the MouseLight and Braintell projects. Neurons were clustered according to their most dominantly targeted cortical area and further subdivided by their jointly targeted areas. We obtained a 2D embedding of morphological diversity using the dissimilarity between all pairs of axonal trees. The curved shape of the embedding allowed us to characterize neurons by a 1-dimensional coordinate. The coordinate values were aligned both with the progression of soma position along the dorsal-ventral and lateral-medial axes and with that of axonal terminals along the posterior-anterior and medial-lateral axes, as well as with an increase in the number of branching points, distance from soma and branching width. Taken together, we have developed a novel workflow for linking three challenging aspects of connectomics, namely the topography, higher order connectivity patterns and morphological diversity, with VPM as a test-case. The workflow is linked to a unified access portal that contains the morphologies and integrated with 2D cortical flatmap and subcortical visualization tools. The workflow and resulting processed data have been made available in Python, and can thus be used for modeling and experimentally validating new hypotheses on thalamocortical connectivity.

Topological analysis of sharp-wave ripple waveforms reveals input mechanisms behind feature variations

The reactivation of experience-based neural activity patterns in the hippocampus is crucial for learning and memory. These reactivation patterns and their associated sharp-wave ripples (SWRs) are highly variable. However, this variability is missed by commonly used spectral methods. Here, we use topological and dimensionality reduction techniques to analyze the waveform of ripples recorded at the pyramidal layer of CA1. We show that SWR waveforms distribute along a continuum in a low-dimensional space, which conveys information about the underlying layer-specific synaptic inputs. A decoder trained in this space successfully links individual ripples with their expected sinks and sources, demonstrating how physiological mechanisms shape SWR variability. Furthermore, we found that SWR waveforms segregated differently during wakefulness and sleep before and after a series of cognitive tasks, with striking effects of novelty and learning. Our results thus highlight how the topological analysis of ripple waveforms enables a deeper physiological understanding of SWRs.

GABAergic-like dopamine synapses in the brain

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

PIEZO2 in somatosensory neurons controls gastrointestinal transit

The gastrointestinal tract is in a state of constant motion. These movements are tightly regulated by the presence of food and help digestion by mechanically breaking down and propelling gut content. Mechanical sensing in the gut is thought to be essential for regulating motility; however, the identity of the neuronal populations, the molecules involved, and the functional consequences of this sensation are unknown. Here, we show that humans lacking PIEZO2 exhibit impaired bowel sensation and motility. Piezo2 in mouse dorsal root, but not nodose ganglia is required to sense gut content, and this activity slows down food transit rates in the stomach, small intestine, and colon. Indeed, Piezo2 is directly required to detect colon distension in vivo. Our study unveils the mechanosensory mechanisms that regulate the transit of luminal contents throughout the gut, which is a critical process to ensure proper digestion, nutrient absorption, and waste removal.

How Do You Build a Cognitive Map? The Development of Circuits and Computations for the Representation of Space in the Brain

In mammals, the activity of neurons in the entorhinal-hippocampal network is modulated by the animal's position and its movement through space. At multiple stages of this distributed circuit, distinct populations of neurons can represent a rich repertoire of navigation-related variables like the animal's location, the speed and direction of its movements, or the presence of borders and objects. Working together, spatially tuned neurons give rise to an internal representation of space, a cognitive map that supports an animal's ability to navigate the world and to encode and consolidate memories from experience. The mechanisms by which, during development, the brain acquires the ability to create an internal representation of space are just beginning to be elucidated. In this review, we examine recent work that has begun to investigate the ontogeny of circuitry, firing patterns, and computations underpinning the representation of space in the mammalian brain.

