Patterns Of Synaptic Connectivity Research Articles

Searching for long timescales without fine tuning.

Animal behavior occurs on timescales much longer than the response times of individual neurons. In many cases, it is plausible that these long timescales emerge from the recurrent dynamics of electrical activity in networks of neurons. In linear models, timescales are set by the eigenvalues of a dynamical matrix whose elements measure the strengths of synaptic connections between neurons. It is not clear to what extent these matrix elements need to be tuned to generate long timescales; in some cases, one needs not just a single long timescale but a whole range. Starting from the simplest case of random symmetric connections, we combine maximum entropy and random matrix theory methods to construct ensembles of networks, exploring the constraints required for long timescales to become generic. We argue that a single long timescale can emerge generically from realistic constraints, but a full spectrum of slow modes requires more tuning. Langevin dynamics that generates patterns of synaptic connections drawn from these ensembles involves a combination of Hebbian learning and activity-dependent synaptic scaling.

Repeated stimulation of feeding mechanoafferents in Aplysia generates responses consistent with the release of food.

How does repeated stimulation of mechanoafferents affect feeding motor neurons? Monosynaptic connections from a mechanoafferent population in the Aplysia buccal ganglia to five motor followers with different functions were examined during repeated stimulus trains. The mechanoafferents produced both fast and slow synaptic outputs, which could be excitatory or inhibitory. In contrast, other Aplysia mechanoafferents produce only fast excitation on their followers. In addition, patterns of synaptic connections were different to the different motor followers. Some followers received both fast excitation and fast inhibition, whereas others received exclusively fast excitation. All followers showed strong decreases in fast postsynaptic potential (PSP) amplitude within a stimulus train. Fast and slow synaptic connections were of net opposite signs in some followers but not in others. For one follower, synaptic contacts were not uniform from all subareas of the mechanoafferent cluster. Differences in properties of the buccal ganglia mechanoafferents and other Aplysia mechanoafferents may arise because the buccal ganglia neurons innervate the interior of the feeding apparatus, rather than an external surface, and connect to motor neurons for muscles with different motor functions. Fast connection patterns suggest that these synapses may be activated when food slips, biasing the musculature to release food. The largest slow inhibitory synaptic PSPs may contribute to a delay in the onset of the next behavior. Additional functions are also possible.

Nr4a1 regulates cell-specific transcriptional programs in inhibitory GABAergic interneurons

The patterns of synaptic connectivity and physiological properties of diverse neuron types are shaped by distinct gene sets. Our study demonstrates that, in the mouse forebrain, the transcriptional profiles of inhibitory GABAergic interneurons are regulated by Nr4a1, an orphan nuclear receptor whose expression is transiently induced by sensory experiences and is required for normal learning. Nr4a1 exerts contrasting effects on the local axonal wiring of parvalbumin- and somatostatin-positive interneurons, which innervate different subcellular domains of their postsynaptic partners. The loss of Nr4a1 activity in these interneurons results in bidirectional, cell-type-specific transcriptional switches across multiple gene families, including those involved in surface adhesion and repulsion. Our findings reveal that combinatorial synaptic organizing codes are surprisingly flexible and highlight a mechanism by which inducible transcription factors can influence neural circuit structure and function.

Stability against fluctuations: a two-dimensional study of scaling, bifurcations and spontaneous symmetry breaking in stochastic models of synaptic plasticity

