Structured Synaptic Connectivity between Hippocampal Regions

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.

Locally sequential synaptic reactivation during hippocampal ripples

The sequential reactivation of memory-relevant neuronal ensembles during hippocampal sharp-wave (SW) ripple oscillations reflects cognitive processing. However, how a downstream neuron decodes this spatiotemporally organized activity remains unexplored. Using subcellular calcium imaging from CA1 pyramidal neurons in ex vivo hippocampal networks, we discovered that neighboring spines are activated serially along dendrites toward or away from cell bodies. Sequential spine activity was engaged repeatedly in different SWs in a complex manner. In a single SW event, multiple sequences appeared discretely in dendritic trees, but overall, sequences occurred preferentially in some dendritic branches. Thus, sequential replays of multineuronal spikes are distributed across several compartmentalized dendritic foci of a postsynaptic neuron, with their spatiotemporal features preserved.

High-Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality

High-dimensional data and high-dimensional representations of reality are inherent features of modern Artificial Intelligence systems and applications of machine learning. The well-known phenomenon of the “curse of dimensionality” states: many problems become exponentially difficult in high dimensions. Recently, the other side of the coin, the “blessing of dimensionality”, has attracted much attention. It turns out that generic high-dimensional datasets exhibit fairly simple geometric properties. Thus, there is a fundamental tradeoff between complexity and simplicity in high dimensional spaces. Here we present a brief explanatory review of recent ideas, results and hypotheses about the blessing of dimensionality and related simplifying effects relevant to machine learning and neuroscience.

Combining mGRASP and Optogenetics Enables High-Resolution Functional Mapping of Descending Cortical Projections

We have applied optogenetics and mGRASP, a light microscopy technique that labels synaptic contacts, to map the number and strength of defined corticocollicular (CC) connections. Using mGRASP, we show that CC projections form small, medium, and large synapses, and both the number and the distribution of synapse size vary among the IC regions. Using optogenetics, we show that low-frequency stimulation of CC axons expressing channelrhodopsin produces prolonged elevations of the CC miniature EPSC (mEPSC) rate. Functional analysis of CC mEPSCs reveals small-, medium-, and large-amplitude events that mirror the synaptic distributions observed with mGRASP. Our results reveal that descending ipsilateral projections dominate CC feedback via an increased number of large synaptic contacts, especially onto the soma of IC neurons. This study highlights the feasibility of combining microscopy (i.e., mGRASP) and optogenetics to reveal synaptic weighting of defined projections at the level of single neurons, enabling functional connectomic mapping in diverse neural circuits.

•

Optogenetic axonal stimulation causes prolonged increases in quantal synaptic release

•

Quantal and anatomical measures of synapse strength directly correspond

•

Strength and cellular location of cortical inputs to midbrain are region specific

Synaptic Clustering and Memory Formation

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

Single Synapse LTP: A Matter of Context?

The most commonly studied form of synaptic plasticity is long-term potentiation (LTP). Over the last 15 years, it has been possible to induce structural and functional LTP in dendritic spines using two-photon glutamate uncaging, allowing for studying the signaling mechanisms of LTP with single synapse resolution. In this review, we compare different stimulation methods to induce single synapse LTP and discuss how LTP is expressed. We summarize the underlying signaling mechanisms that have been studied with high spatiotemporal resolution. Finally, we discuss how LTP in a single synapse can be affected by excitatory and inhibitory synapses nearby. We argue that single synapse LTP is highly dependent on context: the choice of induction method, the history of the dendritic spine and the dendritic vicinity crucially affect signaling pathways and expression of single synapse LTP.

Tau interactome analyses in CRISPR-Cas9 engineered neuronal cells reveal ATPase-dependent binding of wild-type but not P301L Tau to non-muscle myosins

Protein interactions of Tau are of interest in efforts to decipher pathogenesis in Alzheimer's disease, a subset of frontotemporal dementias, and other tauopathies. We CRISPR-Cas9 edited two human cell lines to generate broadly adaptable models for neurodegeneration research. We applied the system to inducibly express balanced levels of 3-repeat and 4-repeat wild-type or P301L mutant Tau. Following 12-h induction, quantitative mass spectrometry revealed the Parkinson's disease-causing protein DJ-1 and non-muscle myosins as Tau interactors whose binding to Tau was profoundly influenced by the presence or absence of the P301L mutation. The presence of wild-type Tau stabilized non-muscle myosins at higher steady-state levels. Strikingly, in human differentiated co-cultures of neuronal and glial cells, the preferential interaction of non-muscle myosins to wild-type Tau depended on myosin ATPase activity. Consistently, transgenic P301L Tau mice exhibited reduced phosphorylation of regulatory myosin light chains known to activate this ATPase. The direct link of Tau to non-muscle myosins corroborates independently proposed roles of Tau in maintaining dendritic spines and mitochondrial fission biology, two subcellular niches affected early in tauopathies.