Symbiosis of an artificial neural network and models of biological neurons: Training and testing

In this paper, we show the possibility of creating and identifying the features of an artificial neural network (ANN), which consists of mathematical models of biological neurons. The FitzHugh-Nagumo (FHN) system is used as a paradigmatic model demonstrating basic neuron activities. First, in order to reveal how biological neurons can be embedded within an ANN, we train the ANN with nonlinear neurons to solve a basic image recognition problem with an MNIST database; next, we describe how FHN systems can be introduced into this trained ANN. After all, we show that an ANN with FHN systems inside can be successfully trained with improved accuracy comparing with first trained ANN and then with inserted FHN systems. This approach opens up great opportunities in terms of the direction of analog neural networks, in which artificial neurons can be replaced by more appropriate biological ones.

BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

Developmental timing-dependent organization of synaptic connections between mossy fibers and granule cells in the cerebellum

The long-standing hypothesis that synapses between mossy fibers (MFs) and cerebellar granule cells (GCs) are organized according to the origins of MFs and locations of GC axons, parallel fibers (PFs), is supported by recent findings. However, the mechanisms of such organized synaptic connections remain unknown. Here, using our technique that enabled PF location-dependent labeling of GCs in mice, we confirmed that synaptic connections of GCs with specific MFs originating from the pontine nucleus (PN-MFs) and dorsal column nuclei (DCoN-MFs) were gently but differentially organized according to their PF locations. We then found that overall MF-GC synaptic connectivity was biased in a way that dendrites of GCs having nearby PFs tended to connect with the same MF terminals, implying that the MF origin- and PF location-dependent organization is associated with the overall biased MF-GC synaptic connectivity. Furthermore, the development of PN-MFs preceded that of DCoN-MFs, which matches the developmental sequence of GCs that preferentially connect with each type of these MFs. Thus, our results revealed that overall MF-GC synaptic connectivity is biased in terms of PF locations, and suggested that such connectivity is likely the result of synaptic formation between developmental timing-matched partners.

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

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

Arc-driven mGRASP highlights CA1 to CA3 synaptic engrams

Subpopulations of neurons display increased activity during memory encoding and manipulating the activity of these neurons can induce artificial formation or erasure of memories. Thus, these neurons are thought to be cellular engrams. Moreover, correlated activity between pre- and postsynaptic engram neurons is thought to lead to strengthening of their synaptic connections, thus increasing the probability of neural activity patterns occurring during encoding to reoccur at recall. Therefore, synapses between engram neurons can also be considered as a substrate of memory, or a synaptic engram. One can label synaptic engrams by targeting two complementary, non-fluorescent, synapse-targeted GFP fragments separately to the pre- and postsynaptic compartment of engram neurons; the two GFP fragments reconstitute a fluorescent GFP at the synaptic cleft between the engram neurons, thereby highlighting synaptic engrams. In this work we explored a transsynaptic GFP reconstitution system (mGRASP) to label synaptic engrams between hippocampal CA1 and CA3 engram neurons identified by different Immediate-Early Genes: cFos and Arc. We characterized the expression of the cellular and synaptic labels of the mGRASP system upon exposure to a novel environment or learning of a hippocampal-dependent memory task. We found that mGRASP under the control of transgenic ArcCre^ERT2 labeled synaptic engrams more efficiently than when controlled by viral cFostTA, possibly due to differences in the genetic systems rather than the specific IEG promoters.

Hippocampal cellular functional organization for fear memory: Effects of sleep

Memory is vital to our daily existence. Although a large number of studies have suggested that the hippocampus is dedicated to long-term memory, understanding how memory is anatomically encoded within the hippocampal neuronal network is still lacking. Previously our laboratory showed that hippocampal pyramidal cells are organized in cell clusters to encode both spatial and episodic memory. Based on these findings, we hypothesized that "cluster-type" is a functional organization principal in the hippocampus to encode all types of memory. Here, we tested whether contextual fear, another hippocampus-dependent memory, is also organized in cell clusters. We further investigated the possibility that post-learning sleep may affect functional organization. Cluster formation was examined by assessing the topographic localization of active cells using immediate early gene (IEG, Zif268) imaging methods. The first experiment provides evidence of a cluster-type organization in the hippocampus for fear memory by showing a spatial distribution of adjacent Zif268 positive cells. Exposure to the context itself, without electric shocks, induced a similar cellular formation; however, the degree of clustering was significantly lower. The second experiment provides evidence that sleep plays a role in the refinement and long-term stability of the clusters. The present results confirm the existence of a cluster-type topographic functional neuronal organization in the hippocampus for memory, and further suggest that post-learning sleep enhances the cluster-type organization.

