Structure and function of a neocortical synapse

Whole-brain annotation and multi-connectome cell typing of Drosophila

The fruit fly Drosophila melanogaster has emerged as a key model organism in neuroscience, in large part due to the concentration of collaboratively generated molecular, genetic and digital resources available for it. Here we complement the approximately 140,000 neuron FlyWire whole-brain connectome1 with a systematic and hierarchical annotation of neuronal classes, cell types and developmental units (hemilineages). Of 8,453 annotated cell types, 3,643 were previously proposed in the partial hemibrain connectome2, and 4,581 are new types, mostly from brain regions outside the hemibrain subvolume. Although nearly all hemibrain neurons could be matched morphologically in FlyWire, about one-third of cell types proposed for the hemibrain could not be reliably reidentified. We therefore propose a new definition of cell type as groups of cells that are each quantitatively more similar to cells in a different brain than to any other cell in the same brain, and we validate this definition through joint analysis of FlyWire and hemibrain connectomes. Further analysis defined simple heuristics for the reliability of connections between brains, revealed broad stereotypy and occasional variability in neuron count and connectivity, and provided evidence for functional homeostasis in the mushroom body through adjustments of the absolute amount of excitatory input while maintaining the excitation/inhibition ratio. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open-source toolchain for brain-scale comparative connectomics.

Prefrontal synaptic regulation of homeostatic sleep pressure revealed through synaptic chemogenetics

Sleep is regulated by homeostatic processes, yet the biological basis of sleep pressure that accumulates during wakefulness, triggers sleep, and dissipates during sleep remains elusive. We explored a causal relationship between cellular synaptic strength and electroencephalography delta power indicating macro-level sleep pressure by developing a theoretical framework and a molecular tool to manipulate synaptic strength. The mathematical model predicted that increased synaptic strength promotes the neuronal "down state" and raises the delta power. Our molecular tool (synapse-targeted chemically induced translocation of Kalirin-7, SYNCit-K), which induces dendritic spine enlargement and synaptic potentiation through chemically induced translocation of protein Kalirin-7, demonstrated that synaptic potentiation of excitatory neurons in the prefrontal cortex (PFC) increases nonrapid eye movement sleep amounts and delta power. Thus, synaptic strength of PFC excitatory neurons dictates sleep pressure in mammals.

Synaptic integration of somatosensory and motor cortical inputs onto spiny projection neurons of mice caudoputamen

The basal ganglia play pivotal roles in motor control and cognitive functioning. These nuclei are embedded in an anatomical loop: cortex to basal ganglia to thalamus back to cortex. We focus here on an essential synapse for descending control, from cortical layer 5 (L5) onto the GABAergic spiny projection neurons (SPNs) of the caudoputamen (CP). We employed genetic labeling to distinguish L5 neurons from somatosensory (S1) and motor (M1) cortices in large volume serial electron microscopy and electrophysiology datasets to better detail these inputs. First, M1 and S1 synapses showed a strong preference to innervate the spines of SPNs and rarely contacted aspiny cells, which are likely to be interneurons. Second, L5 inputs commonly converge from both areas onto single SPNs. Third, compared to unlabeled terminals in CP, those labeled from M1 and S1 show ultrastructural hallmarks of strong driver synapses: They innervate larger spines that were more likely to contain a spine apparatus, more often had embedded mitochondria, and more often contacted multiple targets. Finally, these inputs also demonstrated driver-like functional properties: SPNs responded to optogenetic activation from S1 and M1 with large EPSP/Cs that depressed and were dependent on ionotropic but not metabotropic receptors. Together, our findings suggest that individual SPNs integrate driver input from multiple cortical areas with implications for how the basal ganglia relay cortical input to provide inhibitory innervation of motor thalamus.

Volume electron microscopy reveals 3D synaptic nanoarchitecture in postmortem human prefrontal cortex

Synaptic function is directly reflected in quantifiable ultrastructural features using electron microscopy (EM) approaches. This coupling of synaptic function and ultrastructure suggests that in vivo synaptic function can be inferred from EM analysis of ex vivo human brain tissue. To investigate this, we employed focused ion beam-scanning electron microscopy (FIB-SEM), a volume EM (VEM) approach, to generate ultrafine-resolution, three-dimensional (3D) micrographic datasets of postmortem human dorsolateral prefrontal cortex (DLPFC), a region with cytoarchitectonic characteristics distinct to human brain. Synaptic, sub-synaptic, and organelle measures were highly consistent with findings from experimental models that are free from antemortem or postmortem effects. Further, 3D neuropil reconstruction revealed a unique, ultrastructurally-complex, spiny dendritic shaft that exhibited features characteristic of heightened synaptic communication, integration, and plasticity. Altogether, our findings provide critical proof-of-concept data demonstrating that ex vivo VEM analysis is an effective approach to infer in vivo synaptic functioning in human brain.