Fluorescence-Based Quantitative Synapse Analysis for Cell Type-Specific Connectomics

Anatomical methods for determining cell type-specific connectivity are essential to inspire and constrain our understanding of neural circuit function. We developed genetically-encoded reagents for fluorescence-synapse labeling and connectivity analysis in brain tissue, using a fluorogen-activating protein (FAP)-coupled or YFP-coupled, postsynaptically-localized neuroligin-1 (NL-1) targeting sequence (FAP/YFPpost). FAPpost expression did not alter mEPSC or mIPSC properties. Sparse AAV-mediated expression of FAP/YFPpost with the cell-filling, red fluorophore dTomato (dTom) enabled high-throughput, compartment-specific detection of putative synapses across diverse neuron types in mouse somatosensory cortex. We took advantage of the bright, far-red emission of FAPpost puncta for multichannel fluorescence alignment of dendrites, FAPpost puncta, and presynaptic neurites in transgenic mice with saturated labeling of parvalbumin (PV), somatostatin (SST), or vasoactive intestinal peptide (VIP)-expressing neurons using Cre-reporter driven expression of YFP. Subtype-specific inhibitory connectivity onto layer 2/3 (L2/3) neocortical pyramidal (Pyr) neurons was assessed using automated puncta detection and neurite apposition. Quantitative and compartment-specific comparisons show that PV inputs are the predominant source of inhibition at both the soma and the dendrites and were particularly concentrated at the primary apical dendrite. SST inputs were interleaved with PV inputs at all secondary-order and higher-order dendritic branches. These fluorescence-based synapse labeling reagents can facilitate large-scale and cell-type specific quantitation of changes in synaptic connectivity across development, learning, and disease states.

Can NMDA Spikes Dictate Computations of Local Networks and Behavior?

Intelligence is the ability to learn appropriate responses to stimuli and the capacity to master new skills. Synaptic integration at the dendritic level is thought to be essential for this ability through linear and non-linear processing, by allowing neurons to be tuned to relevant information and to maximize adaptive behavior. Showing that dendrites are able to generate local computations that influence how animals perceive the world, form a new memory or learn a new skill was a break-through in neuroscience, since in the past they were seen as passive elements of the neurons, just funneling information to the soma. Here, we provide an overview of the role of dendritic integration in improving the neuronal network and behavioral performance. We focus on how NMDA spikes are generated and their role in neuronal computation for optimal behavioral output based on recent in vivo studies on rodents.

Revealing the Synaptic Hodology of Mammalian Neural Circuits With Multiscale Neurocartography

The functional features of neural circuits are determined by a combination of properties that range in scale from projections systems across the whole brain to molecular interactions at the synapse. The burgeoning field of neurocartography seeks to map these relevant features of brain structure—spanning a volume ∼20 orders of magnitude—to determine how neural circuits perform computations supporting cognitive function and complex behavior. Recent technological breakthroughs in tissue sample preparation, high-throughput electron microscopy imaging, and automated image analyses have produced the first visualizations of all synaptic connections between neurons of invertebrate model systems. However, the sheer size of the central nervous system in mammals implies that reconstruction of the first full brain maps at synaptic scale may not be feasible for decades. In this review, we outline existing and emerging technologies for neurocartography that complement electron microscopy-based strategies and are beginning to derive some basic organizing principles of circuit hodology at the mesoscale, microscale, and nanoscale. Specifically, we discuss how a host of light microscopy techniques including array tomography have been utilized to determine both long-range and subcellular organizing principles of synaptic connectivity. In addition, we discuss how new techniques, such as two-photon serial tomography of the entire mouse brain, have become attractive approaches to dissect the potential connectivity of defined cell types. Ultimately, principles derived from these techniques promise to facilitate a conceptual understanding of how connectomes, and neurocartography in general, can be effectively utilized toward reaching a mechanistic understanding of circuit function.

Multicontact Co-operativity in Spike-Timing-Dependent Structural Plasticity Stabilizes Networks

Excitatory synaptic connections in the adult neocortex consist of multiple synaptic contacts, almost exclusively formed on dendritic spines. Changes of spine volume, a correlate of synaptic strength, can be tracked in vivo for weeks. Here, we present a combined model of structural and spike-timing–dependent plasticity that explains the multicontact configuration of synapses in adult neocortical networks under steady-state and lesion-induced conditions. Our plasticity rule with Hebbian and anti-Hebbian terms stabilizes both the postsynaptic firing rate and correlations between the pre- and postsynaptic activity at an active synaptic contact. Contacts appear spontaneously at a low rate and disappear if their strength approaches zero. Many presynaptic neurons compete to make strong synaptic connections onto a postsynaptic neuron, whereas the synaptic contacts of a given presynaptic neuron co-operate via postsynaptic firing. We find that co-operation of multiple synaptic contacts is crucial for stable, long-term synaptic memories. In simulations of a simplified network model of barrel cortex, our plasticity rule reproduces whisker-trimming–induced rewiring of thalamocortical and recurrent synaptic connectivity on realistic time scales.

Reduced sensory-evoked structural plasticity in the aging barrel cortex

Impairments in synaptic connectivity have been linked to cognitive deficits in age-related neurodegenerative disorders and healthy aging. However, the anatomical and structural bases of these impairments have not been identified yet. A hallmark of neural plasticity in young adults is short-term synaptic rearrangement, yet aged animals already display higher synaptic turnover rates at the baseline. Using two-photon excitation (2PE) microscopy, we explored if this elevated turnover alters the aged brain's response to plasticity. Following a sensory-evoked plasticity protocol involving whisker stimulation, aged mice display reduced spine dynamics (gain, loss, and turnover), decreased spine clustering, and lower spine stability when compared to young adult mice. These results suggest a deficiency of the cortical neurons of aged mice to structurally incorporate new sensory experiences, in the form of clustered, long-lasting synapses, into already existing cortical circuits. This research provides the first evidence linking experience-dependent plasticity with in vivo spine dynamics in the aged brain and supports a model of both reduced synaptic plasticity and reduced synaptic tenacity in the aged somatosensory system.