Preconfigured dynamics in the hippocampus are guided by embryonic birthdate and rate of neurogenesis

The incorporation of new information into the hippocampal network is likely to be constrained by its innate architecture and internally generated activity patterns. However, the origin, organization and consequences of such patterns remain poorly understood. In the present study we show that hippocampal network dynamics are affected by sequential neurogenesis. We birthdated CA1 pyramidal neurons with in utero electroporation over 4 embryonic days, encompassing the peak of hippocampal neurogenesis, and compared their functional features in freely moving adult mice. Neurons of the same birthdate displayed distinct connectivity, coactivity across brain states and assembly dynamics. Same-birthdate neurons exhibited overlapping spatial representations, which were maintained across different environments. Overall, the wiring and functional features of CA1 pyramidal neurons reflected a combination of birthdate and the rate of neurogenesis. These observations demonstrate that sequential neurogenesis during embryonic development shapes the preconfigured forms of adult network dynamics.

Signatures of rapid plasticity in hippocampal CA1 representations during novel experiences

Neurons in the hippocampus exhibit a striking selectivity for specific combinations of sensory features, forming representations that are thought to subserve episodic memory. Even during completely novel experiences, hippocampal "place cells" are rapidly configured such that the population sparsely encodes visited locations, stabilizing within minutes of the first exposure to a new environment. What mechanisms enable this fast encoding of experience? Using virtual reality and neural population recordings in mice, we dissected the effects of novelty and experience on the dynamics of place field formation. During place field formation, many CA1 neurons immediately modulated the amplitude of their activity and shifted the location of their field, rapid changes in tuning predicted by behavioral timescale synaptic plasticity (BTSP). Signatures of BTSP were particularly enriched during the exploration of a novel context and decayed with experience. Our data suggest that novelty modulates the effective learning rate in CA1, favoring rapid mechanisms of field formation to encode a new experience.

Assessing Local and Branch-specific Activity in Dendrites

Dendrites are elaborate neural processes which integrate inputs from various sources in space and time. While decades of work have suggested an independent role for dendrites in driving nonlinear computations for the cell, only recently have technological advances enabled us to capture the variety of activity in dendrites and their coupling dynamics with the soma. Under certain circumstances, activity generated in a given dendritic branch remains isolated, such that the soma or even sister dendrites are not privy to these localized signals. Such branch-specific activity could radically increase the capacity and flexibility of coding for the cell as a whole. Here, we discuss these forms of localized and branch-specific activity, their functional relevance in plasticity and behavior, and their supporting biophysical and circuit-level mechanisms. We conclude by showcasing electrical and optical approaches in hippocampal area CA3, using original experimental data to discuss experimental and analytical methodology and key considerations to take when investigating the functional relevance of independent dendritic activity.

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

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

Topographic connectivity and cellular profiling reveal detailed input pathways and functionally distinct cell types in the subthalamic nucleus

The subthalamic nucleus (STN) controls psychomotor activity and is an efficient therapeutic deep brain stimulation target in individuals with Parkinson's disease. Despite evidence indicating position-dependent therapeutic effects and distinct functions within the STN, the input circuit and cellular profile in the STN remain largely unclear. Using neuroanatomical techniques, we construct a comprehensive connectivity map of the indirect and hyperdirect pathways in the mouse STN. Our circuit- and cellular-level connectivities reveal a topographically graded organization with three types of indirect and hyperdirect pathways (external globus pallidus only, STN only, and collateral). We confirm consistent pathways into the human STN by 7 T MRI-based tractography. We identify two functional types of topographically distinct glutamatergic STN neurons (parvalbumin [PV+/-]) with synaptic connectivity from indirect and hyperdirect pathways. Glutamatergic PV+ STN neurons contribute to burst firing. These data suggest a complex interplay of information integration within the basal ganglia underlying coordinated movement control and therapeutic effects.