Stochastic models of synaptic plasticity must confront the corrosive influence of fluctuations in synaptic strength on patterns of synaptic connectivity. To solve this problem, we have proposed that synapses act as filters, integrating plasticity induction signals and expressing changes in synaptic strength only upon reaching filter threshold. Our earlier analytical study calculated the lifetimes of quasi-stable patterns of synaptic connectivity with synaptic filtering. We showed that the plasticity step size in a stochastic model of spike-timing-dependent plasticity (STDP) acts as a temperature-like parameter, exhibiting a critical value below which neuronal structure formation occurs. The filter threshold scales this temperature-like parameter downwards, cooling the dynamics and enhancing stability. A key step in this calculation was a resetting approximation, essentially reducing the dynamics to one-dimensional processes. Here, we revisit our earlier study to examine this resetting approximation, with the aim of understanding in detail why it works so well by comparing it, and a simpler approximation, to the system’s full dynamics consisting of various embedded two-dimensional processes without resetting. Comparing the full system to the simpler approximation, to our original resetting approximation, and to a one-afferent system, we show that their equilibrium distributions of synaptic strengths and critical plasticity step sizes are all qualitatively similar, and increasingly quantitatively similar as the filter threshold increases. This increasing similarity is due to the decorrelation in changes in synaptic strength between different afferents caused by our STDP model, and the amplification of this decorrelation with larger synaptic filters.

Cortical cell assemblies and their underlying connectivity: An in silico study.

Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to these assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb's postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs.

Mapping of multiple neurotransmitter receptor subtypes and distinct protein complexes to the connectome

Neurons express various combinations of neurotransmitter receptor (NR) subunits and receive inputs from multiple neuron types expressing different neurotransmitters. Localizing NR subunits to specific synaptic inputs has been challenging. Here, we use epitope-tagged endogenous NR subunits, expansion light-sheet microscopy, and electron microscopy (EM) connectomics to molecularly characterize synapses in Drosophila. We show that in directionally selective motion-sensitive neurons, different multiple NRs elaborated a highly stereotyped molecular topography with NR localized to specific domains receiving cell-type-specific inputs. Developmental studies suggested that NRs or complexes of them with other membrane proteins determine patterns of synaptic inputs. In support of this model, we identify a transmembrane protein selectively associated with a subset of spatially restricted synapses and demonstrate its requirement for synapse formation through genetic analysis. We propose that mechanisms that regulate the precise spatial distribution of NRs provide a molecular cartography specifying the patterns of synaptic connections onto dendrites.

Determinants of functional synaptic connectivity among amygdala-projecting prefrontal cortical neurons in male mice

The medial prefrontal cortex (mPFC) mediates a variety of complex cognitive functions via its vast and diverse connections with cortical and subcortical structures. Understanding the patterns of synaptic connectivity that comprise the mPFC local network is crucial for deciphering how this circuit processes information and relays it to downstream structures. To elucidate the synaptic organization of the mPFC, we developed a high-throughput optogenetic method for mapping large-scale functional synaptic connectivity in acute brain slices. We show that in male mice, mPFC neurons that project to the basolateral amygdala (BLA) display unique spatial patterns of local-circuit synaptic connectivity, which distinguish them from the general mPFC cell population. When considering synaptic connections between pairs of mPFC neurons, the intrinsic properties of the postsynaptic cell and the anatomical positions of both cells jointly account for ~7.5% of the variation in the probability of connection. Moreover, anatomical distance and laminar position explain most of this fraction in variation. Our findings reveal the factors determining connectivity in the mPFC and delineate the architecture of synaptic connections in the BLA-projecting subnetwork.

Electrophysiological Recordings from Identified Cell Types in the Olfactory Cortex of Awake Mice.

Neural circuits consist of a myriad of distinct cell types, each with specific intrinsic properties and patterns of synaptic connectivity, which transform neural input and convey this information to downstream targets. Understanding how different features of an odor stimulus are encoded and relayed to their appropriate targets will require selective identification and manipulation of these different elements of the circuit. Here, we describe methods to obtain dense, extracellular electrophysiological recordings of odor-evoked activity in olfactory (piriform) cortex of awake, head-fixed mice, and optogenetic tools and procedures to identify genetically defined cell types within this circuit.