Fundamental bounds on learning performance in neural circuits

How does the size of a neural circuit influence its learning performance? Larger brains tend to be found in species with higher cognitive function and learning ability. Intuitively, we expect the learning capacity of a neural circuit to grow with the number of neurons and synapses. We show how adding apparently redundant neurons and connections to a network can make a task more learnable. Consequently, large neural circuits can either devote connectivity to generating complex behaviors or exploit this connectivity to achieve faster and more precise learning of simpler behaviors. However, we show that in a biologically relevant setting where synapses introduce an unavoidable amount of noise, there is an optimal size of network for a given task. Above the optimal network size, the addition of neurons and synaptic connections starts to impede learning performance. This suggests that the size of brain circuits may be constrained by the need to learn efficiently with unreliable synapses and provides a hypothesis for why some neurological learning deficits are associated with hyperconnectivity. Our analysis is independent of specific learning rules and uncovers fundamental relationships between learning rate, task performance, network size, and intrinsic noise in neural circuits.

Quantifying How Staining Methods Bias Measurements of Neuron Morphologies

The process through which neurons are labeled is a key methodological choice in measuring neuron morphology. However, little is known about how this choice may bias measurements. To quantify this bias we compare the extracted morphology of neurons collected from the same rodent species, experimental condition, gender distribution, age distribution, brain region and putative cell type, but obtained with 19 distinct staining methods. We found strong biases on measured features of morphology. These were largest in features related to the coverage of the dendritic tree (e.g., the total dendritic tree length). Understanding measurement biases is crucial for interpreting morphological data.

Lipopolysaccharide-induced neuroinflammation induces presynaptic disruption through a direct action on brain tissue involving microglia-derived interleukin 1 beta

BACKGROUND

Systemic inflammation has been linked to synapse loss and cognitive decline in human patients and animal models. A role for microglial release of pro-inflammatory cytokines has been proposed based on in vivo and primary culture studies. However, mechanisms are hard to study in vivo as specific microglial ablation is challenging and the extracellular fluid cannot be sampled without invasive methods. Primary cultures have different limitations as the intricate multicellular architecture in the brain is not fully reproduced. It is essential to confirm proposed brain-specific mechanisms of inflammatory synapse loss directly in brain tissue. Organotypic hippocampal slice cultures (OHSCs) retain much of the in vivo neuronal architecture, synaptic connections and diversity of cell types whilst providing convenient access to manipulate and sample the culture medium and observe cellular reactions.

METHODS

OHSCs were generated from P6-P9 C57BL/6 mice. Inflammation was induced via addition of lipopolysaccharide (LPS), and cultures were analysed for changes in synaptic proteins, gene expression and protein secretion. Microglia were selectively depleted using clodronate, and the effect of IL1β was assessed using a specific neutralising monoclonal antibody.

RESULTS

LPS treatment induced loss of the presynaptic protein synaptophysin without altering PSD95 or Aβ protein levels. Depletion of microglia prior to LPS application prevented the loss of synaptophysin, whilst microglia depletion after the inflammatory insult was partially effective, although less so than pre-emptive treatment, indicating a time-critical window in which microglia can induce synaptic damage. IL1β protein and mRNA were increased after LPS addition, with these effects also prevented by microglia depletion. Direct application of IL1β to OHSCs resulted in synaptophysin loss whilst pre-treatment with IL1β neutralising antibody prior to LPS addition prevented a significant loss of synaptophysin but may also impact basal synaptic levels.

CONCLUSIONS

The loss of synaptophysin in this system confirms LPS can act directly within brain tissue to disrupt synapses, and we show that microglia are the relevant cellular target when all major CNS cell types are present. By overcoming limitations of primary culture and in vivo work, our study strengthens the evidence for a key role of microglia-derived IL1β in synaptic dysfunction after inflammatory insult.

Sustained rescue of prefrontal circuit dysfunction by antidepressant-induced spine formation

The neurobiological mechanisms underlying the induction and remission of depressive episodes over time are not well understood. Through repeated longitudinal imaging of medial prefrontal microcircuits in the living brain, we found that prefrontal spinogenesis plays a critical role in sustaining specific antidepressant behavioral effects and maintaining long-term behavioral remission. Depression-related behavior was associated with targeted, branch-specific elimination of postsynaptic dendritic spines on prefrontal projection neurons. Antidepressant-dose ketamine reversed these effects by selectively rescuing eliminated spines and restoring coordinated activity in multicellular ensembles that predict motivated escape behavior. Prefrontal spinogenesis was required for the long-term maintenance of antidepressant effects on motivated escape behavior but not for their initial induction.

How Dendrites Affect Online Recognition Memory

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

High-Density 2-μm-Pitch pH Image Sensor With High-Speed Operation up to 1933 fps

Various biosensing platforms for real-time monitoring and mapping of chemical signals in neural networks have been developed based on CMOS process technology. Despite their achievements, however, there remains a demand for an advanced method that can offer detailed insights into cellular functions with higher spatiotemporal resolution. Here, we present a pH image sensor that employs a high-density array of 256 × 256 pixels and readout circuitry designed for fast operation. The sensor's characteristics, such as the pH sensitivity of 55.1 mV/pH and higher frame speed of 1933 fps, are experimentally demonstrated and compared to those of state-of-the-art pH image sensors. Among them, our sensor presents the smallest pitch of 2 μm with a significantly high operation speed. This sensor can successfully detect a pH change, but also transform the measured data to a two-dimensional image series in real time. The practical spatial resolution of images is investigated by an evaluation method that we first propose in this paper. By this method, we confirm that our sensor can discriminate objects distanced over 4 μm apart, which is twice bigger than the pixel pitch. In order to analyze the degraded resolution and image blur, a capacitive coupling effect at an ion-sensitive membrane is suggested as the main factor and demonstrated by simulation.