Real-time tracking of bioluminescent influenza A virus infection in mice

Despite the availability of vaccines and antiviral therapies, seasonal influenza infections cause 400,000 human deaths on average per year. Low vaccine coverage and the occurrence of drug-resistant viral strains highlight the need for new and improved countermeasures. While influenza A virus (IAV) engineered to express a reporter gene may serve as a valuable tool for real-time tracking of viral infection, reporter gene insertion into IAV typically attenuates viral pathogenicity, hindering its application to research. Here, we demonstrate that lethal or even sublethal doses of bioluminescent IAV carrying the NanoLuc gene in the C-terminus of PB2 can be tracked in real-time in live mice without compromising pathogenicity. Real-time tracking of this bioluminescent IAV enables spatiotemporal viral replication tracking in animals that will facilitate the development of countermeasures by enhancing the interpretation of clinical signs and prognosis while also allowing less animal usage.

High-frequency oscillations and sequence generation in two-population models of hippocampal region CA1

Hippocampal sharp wave/ripple oscillations are a prominent pattern of collective activity, which consists of a strong overall increase of activity with superimposed (140 − 200 Hz) ripple oscillations. Despite its prominence and its experimentally demonstrated importance for memory consolidation, the mechanisms underlying its generation are to date not understood. Several models assume that recurrent networks of inhibitory cells alone can explain the generation and main characteristics of the ripple oscillations. Recent experiments, however, indicate that in addition to inhibitory basket cells, the pattern requires in vivo the activity of the local population of excitatory pyramidal cells. Here, we study a model for networks in the hippocampal region CA1 incorporating such a local excitatory population of pyramidal neurons. We start by investigating its ability to generate ripple oscillations using extensive simulations. Using biologically plausible parameters, we find that short pulses of external excitation triggering excitatory cell spiking are required for sharp/wave ripple generation with oscillation patterns similar to in vivo observations. Our model has plausible values for single neuron, synapse and connectivity parameters, random connectivity and no strong feedforward drive to the inhibitory population. Specifically, whereas temporally broad excitation can lead to high-frequency oscillations in the ripple range, sparse pyramidal cell activity is only obtained with pulse-like external CA3 excitation. Further simulations indicate that such short pulses could originate from dendritic spikes in the apical or basal dendrites of CA1 pyramidal cells, which are triggered by coincident spike arrivals from hippocampal region CA3. Finally we show that replay of sequences by pyramidal neurons and ripple oscillations can arise intrinsically in CA1 due to structured connectivity that gives rise to alternating excitatory pulse and inhibitory gap coding; the latter denotes phases of silence in specific basket cell groups, which induce selective disinhibition of groups of pyramidal neurons. This general mechanism for sequence generation leads to sparse pyramidal cell and dense basket cell spiking, does not rely on synfire chain-like feedforward excitation and may be relevant for other brain regions as well.

VTA-projecting cerebellar neurons mediate stress-dependent depression-like behaviors

Although cerebellar alterations have been implicated in stress symptoms, the exact contribution of the cerebellum to stress symptoms remains to be elucidated. Here, we demonstrated the crucial role of cerebellar neurons projecting to the ventral tegmental area (VTA) in the development of chronic stress-induced behavioral alterations in mice. Chronic chemogenetic activation of inhibitory Purkinje cells in crus I suppressed c-Fos expression in the DN and an increase in immobility in the tail suspension test or forced swimming test, which were triggered by chronic stress application. The combination of adeno-associated virus-based circuit mapping and electrophysiological recording identified network connections from crus I to the VTA via the dentate nucleus (DN) of the deep cerebellar nuclei. Furthermore, chronic inhibition of specific neurons in the DN that project to the VTA prevented stressed mice from showing such depression-like behavior, whereas chronic activation of these neurons alone triggered behavioral changes that were comparable with the depression-like behaviors triggered by chronic stress application. Our results indicate that the VTA-projecting cerebellar neurons proactively regulate the development of depression-like behavior, raising the possibility that cerebellum may be an effective target for the prevention of depressive disorders in human.