Improving the accuracy of decoding monkey brain–machine interface data by estimating the state of unobserved cell assemblies

BackgroundThe brain–machine interface is a technology that has been used for improving the quality of life of individuals with physical disabilities and also healthy individuals. It is important to improve the methods used for decoding the brain–machine interface data as the accuracy and speed of movements achieved using the existing technology are not comparable to the normal body. Comparison with the existing methodDecoding of brain–machine interface data using the proposed method resulted in improved decoding accuracy compared to the existing method. ConclusionsThe results demonstrated the usefulness of cell assembly state estimation method for decoding the brain–machine interface data. New MethodWe incorporated a novel method of estimating cell assembly states using spike trains with the existing decoding method that used only firing rate data. Synaptic connectivity pattern was used as feature values in addition to firing rate. Publicly available monkey brain–machine interface datasets were used in the study. ResultsAs long as the decoding was successful, the root mean square error of the proposed method was significantly smaller than the existing method. Artificial neural netowork-based decoding method resulted in more stable decoding, and also improved the decoding accuracy due to incorporation of synaptic connectivity pattern.

Geometric framework to predict structure from function in neural networks.

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

Synaptic counts approximate synaptic contact area in Drosophila.

The pattern of synaptic connections among neurons defines the circuit structure, which constrains the computations that a circuit can perform. The strength of synaptic connections is costly to measure yet important for accurate circuit modeling. Synaptic surface area has been shown to correlate with synaptic strength, yet in the emerging field of connectomics, most studies rely instead on the counts of synaptic contacts between two neurons. Here we quantified the relationship between synaptic count and synaptic area as measured from volume electron microscopy of the larval Drosophila central nervous system. We found that the total synaptic surface area, summed across all synaptic contacts from one presynaptic neuron to a postsynaptic one, can be accurately predicted solely from the number of synaptic contacts, for a variety of neurotransmitters. Our findings support the use of synaptic counts for approximating synaptic strength when modeling neural circuits.

Observing Cell Assemblies From Spike Train Recordings Based on the Biological Basis of Synaptic Connectivity.

Cell assemblies are difficult to observe because they consist of many neurons. We aimed to observe cell assemblies based on biological statistics, such as synaptic connectivity. We developed an estimation method to estimate the activity and synaptic connectivity of cell assemblies from spike trains using mathematical models of individual neurons and cell assemblies. Synaptic transmissions were averaged to generate postsynaptic currents with the same timing and waveform but different amplitudes, as the number of presynaptic neurons was large. We estimated the average synaptic transmission and synaptic connectivity from active cell assemblies based on the stochastic prediction of membrane potentials and verified the estimation ability of the average synaptic transmission and synaptic connectivity using the proposed method on simulated neural activity. Different cell assembly activities evoked by electrical stimuli were correctly sorted into various clusters in experiments using rat cortical neurons cultured on microelectrode arrays. We observed multiple cell assemblies from the spontaneous activity of rat cortical networks on microelectrode arrays, based on the synaptic connectivity patterns estimated by the proposed method. The proposed method was superior to the conventional method for detecting the activity of multiple cell assemblies. Using the proposed method, it is possible to observe multiple cell assemblies based on the biological basis of synaptic connectivity. In summary, we report a novel method to observe cell assemblies from spike train recordings based on the biological basis of synaptic connectivity, rather than merely relying on a statistical method.

Kinesin-3 mediated axonal delivery of presynaptic neurexin stabilizes dendritic spines and postsynaptic components.

The functional properties of neural circuits are defined by the patterns of synaptic connections between their partnering neurons, but the mechanisms that stabilize circuit connectivity are poorly understood. We systemically examined this question at synapses onto newly characterized dendritic spines of C. elegans GABAergic motor neurons. We show that the presynaptic adhesion protein neurexin/NRX-1 is required for stabilization of postsynaptic structure. We find that early postsynaptic developmental events proceed without a strict requirement for synaptic activity and are not disrupted by deletion of neurexin/nrx-1. However, in the absence of presynaptic NRX-1, dendritic spines and receptor clusters become destabilized and collapse prior to adulthood. We demonstrate that NRX-1 delivery to presynaptic terminals is dependent on kinesin-3/UNC-104 and show that ongoing UNC-104 function is required for postsynaptic maintenance in mature animals. By defining the dynamics and temporal order of synapse formation and maintenance events in vivo, we describe a mechanism for stabilizing mature circuit connectivity through neurexin-based adhesion.