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

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

Neural Interactome: Interactive Simulation of a Neuronal System

Connectivity and biophysical processes determine the functionality of neuronal networks. We, therefore, developed a real-time framework, called Neural Interactome^,, to simultaneously visualize and interact with the structure and dynamics of such networks. Neural Interactome is a cross-platform framework, which combines graph visualization with the simulation of neural dynamics, or experimentally recorded multi neural time series, to allow application of stimuli to neurons to examine network responses. In addition, Neural Interactome supports structural changes, such as disconnection of neurons from the network (ablation feature). Neural dynamics can be explored on a single neuron level (using a zoom feature), back in time (using a review feature), and recorded (using presets feature). The development of the Neural Interactome was guided by generic concepts to be applicable to neuronal networks with different neural connectivity and dynamics. We implement the framework using a model of the nervous system of Caenorhabditis elegans (C. elegans) nematode, a model organism with resolved connectome and neural dynamics. We show that Neural Interactome assists in studying neural response patterns associated with locomotion and other stimuli. In particular, we demonstrate how stimulation and ablation help in identifying neurons that shape particular dynamics. We examine scenarios that were experimentally studied, such as touch response circuit, and explore new scenarios that did not undergo elaborate experimental studies.

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

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

Ephrin-B3 controls excitatory synapse density through cell-cell competition for EphBs

Cortical networks are characterized by sparse connectivity, with synapses found at only a subset of axo-dendritic contacts. Yet within these networks, neurons can exhibit high connection probabilities, suggesting that cell-intrinsic factors, not proximity, determine connectivity. Here, we identify ephrin-B3 (eB3) as a factor that determines synapse density by mediating a cell-cell competition that requires ephrin-B-EphB signaling. In a microisland culture system designed to isolate cell-cell competition, we find that eB3 determines winning and losing neurons in a contest for synapses. In a Mosaic Analysis with Double Markers (MADM) genetic mouse model system in vivo the relative levels of eB3 control spine density in layer 5 and 6 neurons. MADM cortical neurons in vitro reveal that eB3 controls synapse density independently of action potential-driven activity. Our findings illustrate a new class of competitive mechanism mediated by trans-synaptic organizing proteins which control the number of synapses neurons receive relative to neighboring neurons.

Functional Synaptic Architecture of Callosal Inputs in Mouse Primary Visual Cortex

Callosal projections are thought to play a critical role in coordinating neural activity between the cerebral hemispheres in placental mammals, but the rules that govern the arrangement of callosal synapses on the dendrites of their target neurons remain poorly understood. Here we describe a high-throughput method to map the functional organization of callosal connectivity by combining in vivo 3D random-access two-photon calcium imaging of the dendritic spines of single V1 neurons with optogenetic stimulation of the presynaptic neural population in the contralateral hemisphere. We find that callosal-recipient spines are more likely to cluster with non-callosal-recipient spines with similar orientation preference. These observations, based on optogenetic stimulation, were confirmed by direct anatomical visualization of callosal synaptic connections using post hoc expansion microscopy. Our results demonstrate, for the first time, that functional synaptic clustering in a short dendritic segment could play a role in integrating distinct neuronal circuits.

Mapping of excitatory and inhibitory postsynaptic potentials of neuronal populations in hippocampal slices using the GEVI, ArcLight

To understand the circuitry of the brain, it is essential to clarify the functional connectivity among distinct neuronal populations. For this purpose, neuronal activity imaging using genetically-encoded calcium sensors such as GCaMP has been a powerful approach due to its cell-type specificity. However, calcium (Ca²⁺) is an indirect measure of neuronal activity. A more direct approach would be to use genetically encoded voltage indicators (GEVIs) to observe subthreshold, synaptic activities. The GEVI, ArcLight, which exhibits large fluorescence transients in response to voltage, was expressed in excitatory neurons of the mouse CA1 hippocampus. Fluorescent signals in response to the electrical stimulation of the Schaffer collateral axons were observed in brain slice preparations. ArcLight was able to map both excitatory and inhibitory inputs projected to excitatory neurons. In contrast, the Ca²⁺ signal detected by GCaMP6f, was only associated with excitatory inputs. ArcLight and similar voltage sensing probes are also becoming powerful paradigms for functional connectivity mapping of brain circuitry.

The Circuit Motif as a Conceptual Tool for Multilevel Neuroscience

Modern neuroscientific techniques that specifically manipulate and measure neuronal activity in behaving animals now allow bridging of the gap from the cellular to the behavioral level. However, in doing so, they also pose new challenges. Research using incompletely defined manipulations in a high-dimensional space without clear hypotheses is likely to suffer from multiple well-known conceptual and statistical problems. In this context it is essential to develop hypotheses with testable implications across levels. Here we propose that a focus on circuit motifs can help achieve this goal. Viewing neural structures as an assembly of circuit motif building blocks is not new. However, recent tool advances have made it possible to extensively map, specifically manipulate, and quantitatively investigate circuit motifs and thereby reexamine their relevance to brain function.

Synaptic topography - Converging connections and emerging function

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

Two-Photon Optogenetic Mapping of Excitatory Synaptic Connectivity and Strength

The development of optical methods to activate neurons with single-cell resolution has enabled systematic mapping of inhibitory connections. In contrast, optical mapping of excitatory connections between pyramidal neurons (PCs) has been a major challenge due to their high densities in cortical tissue and their weak and stochastic connectivity. Here we present an optogenetic two-photon mapping method in mouse neocortical slices by activating PCs with the red-shifted opsin C1V1 while recording postsynaptic responses in whole-cell configuration. Comparison of delays from triggered action potentials (APs) with those from synaptic inputs allowed us to predict connected PCs in three dimensions. We confirmed these predictions with paired recordings, and used this method to map strong connections among large populations of layer 2/3 PCs. Our method can be used for fast, systematic mapping of synaptic connectivity and weights.