Control of parallel hippocampal output pathways by amygdalar long-range inhibition

Projections from the basal amygdala (BA) to the ventral hippocampus (vH) are proposed to provide information about the rewarding or threatening nature of learned associations to support appropriate goal-directed and anxiety-like behaviour. Such behaviour occurs via the differential activity of multiple, parallel populations of pyramidal neurons in vH that project to distinct downstream targets, but the nature of BA input and how it connects with these populations is unclear. Using channelrhodopsin-2-assisted circuit mapping in mice, we show that BA input to vH consists of both excitatory and inhibitory projections. Excitatory input specifically targets BA- and nucleus accumbens-projecting vH neurons and avoids prefrontal cortex-projecting vH neurons, while inhibitory input preferentially targets BA-projecting neurons. Through this specific connectivity, BA inhibitory projections gate place-value associations by controlling the activity of nucleus accumbens-projecting vH neurons. Our results define a parallel excitatory and inhibitory projection from BA to vH that can support goal-directed behaviour.

Hippocampal and thalamic afferents form distinct synaptic microcircuits in the mouse infralimbic frontal cortex

The selection of goal-directed behaviors is supported by neural circuits located within the frontal cortex. Frontal cortical afferents arise from multiple brain areas, yet the cell-type-specific targeting of these inputs is unclear. Here, we use monosynaptic retrograde rabies mapping to examine the distribution of afferent neurons targeting distinct classes of local inhibitory interneurons and excitatory projection neurons in mouse infralimbic frontal cortex. Interneurons expressing parvalbumin, somatostatin, or vasoactive intestinal peptide receive a large proportion of inputs from the hippocampus, while interneurons expressing neuron-derived neurotrophic factor receive a large proportion of inputs from thalamic regions. A similar dichotomy is present among the four different excitatory projection neurons. These results show a prominent bias among long-range hippocampal and thalamic afferent systems in their targeting to specific sets of frontal cortical neurons. Moreover, they suggest the presence of two distinct local microcircuits that control how different inputs govern frontal cortical information processing.

Gephyrin-Lacking PV Synapses on Neocortical Pyramidal Neurons

Gephyrin has long been thought of as a master regulator for inhibitory synapses, acting as a scaffold to organize γ-aminobutyric acid type A receptors (GABA_ARs) at the post-synaptic density. Accordingly, gephyrin immunostaining has been used as an indicator of inhibitory synapses; despite this, the pan-synaptic localization of gephyrin to specific classes of inhibitory synapses has not been demonstrated. Genetically encoded fibronectin intrabodies generated with mRNA display (FingRs) against gephyrin (Gephyrin.FingR) reliably label endogenous gephyrin, and can be tagged with fluorophores for comprehensive synaptic quantitation and monitoring. Here we investigated input- and target-specific localization of gephyrin at a defined class of inhibitory synapse, using Gephyrin.FingR proteins tagged with EGFP in brain tissue from transgenic mice. Parvalbumin-expressing (PV) neuron presynaptic boutons labeled using Cre- dependent synaptophysin-tdTomato were aligned with postsynaptic Gephyrin.FingR puncta. We discovered that more than one-third of PV boutons adjacent to neocortical pyramidal (Pyr) cell somas lack postsynaptic gephyrin labeling. This finding was confirmed using correlative fluorescence and electron microscopy. Our findings suggest some inhibitory synapses may lack gephyrin. Gephyrin-lacking synapses may play an important role in dynamically regulating cell activity under different physiological conditions.