Signatures of Bayesian inference emerge from energy-efficient synapses

Biological synaptic transmission is unreliable, and this unreliability likely degrades neural circuit performance. While there are biophysical mechanisms that can increase reliability, for instance by increasing vesicle release probability, these mechanisms cost energy. We examined four such mechanisms along with the associated scaling of the energetic costs. We then embedded these energetic costs for reliability in artificial neural networks (ANNs) with trainable stochastic synapses, and trained these networks on standard image classification tasks. The resulting networks revealed a tradeoff between circuit performance and the energetic cost of synaptic reliability. Additionally, the optimised networks exhibited two testable predictions consistent with pre-existing experimental data. Specifically, synapses with lower variability tended to have (1) higher input firing rates and (2) lower learning rates. Surprisingly, these predictions also arise when synapse statistics are inferred through Bayesian inference. Indeed, we were able to find a formal, theoretical link between the performance-reliability cost tradeoff and Bayesian inference. This connection suggests two incompatible possibilities: evolution may have chanced upon a scheme for implementing Bayesian inference by optimising energy efficiency, or alternatively, energy-efficient synapses may display signatures of Bayesian inference without actually using Bayes to reason about uncertainty.

Combining array tomography with electron tomography provides insights into leakiness of the blood-brain barrier in mouse cortex

Like other volume electron microscopy approaches, automated tape-collecting ultramicrotomy (ATUM) enables imaging of serial sections deposited on thick plastic tapes by scanning electron microscopy (SEM). ATUM is unique in enabling hierarchical imaging and thus efficient screening for target structures, as needed for correlative light and electron microscopy. However, SEM of sections on tape can only access the section surface, thereby limiting the axial resolution to the typical size of cellular vesicles with an order of magnitude lower than the acquired xy resolution. In contrast, serial-section electron tomography (ET), a transmission electron microscopy-based approach, yields isotropic voxels at full EM resolution, but requires deposition of sections on electron-stable thin and fragile films, thus making screening of large section libraries difficult and prone to section loss. To combine the strength of both approaches, we developed 'ATUM-Tomo, a hybrid method, where sections are first reversibly attached to plastic tape via a dissolvable coating, and after screening detached and transferred to the ET-compatible thin films. As a proof-of-principle, we applied correlative ATUM-Tomo to study ultrastructural features of blood-brain barrier (BBB) leakiness around microthrombi in a mouse model of traumatic brain injury. Microthrombi and associated sites of BBB leakiness were identified by confocal imaging of injected fluorescent and electron-dense nanoparticles, then relocalized by ATUM-SEM, and finally interrogated by correlative ATUM-Tomo. Overall, our new ATUM-Tomo approach will substantially advance ultrastructural analysis of biological phenomena that require cell- and tissue-level contextualization of the finest subcellular textures.

Parallel Synapses with Transmission Nonlinearities Enhance Neuronal Classification Capacity

Cortical neurons often establish multiple synaptic contacts with the same postsynaptic neuron. To avoid functional redundancy of these parallel synapses, it is crucial that each synapse exhibits distinct computational properties. Here we model the current to the soma contributed by each synapse as a sigmoidal transmission function of its presynaptic input, with learnable parameters such as amplitude, slope, and threshold. We evaluate the classification capacity of a neuron equipped with such nonlinear parallel synapses, and show that with a small number of parallel synapses per axon, it substantially exceeds that of the Perceptron. Furthermore, the number of correctly classified data points can increase superlinearly as the number of presynaptic axons grows. When training with an unrestricted number of parallel synapses, our model neuron can effectively implement an arbitrary aggregate transmission function for each axon, constrained only by monotonicity. Nevertheless, successful learning in the model neuron often requires only a small number of parallel synapses. We also apply these parallel synapses in a feedforward neural network trained to classify MNIST images, and show that they can increase the test accuracy. This demonstrates that multiple nonlinear synapses per input axon can substantially enhance a neuron's computational power.

Intracellular magnesium optimizes transmission efficiency and plasticity of hippocampal synapses by reconfiguring their connectivity

Synapses at dendritic branches exhibit specific properties for information processing. However, how the synapses are orchestrated to dynamically modify their properties, thus optimizing information processing, remains elusive. Here, we observed at hippocampal dendritic branches diverse configurations of synaptic connectivity, two extremes of which are characterized by low transmission efficiency, high plasticity and coding capacity, or inversely. The former favors information encoding, pertinent to learning, while the latter prefers information storage, relevant to memory. Presynaptic intracellular Mg2+ crucially mediates the dynamic transition continuously between the two extreme configurations. Consequently, varying intracellular Mg2+ levels endow individual branches with diverse synaptic computations, thus modulating their ability to process information. Notably, elevating brain Mg2+ levels in aging animals restores synaptic configuration resembling that of young animals, coincident with improved learning and memory. These findings establish intracellular Mg2+ as a crucial factor reconfiguring synaptic connectivity at dendrites, thus optimizing their branch-specific properties in information processing.