•

Two-photon optogenetic mapping of excitatory connectivity and strength in neocortex

•

Identification of connected neurons in acute slices through numerical optimization

•

Synaptic delays align with location of connected presynaptic cell

•

Confirmation of predicted connections by dual patch-clamp recordings

Spatially dispersed synapses yield sharply-tuned place cell responses through dendritic spike initiation

KEY POINTS

The generation of dendritic spikes and the consequent sharp tuning of neuronal responses are together attainable even when iso-feature synapses are randomly dispersed across the dendritic arbor. Disparate combinations of channel conductances with distinct configurations of randomly dispersed place field synapses concomitantly yield similar sharp tuning profiles and similar functional maps of several intrinsic properties. Targeted synaptic plasticity converts silent cells to place cells for specific place fields in models with disparate channel combinations that receive dispersed synaptic inputs from multiple place field locations. Dispersed localization of iso-feature synapses is a strong candidate for achieving sharp feature selectivity in neurons across sensory-perceptual systems, with several degrees of freedom in relation to synaptic locations. Quantitative evidence for the possibility that degeneracy (i.e. the ability of disparate structural components to yield similar functional outcomes) could act as a broad framework that effectively accomplishes the twin goals of input-feature encoding and homeostasis of intrinsic properties without cross interferences.

ABSTRACT

A prominent hypothesis spanning several sensory-perceptual systems implicates spatially clustered synapses in the generation of dendritic spikes that mediate sharply-tuned neuronal responses to input features. In this conductance-based morphologically-precise computational study, we tested this hypothesis by systematically analysing the impact of distinct synaptic and channel localization profiles on sharpness of spatial tuning in hippocampal pyramidal neurons. We found that the generation of dendritic spikes, the emergence of an excitatory ramp in somatic voltage responses, the expression of several intrinsic somatodendritic functional maps and sharp tuning of place-cell responses were all attainable even when iso-feature synapses are randomly dispersed across the dendritic arbor of models with disparate channel combinations. Strikingly, the generation and propagation of dendritic spikes, reliant on dendritic sodium channels and N-methyl-d-asparate receptors, mediated the sharpness of spatial tuning achieved with dispersed synaptic localization. To ensure that our results were not artefacts of narrow parametric choices, we confirmed these conclusions with independent multiparametric stochastic search algorithms spanning thousands of unique models for each synaptic localization scenario. Next, employing virtual knockout models, we demonstrated a vital role for dendritically expressed voltage-gated ion channels, especially the transient potassium channels, in maintaining sharpness of place-cell tuning. Importantly, we established that synaptic potentiation targeted to afferents from one specific place field was sufficient to impart place field selectivity even when intrinsically disparate neurons received randomly dispersed afferents from multiple place field locations. Our results provide quantitative evidence for disparate combinations of channel and synaptic localization profiles to concomitantly yield similar tuning and similar intrinsic properties.

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.

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

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

Schaffer Collateral Inputs to CA1 Excitatory and Inhibitory Neurons Follow Different Connectivity Rules

Neural circuits, governed by a complex interplay between excitatory and inhibitory neurons, are the substrate for information processing, and the organization of synaptic connectivity in neural network is an important determinant of circuit function. Here, we analyzed the fine structure of connectivity in hippocampal CA1 excitatory and inhibitory neurons innervated by Schaffer collaterals (SCs) using mGRASP in male mice. Our previous study revealed spatially structured synaptic connectivity between CA3 and CA1 pyramidal cells (PCs). Surprisingly, parvalbumin-positive interneurons (PVs) showed a significantly more random pattern spatial structure. Notably, application of Peters' rule for synapse prediction by random overlap between axons and dendrites enhanced structured connectivity in PCs, but, by contrast, made the connectivity pattern in PVs more random. In addition, PCs in a deep sublayer of striatum pyramidale appeared more highly structured than PCs in superficial layers, and little or no sublayer specificity was found in PVs. Our results show that CA1 excitatory PCs and inhibitory PVs innervated by the same SC inputs follow different connectivity rules. The different organizations of fine scale structured connectivity in hippocampal excitatory and inhibitory neurons provide important insights into the development and functions of neural networks.SIGNIFICANCE STATEMENT Understanding how neural circuits generate behavior is one of the central goals of neuroscience. An important component of this endeavor is the mapping of fine-scale connection patterns that underlie, and help us infer, signal processing in the brain. Here, using our recently developed synapse detection technology (mGRASP and neuTube), we provide detailed profiles of synaptic connectivity in excitatory (CA1 pyramidal) and inhibitory (CA1 parvalbumin-positive) neurons innervated by the same presynaptic inputs (CA3 Schaffer collaterals). Our results reveal that these two types of CA1 neurons follow different connectivity patterns. Our new evidence for differently structured connectivity at a fine scale in hippocampal excitatory and inhibitory neurons provides a better understanding of hippocampal networks and will guide theoretical and experimental studies.

Biophysics of object segmentation in a collision-detecting neuron

Collision avoidance is critical for survival, including in humans, and many species possess visual neurons exquisitely sensitive to objects approaching on a collision course. Here, we demonstrate that a collision-detecting neuron can detect the spatial coherence of a simulated impending object, thereby carrying out a computation akin to object segmentation critical for proper escape behavior. At the cellular level, object segmentation relies on a precise selection of the spatiotemporal pattern of synaptic inputs by dendritic membrane potential-activated channels. One channel type linked to dendritic computations in many neural systems, the hyperpolarization-activated cation channel, HCN, plays a central role in this computation. Pharmacological block of HCN channels abolishes the neuron's spatial selectivity and impairs the generation of visually guided escape behaviors, making it directly relevant to survival. Additionally, our results suggest that the interaction of HCN and inactivating K⁺ channels within active dendrites produces neuronal and behavioral object specificity by discriminating between complex spatiotemporal synaptic activation patterns.