Computation, wiring, and plasticity in synaptic clusters

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

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

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

Interstitial Axon Collaterals of Callosal Neurons Form Association Projections from the Primary Somatosensory to Motor Cortex in Mice

Association projections from cortical pyramidal neurons connect disparate intrahemispheric cortical areas, which are implicated in higher cortical functions. The underlying developmental processes of these association projections, especially the initial phase before reaching the target areas, remain unknown. To visualize developing axons of individual neurons with association projections in the mouse neocortex, we devised a sparse labeling method that combined in utero electroporation and confocal imaging of flattened and optically cleared cortices. Using the promoter of an established callosal neuron marker gene that was expressed in over 80% of L2/3 neurons in the primary somatosensory cortex (S1) that project to the primary motor cortex (M1), we found that an association projection of a single neuron was the longest among the interstitial collaterals that branched out in L5 from the earlier-extended callosal projection. Collaterals to M1 elongated primarily within the cortical gray matter with little branching before reaching the target. Our results suggest that dual-projection neurons in S1 make a significant fraction of the association projections to M1, supporting the directed guidance mechanism in long-range corticocortical circuit formation over random projections followed by specific pruning.

Convergence Circuit Mapping: Genetic Approaches From Structure to Function

Understanding the complex neural circuits that underpin brain function and behavior has been a long-standing goal of neuroscience. Yet this is no small feat considering the interconnectedness of neurons and other cell types, both within and across brain regions. In this review, we describe recent advances in mouse molecular genetic engineering that can be used to integrate information on brain activity and structure at regional, cellular, and subcellular levels. The convergence of structural inputs can be mapped throughout the brain in a cell type-specific manner by antero- and retrograde viral systems expressing various fluorescent proteins and genetic switches. Furthermore, neural activity can be manipulated using opto- and chemo-genetic tools to interrogate the functional significance of this input convergence. Monitoring neuronal activity is obtained with precise spatiotemporal resolution using genetically encoded sensors for calcium changes and specific neurotransmitters. Combining these genetically engineered mapping tools is a compelling approach for unraveling the structural and functional brain architecture of complex behaviors and malfunctioned states of neurological disorders.

The functional organization of excitatory synaptic input to place cells

Hippocampal place cells contribute to mammalian spatial navigation and memory formation. Numerous models have been proposed to explain the location-specific firing of this cognitive representation, but the pattern of excitatory synaptic input leading to place firing is unknown, leaving no synaptic-scale explanation of place coding. Here we used resonant scanning two-photon microscopy to establish the pattern of synaptic glutamate input received by CA1 place cells in behaving mice. During traversals of the somatic place field, we found increased excitatory dendritic input, mainly arising from inputs with spatial tuning overlapping the somatic field, and functional clustering of this input along the dendrites over ~10 µm. These results implicate increases in total excitatory input and co-activation of anatomically clustered synaptic input in place firing. Since they largely inherit their fields from upstream synaptic partners with similar fields, many CA1 place cells appear to be part of multi-brain-region cell assemblies forming representations of specific locations.

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

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

The Quest for the Hippocampal Memory Engram: From Theories to Experimental Evidence

More than a century after Richard Semon's theoretical proposal of the memory engram, technological advancements have finally enabled experimental access to engram cells and their functional contents. In this review, we summarize theories and their experimental support regarding hippocampal memory engram formation and function. Specifically, we discuss recent advances in the engram field which help to reconcile two main theories for how the hippocampus supports memory formation: The Memory Indexing and Cognitive Map theories. We also highlight the latest evidence for engram allocation mechanisms through which memories can be linked or separately encoded. Finally, we identify unanswered questions for future investigations, through which a more comprehensive understanding of memory formation and retrieval may be achieved.