Determinants of synapse diversity revealed by super-resolution quantal transmission and active zone imaging

Neural circuit function depends on the pattern of synaptic connections between neurons and the strength of those connections. Synaptic strength is determined by both postsynaptic sensitivity to neurotransmitter and the presynaptic probability of action potential evoked transmitter release (Pr). Whereas morphology and neurotransmitter receptor number indicate postsynaptic sensitivity, presynaptic indicators and the mechanism that sets Pr remain to be defined. To address this, we developed QuaSOR, a super-resolution method for determining Pr from quantal synaptic transmission imaging at hundreds of glutamatergic synapses at a time. We mapped the Pr onto super-resolution 3D molecular reconstructions of the presynaptic active zones (AZs) of the same synapses at the Drosophila larval neuromuscular junction (NMJ). We find that Pr varies greatly between synapses made by a single axon, quantify the contribution of key AZ proteins to Pr diversity and find that one of these, Complexin, suppresses spontaneous and evoked transmission differentially, thereby generating a spatial and quantitative mismatch between release modes. Transmission is thus regulated by the balance and nanoscale distribution of release-enhancing and suppressing presynaptic proteins to generate high signal-to-noise evoked transmission.

Mechanisms Underlying Target Selectivity for Cell Types and Subcellular Domains in Developing Neocortical Circuits.

The cerebral cortex contains numerous neuronal cell types, distinguished by their molecular identity as well as their electrophysiological and morphological properties. Cortical function is reliant on stereotyped patterns of synaptic connectivity and synaptic function among these neuron types, but how these patterns are established during development remains poorly understood. Selective targeting not only of different cell types but also of distinct postsynaptic neuronal domains occurs in many brain circuits and is directed by multiple mechanisms. These mechanisms include the regulation of axonal and dendritic guidance and fine-scale morphogenesis of pre- and postsynaptic processes, lineage relationships, activity dependent mechanisms and intercellular molecular determinants such as transmembrane and secreted molecules, many of which have also been implicated in neurodevelopmental disorders. However, many studies of synaptic targeting have focused on circuits in which neuronal processes target different lamina, such that cell-type-biased connectivity may be confounded with mechanisms of laminar specificity. In the cerebral cortex, each cortical layer contains cell bodies and processes from intermingled neuronal cell types, an arrangement that presents a challenge for the development of target-selective synapse formation. Here, we address progress and future directions in the study of cell-type-biased synaptic targeting in the cerebral cortex. We highlight challenges to identifying developmental mechanisms generating stereotyped patterns of intracortical connectivity, recent developments in uncovering the determinants of synaptic target selection during cortical synapse formation, and current gaps in the understanding of cortical synapse specificity.

Architectures of neuronal circuits.

Although individual neurons are the basic unit of the nervous system, they process information by working together in neuronal circuits with specific patterns of synaptic connectivity. Here, I review common circuit motifs and architectural plans used in diverse brain regions and animal species. I also consider how these circuit architectures assemble during development and might have evolved. Understanding how specific patterns of synaptic connectivity can implement specific neural computations will help to bridge the huge gap between the biology of the individual neuron and the function of the entire brain, allow us to better understand the neural basis of behavior, and may inspire new advances in artificial intelligence.

Imaging of In Vitro and In Vivo Neurons in Drosophila Using Stochastic Optical Reconstruction Microscopy.

The Drosophila melanogaster brain comprises different neuronal cell types that interconnect with precise patterns of synaptic connections. These patterns are essential for the normal function of the brain. To understand the connectivity patterns requires characterizing them at single-cell resolution, for which a fluorescence microscope becomes an indispensable tool. Additionally, because the neurons connect at the nanoscale, the investigation often demands super-resolution microscopy. Here, we adopt one super-resolution microscopy technique, called stochastic optical reconstruction microscopy (STORM), improving the lateral and axial resolution to ∼20 nm. This article extensively describes our methods along with considerations for sample preparation of neurons in vitro and in vivo, conjugation of dyes to antibodies, immunofluorescence labeling, and acquisition and processing of STORM data. With these tools and techniques, we open up the potential to investigate cell-cell interactions using STORM in the Drosophila nervous system. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Preparation of Drosophila primary neuronal culture and embryonic fillets Basic Protocol 2: Immunofluorescence labeling of samples Basic Protocol 3: Single-molecule fluorescence imaging Basic Protocol 4: Localization and visualization of single-molecule data Supporting Protocol: Conjugation of antibodies with STORM-compatible dyes.