CA1 pyramidal cell diversity enabling parallel information processing in the hippocampus

Hippocampal network operations supporting spatial navigation and declarative memory are traditionally interpreted in a framework where each hippocampal area, such as the dentate gyrus, CA3, and CA1, consists of homogeneous populations of functionally equivalent principal neurons. However, heterogeneity within hippocampal principal cell populations, in particular within pyramidal cells at the main CA1 output node, is increasingly recognized and includes developmental, molecular, anatomical, and functional differences. Here we review recent progress in the delineation of hippocampal principal cell subpopulations by focusing on radially defined subpopulations of CA1 pyramidal cells, and we consider how functional segregation of information streams, in parallel channels with nonuniform properties, could represent a general organizational principle of the hippocampus supporting diverse behaviors.

Rewiring the connectome: Evidence and effects

•

Rewiring is a plasticity mechanism that alters connectivity between neurons.

•

Evidence for rewiring has been difficult to obtain.

•

New evidence indicates that local circuitry is rewired during learning.

•

Harnessing rewiring offers new ways to treat psychiatric and neurological diseases.

Neuronal connections form the physical basis for communication in the brain. Recently, there has been much interest in mapping the “connectome” to understand how brain structure gives rise to brain function, and ultimately, to behaviour. These attempts to map the connectome have largely assumed that connections are stable once formed. Recent studies, however, indicate that connections in mammalian brains may undergo rewiring during learning and experience-dependent plasticity. This suggests that the connectome is more dynamic than previously thought. To what extent can neural circuitry be rewired in the healthy adult brain? The connectome has been subdivided into multiple levels of scale, from synapses and microcircuits through to long-range tracts. Here, we examine the evidence for rewiring at each level. We then consider the role played by rewiring during learning. We conclude that harnessing rewiring offers new avenues to treat brain diseases.

Single excitatory axons form clustered synapses onto CA1 pyramidal cell dendrites

CA1 pyramidal neurons are a major output of the hippocampus and encode features of experience that constitute episodic memories. Feature-selective firing of these neurons results from the dendritic integration of inputs from multiple brain regions. While it is known that synchronous activation of spatially clustered inputs can contribute to firing through the generation of dendritic spikes, there is no established mechanism for spatiotemporal synaptic clustering. Here we show that single presynaptic axons form multiple, spatially clustered inputs onto the distal, but not proximal, dendrites of CA1 pyramidal neurons. These compound connections exhibit ultrastructural features indicative of strong synapses and occur much more commonly in entorhinal than in thalamic afferents. Computational simulations revealed that compound connections depolarize dendrites in a biophysically efficient manner, owing to their inherent spatiotemporal clustering. Our results suggest that distinct afferent projections use different connectivity motifs that differentially contribute to dendritic integration.

Effects of location and extent of spine clustering on synaptic integration in striatal medium spiny neurons-a computational study

The nucleus accumbens (NAc) is known widely for its role in the reward circuit, which is dysregulated in a number of psychological disorders. Recent evidence also suggests the contribution of this structure in spatial and gustatory memories. Because of its role in different types of memories, similar to the hippocampus, we assumed the formation of spine clusters, which are engrams of memory, to be present on dendrites of medium spiny neurons (MSNs). We found that the activation of clustered inputs resulted in sublinear summation when clusters were present on the same branch and also when inputs were distributed on different branches. The size, as well as the location of clusters, was found to affect the summation. With an increase in cluster size and distance from soma, the summation was increasingly sublinear. When the temporal integration window was measured for clustered spines, it was found to be narrower as compared to that for a single spine. Also, distally located clusters resulted in a wider temporal window, as compared to proximal clusters. Our results suggest that depending on the location of clusters, the modes of integration will differ in MSNs possessing clustered spines.

Synaptic integrative mechanisms for spatial cognition

Synaptic integrative mechanisms have profound effects on electrical signaling in the brain that, although largely hidden from recording methods that observe the spiking activity of neurons, may be critical for the encoding, storage and retrieval of information. Here we review roles for synaptic integrative mechanisms in the selection, generation and plasticity of place and grid fields, and in related temporal codes for the representation of space. We outline outstanding questions and challenges in the testing of hypothesized models for spatial computation and memory.

Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level

Synaptic plasticity is thought to be the principal neuronal mechanism underlying learning. Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the local non-linear processing of synaptic inputs allowed for by dendrites. Furthermore, experimental evidence suggests that STDP is not the only learning rule available to neurons. By implementing biophysically realistic neuron models, we study how dendrites enable multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compare the conditions for STDP and for synaptic strengthening by local dendritic spikes. We also explore how the connectivity between two cells is affected by these plasticity rules and by different synaptic distributions. Finally, we show that how memory retention during associative learning can be prolonged in networks of neurons by including dendrites.

Synaptic plasticity is the neuronal mechanism underlying learning. Here the authors construct biophysical models of pyramidal neurons that reproduce observed plasticity gradients along the dendrite and show that dendritic spike dependent LTP which is predominant in distal sections can prolong memory retention.