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

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

Adeno-Associated Virus-Mediated Single-Cell Labeling of Mitral Cells in the Mouse Olfactory Bulb: Insights into the Developmental Dynamics of Dendrite Remodeling

Neurons typically remodel axons/dendrites for functional refinement of neural circuits in the developing brain. Mitral cells in the mammalian olfactory system remodel their dendritic arbors in the perinatal development, but the underlying molecular and cellular mechanisms remain elusive in part due to a lack of convenient methods to label mitral cells with single-cell resolution. Here we report a novel method for single-cell labeling of mouse mitral cells using adeno-associated virus (AAV)-mediated gene delivery. We first demonstrated that AAV injection into the olfactory ventricle of embryonic day 14.5 (E14.5) mice preferentially labels mitral cells in the olfactory bulb (OB). Birthdate labeling indicated that AAV can transduce mitral cells independently of their birthdates. Furthermore, in combination with the Cre-mediated gene expression system, AAV injection allows visualization of mitral cells at single-cell resolution. Using this AAV-mediated single-cell labeling method, we investigated dendrite development of mitral cells and found that ~50% of mitral cells exhibited mature apical dendrites with a single thick and tufted branch before birth, suggesting that a certain population of mitral cells completes dendrite remodeling during embryonic stages. We also found an atypical subtype of mitral cells that have multiple dendritic shafts innervating the same glomeruli. Our data thus demonstrate that the AAV-mediated labeling method that we reported here provides an efficient way to visualize mitral cells with single-cell resolution and could be utilized to study dynamic aspects as well as functions of mitral cells in the olfactory circuits.

My Neighbour Hetero-deconstructing the mechanisms underlying heterosynaptic plasticity

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

Firing rate homeostasis counteracts changes in stability of recurrent neural networks caused by synapse loss in Alzheimer's disease

The impairment of cognitive function in Alzheimer's disease is clearly correlated to synapse loss. However, the mechanisms underlying this correlation are only poorly understood. Here, we investigate how the loss of excitatory synapses in sparsely connected random networks of spiking excitatory and inhibitory neurons alters their dynamical characteristics. Beyond the effects on the activity statistics, we find that the loss of excitatory synapses on excitatory neurons reduces the network's sensitivity to small perturbations. This decrease in sensitivity can be considered as an indication of a reduction of computational capacity. A full recovery of the network's dynamical characteristics and sensitivity can be achieved by firing rate homeostasis, here implemented by an up-scaling of the remaining excitatory-excitatory synapses. Mean-field analysis reveals that the stability of the linearised network dynamics is, in good approximation, uniquely determined by the firing rate, and thereby explains why firing rate homeostasis preserves not only the firing rate but also the network's sensitivity to small perturbations.

BRAIN Initiative: Cutting-Edge Tools and Resources for the Community

The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance the understanding of healthy and diseased brain circuit function through technological innovation. Core principles for this goal include the validation and dissemination of the myriad innovative technologies, tools, methods, and resources emerging from BRAIN-funded research. Innovators, BRAIN funding agencies, and non-Federal partners are working together to develop strategies for making these products usable, available, and accessible to the scientific community. Here, we describe several early strategies for supporting the dissemination of BRAIN technologies. We aim to invigorate a dialogue with the neuroscience research and funding community, interdisciplinary collaborators, and trainees about the existing and future opportunities for cultivating groundbreaking research products into mature, integrated, and adaptable research systems. Along with the accompanying Society for Neuroscience 2019 Mini-Symposium, "BRAIN Initiative: Cutting-Edge Tools and Resources for the Community," we spotlight the work of several BRAIN investigator teams who are making progress toward providing tools, technologies, and services for the neuroscience community. These tools access neural circuits at multiple levels of analysis, from subcellular composition to brain-wide network connectivity, including the following: integrated systems for EM- and florescence-based connectomics, advances in immunolabeling capabilities, and resources for recording and analyzing functional connectivity. Investigators describe how the resources they provide to the community will contribute to achieving the goals of the NIH BRAIN Initiative. Finally, in addition to celebrating the contributions of these BRAIN-funded investigators, the Mini-Symposium will illustrate the broader diversity of BRAIN Initiative investments in cutting-edge technologies and resources.