Untangling the cortico-thalamo-cortical loop: cellular pieces of a knotty circuit puzzle.

Functions of the neocortex depend on its bidirectional communication with the thalamus, via cortico-thalamo-cortical (CTC) loops. Recent work dissecting the synaptic connectivity in these loops is generating a clearer picture of their cellular organization. Here, we review findings across sensory, motor and cognitive areas, focusing on patterns of cell type-specific synaptic connections between the major types of cortical and thalamic neurons. We outline simple and complex CTC loops, and note features of these loops that appear to be general versus specialized. CTC loops are tightly interlinked with local cortical and corticocortical (CC) circuits, forming extended chains of loops that are probably critical for communication across hierarchically organized cerebral networks. Such CTC-CC loop chains appear to constitute a modular unit of organization, serving as scaffolding for area-specific structural and functional modifications. Inhibitory neurons and circuits are embedded throughout CTC loops, shaping the flow of excitation. We consider recent findings in the context of established CTC and CC circuit models, and highlight current efforts to pinpoint cell type-specific mechanisms in CTC loops involved in consciousness and perception. As pieces of the connectivity puzzle fall increasingly into place, this knowledge can guide further efforts to understand structure-function relationships in CTC loops.

MicroRNA Signatures of the Developing Primate Fovea.

Rod and cone photoreceptors differ in their shape, photopigment expression, synaptic connection patterns, light sensitivity, and distribution across the retina. Although rods greatly outnumber cones, human vision is mostly dependent on cone photoreceptors since cones are essential for our sharp visual acuity and color discrimination. In humans and other primates, the fovea centralis (fovea), a specialized region of the central retina, contains the highest density of cones. Despite the vast importance of the fovea for human vision, the molecular mechanisms guiding the development of this region are largely unknown. MicroRNAs (miRNAs) are small post-transcriptional regulators known to orchestrate developmental transitions and cell fate specification in the retina. Here, we have characterized the transcriptional landscape of the developing rhesus monkey retina. Our data indicates that non-human primate fovea development is significantly accelerated compared to the equivalent retinal region at the other side of the optic nerve head, as described previously. Notably, we also identify several miRNAs differentially expressed in the presumptive fovea, including miR-15b-5p, miR-342-5p, miR-30b-5p, miR-103-3p, miR-93-5p as well as the miRNA cluster miR-183/-96/-182. Interestingly, miR-342-5p is enriched in the nasal primate retina and in the peripheral developing mouse retina, while miR-15b is enriched in the temporal primate retina and increases over time in the mouse retina in a central-to-periphery gradient. Together our data constitutes the first characterization of the developing rhesus monkey retinal miRNome and provides novel datasets to attain a more comprehensive understanding of foveal development.

Transition to synchronization in heterogeneous inhibitory neural networks with structured synapses.

Inhibitory neurons form an extensive network involved in the development of different rhythms in the cerebral cortex. A transition from an incoherent state, where all inhibitory neurons fire unrelated to each other, to a synchronized or locked state, where all or most neurons define a tight firing pattern, is maybe the most salient process to analyze when considering neuronal rhythms. In this work, we analyzed whether different patterns of effective synaptic connectivity may support a first-order-like transition in this path to synchronization. Such an "explosive" phenomenon may be relevant in neural processes, as normal cognitive processing in different tasks and some neurological disorders manifest an increased power in many neuronal rhythms, supported by an extended concerted spiking activity and an abrupt change to this state. Furthermore, we built an adaptive mechanism that supports the generation of this kind of network, which rapidly creates the underlying structure based on the ongoing firing statistics.