Spatial patterning of excitatory and inhibitory neuropil territories during spinal circuit development

To generate rhythmic motor behaviors, both single neurons and neural circuits require a balance between excitatory inputs that trigger action potentials and inhibitory inputs that promote a stable resting potential (E/I balance). Previous studies have focused on individual neurons and have shown that, over a short spatial scale, excitatory and inhibitory (E/I) synapses tend to form structured territories with inhibitory inputs enriched on cell bodies and proximal dendrites and excitatory inputs on distal dendrites. However, systems-level E/I patterns, at spatial scales larger than single neurons, are largely uncharted. We used immunostaining for PSD-95 and gephyrin postsynaptic scaffolding proteins as proxies for excitatory and inhibitory synapses, respectively, to quantify the numbers and map the distributions of E/I synapses in zebrafish spinal cord at both an embryonic stage and a larval stage. At the embryonic stage, we found that PSD-95 puncta outnumber gephyrin puncta, with the number of gephyrin puncta increasing to match that of PSD-95 puncta at the larval stage. At both stages, PSD-95 puncta are enriched in the most lateral neuropil corresponding to distal dendrites while gephyrin puncta are enriched on neuronal somata and in the medial neuropil. Significantly, similar to synaptic puncta, neuronal processes also exhibit medial-lateral territories at both developmental stages with enrichment of glutamatergic (excitatory) processes laterally and glycinergic (inhibitory) processes medially. This establishment of neuropil excitatory-inhibitory structure largely precedes dendritic arborization of primary motor neurons, suggesting that the structured neuropil could provide a framework for the development of E/I balance at the cellular level. J. Comp. Neurol. 525:1649-1667, 2017. © 2016 Wiley Periodicals, Inc.

Modelling plasticity in dendrites: from single cells to networks

One of the key questions in neuroscience is how our brain self-organises to efficiently process information. To answer this question, we need to understand the underlying mechanisms of plasticity and their role in shaping synaptic connectivity. Theoretical neuroscience typically investigates plasticity on the level of neural networks. Neural network models often consist of point neurons, completely neglecting neuronal morphology for reasons of simplicity. However, during the past decades it became increasingly clear that inputs are locally processed in the dendrites before they reach the cell body. Dendritic properties enable local interactions between synapses and location-dependent modulations of inputs, rendering the position of synapses on dendrites highly important. These insights changed our view of neurons, such that we now think of them as small networks of nearly independent subunits instead of a simple point. Here, we propose that understanding how the brain processes information strongly requires that we consider the following properties: which plasticity mechanisms are present in the dendrites and how do they enable the self-organisation of synapses across the dendritic tree for efficient information processing? Ultimately, dendritic plasticity mechanisms can be studied in networks of neurons with dendrites, possibly uncovering unknown mechanisms that shape the connectivity in our brains.

Mechanisms for Selective Single-Cell Reactivation during Offline Sharp-Wave Ripples and Their Distortion by Fast Ripples

Memory traces are reactivated selectively during sharp-wave ripples. The mechanisms of selective reactivation, and how degraded reactivation affects memory, are poorly understood. We evaluated hippocampal single-cell activity during physiological and pathological sharp-wave ripples using juxtacellular and intracellular recordings in normal and epileptic rats with different memory abilities. CA1 pyramidal cells participate selectively during physiological events but fired together during epileptic fast ripples. We found that firing selectivity was dominated by an event- and cell-specific synaptic drive, modulated in single cells by changes in the excitatory/inhibitory ratio measured intracellularly. This mechanism collapses during pathological fast ripples to exacerbate and randomize neuronal firing. Acute administration of a use- and cell-type-dependent sodium channel blocker reduced neuronal collapse and randomness and improved recall in epileptic rats. We propose that cell-specific synaptic inputs govern firing selectivity of CA1 pyramidal cells during sharp-wave ripples.

Dendrites Enable a Robust Mechanism for Neuronal Stimulus Selectivity

Hearing, vision, touch: underlying all of these senses is stimulus selectivity, a robust information processing operation in which cortical neurons respond more to some stimuli than to others. Previous models assume that these neurons receive the highest weighted input from an ensemble encoding the preferred stimulus, but dendrites enable other possibilities. Nonlinear dendritic processing can produce stimulus selectivity based on the spatial distribution of synapses, even if the total preferred stimulus weight does not exceed that of nonpreferred stimuli. Using a multi-subunit nonlinear model, we demonstrate that stimulus selectivity can arise from the spatial distribution of synapses. We propose this as a general mechanism for information processing by neurons possessing dendritic trees. Moreover, we show that this implementation of stimulus selectivity increases the neuron's robustness to synaptic and dendritic failure. Importantly, our model can maintain stimulus selectivity for a larger range of loss of synapses or dendrites than an equivalent linear model. We then use a layer 2/3 biophysical neuron model to show that our implementation is consistent with two recent experimental observations: (1) one can observe a mixture of selectivities in dendrites that can differ from the somatic selectivity, and (2) hyperpolarization can broaden somatic tuning without affecting dendritic tuning. Our model predicts that an initially nonselective neuron can become selective when depolarized. In addition to motivating new experiments, the model's increased robustness to synapses and dendrites loss provides a starting point for fault-resistant neuromorphic chip development.

Stellate cells drive maturation of the entorhinal-hippocampal circuit

The neural representation of space relies on a network of entorhinal-hippocampal cell types with firing patterns tuned to different abstract features of the environment. To determine how this network is set up during early postnatal development, we monitored markers of structural maturation in developing mice, both in naïve animals and after temporally restricted pharmacogenetic silencing of specific cell populations. We found that entorhinal stellate cells provide an activity-dependent instructive signal that drives maturation sequentially and unidirectionally through the intrinsic circuits of the entorhinal-hippocampal network. The findings raise the possibility that a small number of autonomously developing neuronal populations operate as intrinsic drivers of maturation across widespread regions of the cortex.