Illuminating dendritic function with computational models

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

Potential factors influencing replay across CA1 during sharp-wave ripples

Sharp-wave ripples are complex neurophysiological events recorded along the trisynaptic hippocampal circuit (i.e. from CA3 to CA1 and the subiculum) during slow-wave sleep and awake states. They arise locally but scale brain-wide to the hippocampal target regions at cortical and subcortical structures. During these events, neuronal firing sequences are replayed retrospectively or prospectively and in the forward or reverse order as defined by experience. They could reflect either pre-configured firing sequences, learned sequences or an option space to inform subsequent decisions. How can different sequences arise during sharp-wave ripples? Emerging data suggest the hippocampal circuit is organized in different loops across the proximal (close to dentate gyrus) and distal (close to entorhinal cortex) axis. These data also disclose a so-far neglected laminar organization of the hippocampal output during sharp-wave events. Here, I discuss whether by incorporating cell-type-specific mechanisms converging on deep and superficial CA1 sublayers along the proximodistal axis, some novel factors influencing the organization of hippocampal sequences could be unveiled. This article is part of the Theo Murphy meeting issue 'Memory reactivation: replaying events past, present and future'.

Synaptic Plasticity Forms and Functions

Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long been considered an important component of learning and memory. Computational and engineering work corroborate the power of learning through the directed adjustment of connection weights. Here we review the fundamental elements of four broadly categorized forms of synaptic plasticity and discuss their functional capabilities and limitations. Although standard, correlation-based, Hebbian synaptic plasticity has been the primary focus of neuroscientists for decades, it is inherently limited. Three-factor plasticity rules supplement Hebbian forms with neuromodulation and eligibility traces, while true supervised types go even further by adding objectives and instructive signals. Finally, a recently discovered hippocampal form of synaptic plasticity combines the above elements, while leaving behind the primary Hebbian requirement. We suggest that the effort to determine the neural basis of adaptive behavior could benefit from renewed experimental and theoretical investigation of more powerful directed types of synaptic plasticity.

Synaptic Plasticity Depends on the Fine-Scale Input Pattern in Thin Dendrites of CA1 Pyramidal Neurons

Coordinated long-term plasticity of nearby excitatory synaptic inputs has been proposed to shape experience-related neuronal information processing. To elucidate the induction rules leading to spatially structured forms of synaptic potentiation in dendrites, we explored plasticity of glutamate uncaging-evoked excitatory input patterns with various spatial distributions in perisomatic dendrites of CA1 pyramidal neurons in slices from adult male rats.

Coordinated long-term plasticity of nearby excitatory synaptic inputs has been proposed to shape experience-related neuronal information processing. To elucidate the induction rules leading to spatially structured forms of synaptic potentiation in dendrites, we explored plasticity of glutamate uncaging-evoked excitatory input patterns with various spatial distributions in perisomatic dendrites of CA1 pyramidal neurons in slices from adult male rats. We show that (1) the cooperativity rules governing the induction of synaptic LTP depend on dendritic location; (2) LTP of input patterns that are subthreshold or suprathreshold to evoke local dendritic spikes (d-spikes) requires different spatial organization; and (3) input patterns evoking d-spikes can strengthen nearby, nonsynchronous synapses by local heterosynaptic plasticity crosstalk mediated by NMDAR-dependent MEK/ERK signaling. These results suggest that multiple mechanisms can trigger spatially organized synaptic plasticity on various spatial and temporal scales, enriching the ability of neurons to use synaptic clustering for information processing.

SIGNIFICANCE STATEMENT A fundamental question in neuroscience is how neuronal feature selectivity is established via the combination of dendritic processing of synaptic input patterns with long-term synaptic plasticity. As these processes have been mostly studied separately, the relationship between the rules of integration and rules of plasticity remained elusive. Here we explore how the fine-grained spatial pattern and the form of voltage integration determine plasticity of different excitatory synaptic input patterns in perisomatic dendrites of CA1 pyramidal cells. We demonstrate that the plasticity rules depend highly on three factors: (1) the location of the input within the dendritic branch (proximal vs distal), (2) the strength of the input pattern (subthreshold or suprathreshold for dendritic spikes), and (3) the stimulation of neighboring synapses.