Micro-connectomics: probing the organization of neuronal networks at the cellular scale

Defining the organizational principles of neuronal networks at the cellular scale, or micro-connectomics, is a key challenge of modern neuroscience. In this Review, we focus on graph theoretical parameters of micro-connectome topology, often informed by economical principles that conceptually originated with Ramón y Cajal's conservation laws. First, we summarize results from studies in intact small organisms and in samples from larger nervous systems. We then evaluate the evidence for an economical trade-off between biological cost and functional value in the organization of neuronal networks. Various results suggest that many aspects of neuronal network organization are indeed the outcome of competition between these two fundamental selection pressures.

Graph Theoretic and Motif Analyses of the Hippocampal Neuron Type Potential Connectome

We computed the potential connectivity map of all known neuron types in the rodent hippocampal formation by supplementing scantly available synaptic data with spatial distributions of axons and dendrites from the open-access knowledge base Hippocampome.org. The network that results from this endeavor, the broadest and most complete for a mammalian cortical region at the neuron-type level to date, contains more than 3200 connections among 122 neuron types across six subregions. Analyses of these data using graph theory metrics unveil the fundamental architectural principles of the hippocampal circuit. Globally, we identify a highly specialized topology minimizing communication cost; a modular structure underscoring the prominence of the trisynaptic loop; a core set of neuron types serving as information-processing hubs as well as a distinct group of particular antihub neurons; a nested, two-tier rich club managing much of the network traffic; and an innate resilience to random perturbations. At the local level, we uncover the basic building blocks, or connectivity patterns, that combine to produce complex global functionality, and we benchmark their utilization in the circuit relative to random networks. Taken together, these results provide a comprehensive connectivity profile of the hippocampus, yielding novel insights on its functional operations at the computationally crucial level of neuron types.

Connectomic Analysis of Brain Networks: Novel Techniques and Future Directions

Brain networks, localized or brain-wide, exist only at the cellular level, i.e., between specific pre- and post-synaptic neurons, which are connected through functionally diverse synapses located at specific points of their cell membranes. "Connectomics" is the emerging subfield of neuroanatomy explicitly aimed at elucidating the wiring of brain networks with cellular resolution and a quantified accuracy. Such data are indispensable for realistic modeling of brain circuitry and function. A connectomic analysis, therefore, needs to identify and measure the soma, dendrites, axonal path, and branching patterns together with the synapses and gap junctions of the neurons involved in any given brain circuit or network. However, because of the submicron caliber, 3D complexity, and high packing density of most such structures, as well as the fact that axons frequently extend over long distances to make synapses in remote brain regions, creating connectomic maps is technically challenging and requires multi-scale approaches, Such approaches involve the combination of the most sensitive cell labeling and analysis methods available, as well as the development of new ones able to resolve individual cells and synapses with increasing high-throughput. In this review, we provide an overview of recently introduced high-resolution methods, which researchers wanting to enter the field of connectomics may consider. It includes several molecular labeling tools, some of which specifically label synapses, and covers a number of novel imaging tools such as brain clearing protocols and microscopy approaches. Apart from describing the tools, we also provide an assessment of their qualities. The criteria we use assess the qualities that tools need in order to contribute to deciphering the key levels of circuit organization. We conclude with a brief future outlook for neuroanatomic research, computational methods, and network modeling, where we also point out several outstanding issues like structure-function relations and the complexity of neural models.

Principles of Synaptic Organization of GABAergic Interneurons in the Striatum

The striatum, the entry nucleus of the basal ganglia, lacks laminar or columnar organization of its principal cells; nevertheless, functional data suggest that it is spatially organized. Here we examine whether the connectivity and synaptic organization of striatal GABAergic interneurons contributes to such spatial organization. Focusing on the two main classes of striatal GABAergic interneurons (fast-spiking interneurons [FSIs] and low-threshold-spiking interneurons [LTSIs]), we apply a combination of optogenetics and viral tracing approaches to dissect striatal microcircuits in mice. Our results reveal fundamental differences between the synaptic organizations of both interneuron types. FSIs target exclusively striatal projection neurons (SPNs) within close proximity and form strong synapses on the proximal somatodendritic region. In contrast, LTSIs target both SPNs and cholinergic interneurons, and synaptic connections onto SPNs are made exclusively over long distances and onto distal dendrites. These results suggest fundamentally different functions of FSIs and LTSIs in shaping striatal output.

A dendritic disinhibitory circuit mechanism for pathway-specific gating

While reading a book in a noisy café, how does your brain ‘gate in' visual information while filtering out auditory stimuli? Here we propose a mechanism for such flexible routing of information flow in a complex brain network (pathway-specific gating), tested using a network model of pyramidal neurons and three classes of interneurons with connection probabilities constrained by data. We find that if inputs from different pathways cluster on a pyramidal neuron dendrite, a pathway can be gated-on by a disinhibitory circuit motif. The branch-specific disinhibition can be achieved despite dense interneuronal connectivity, even with random connections. Moreover, clustering of input pathways on dendrites can naturally emerge through synaptic plasticity regulated by dendritic inhibition. This gating mechanism in a neural circuit is further demonstrated by performing a context-dependent decision-making task. The model suggests that cognitive flexibility engages top-down signalling of behavioural rule or context that targets specific classes of inhibitory neurons.

Cortical circuits receive simultaneous inputs from multiple pathways and are able to flexibly select the appropriate inputs for processing. Here the authors propose a network model in which dendritic branch-specific disinhibition established through synaptic plasticity achieves pathway-specific gating.