Ultrastructural analysis of nigrostriatal dopaminergic terminals in a knockin mouse model of DYT1 dystonia

DYT1 dystonia is associated with decreased striatal dopamine release. In this study, we examined the possibility that ultrastructural changes of nigrostriatal dopamine terminals could contribute to this neurochemical imbalance using a serial block face/scanning electron microscope (SBF/SEM) and three-dimensional reconstruction to analyse striatal tyrosine hydroxylase-immunoreactive (TH-IR) terminals and their synapses in a DYT1(ΔE) knockin (DYT1-KI) mouse model of DYT1 dystonia. Furthermore, to study possible changes in vesicle packaging capacity of dopamine, we used transmission electron microscopy to assess the synaptic vesicle size in striatal dopamine terminals. Quantitative comparative analysis of 80 fully reconstructed TH-IR terminals in the WT and DYT1-KI mice indicate (1) no significant difference in the volume of TH-IR terminals; (2) no major change in the proportion of axo-spinous versus axo-dendritic synapses; (3) no significant change in the post-synaptic density (PSD) area of axo-dendritic synapses, while the PSDs of axo-spinous synapses were significantly smaller in DYT1-KI mice; (4) no significant change in the contact area between TH-IR terminals and dendritic shafts or spines, while the ratio of PSD area/contact area decreased significantly for both axo-dendritic and axo-spinous synapses in DYT1-KI mice; (5) no significant difference in the mitochondria volume; and (6) no significant difference in the synaptic vesicle area between the two groups. Altogether, these findings suggest that abnormal morphometric changes of nigrostriatal dopamine terminals and their post-synaptic targets are unlikely to be a major source of reduced striatal dopamine release in DYT1 dystonia.

Spine plasticity of dentate gyrus parvalbumin-positive interneurons is regulated by experience

Experience-driven alterations in neuronal activity are followed by structural-functional modifications allowing cells to adapt to these activity changes. Structural plasticity has been observed for cortical principal cells. However, how GABAergic interneurons respond to experience-dependent network activity changes is not well understood. We show that parvalbumin-expressing interneurons (PVIs) of the dentate gyrus (DG) possess dendritic spines, which undergo behaviorally induced structural dynamics. Glutamatergic inputs at PVI spines evoke signals with high spatial compartmentalization defined by neck length. Mice experiencing novel contexts form more PVI spines with elongated necks and exhibit enhanced network and PVI activity and cFOS expression. Enhanced green fluorescent protein reconstitution across synaptic partner-mediated synapse labeling shows that experience-driven PVI spine growth boosts targeting of PVI spines over shafts by glutamatergic synapses. Our findings propose a role for PVI spine dynamics in regulating PVI excitation by their inputs, which may allow PVIs to dynamically adjust their functional integration in the DG microcircuitry in relation to network computational demands.

Spot Spine, a freely available ImageJ plugin for 3D detection and morphological analysis of dendritic spines

Background

Dendritic spines are tiny protrusions found along the dendrites of neurons, and their number is a measure of the density of synaptic connections. Altered density and morphology is observed in several pathologies, and spine formation as well as morphological changes correlate with learning and memory. The detection of spines in microscopy images and the analysis of their morphology is therefore a prerequisite for many studies. We have developed a new open-source, freely available, plugin for ImageJ/FIJI, called Spot Spine, that allows detection and morphological measurements of spines in three dimensional images.

Method

Local maxima are detected in spine heads, and the intensity distribution around the local maximum is computed to perform the segmentation of each spine head. Spine necks are then traced from the spine head to the dendrite. Several parameters can be set to optimize detection and segmentation, and manual correction gives further control over the result of the process.

Results

The plugin allows the analysis of images of dendrites obtained with various labeling and imaging methods. Quantitative measurements are retrieved including spine head volume and surface, and neck length.

Conclusion

The plugin and instructions for use are available at https://imagej.net/plugins/spot-spine.

Developmental transformation of Ca2+ channel-vesicle nanotopography at a central GABAergic synapse

The coupling between Ca2+ channels and release sensors is a key factor defining the signaling properties of a synapse. However, the coupling nanotopography at many synapses remains unknown, and it is unclear how it changes during development. To address these questions, we examined coupling at the cerebellar inhibitory basket cell (BC)-Purkinje cell (PC) synapse. Biophysical analysis of transmission by paired recording and intracellular pipette perfusion revealed that the effects of exogenous Ca2+ chelators decreased during development, despite constant reliance of release on P/Q-type Ca2+ channels. Structural analysis by freeze-fracture replica labeling (FRL) and transmission electron microscopy (EM) indicated that presynaptic P/Q-type Ca2+ channels formed nanoclusters throughout development, whereas docked vesicles were only clustered at later developmental stages. Modeling suggested a developmental transformation from a more random to a more clustered coupling nanotopography. Thus, presynaptic signaling developmentally approaches a point-to-point configuration, optimizing speed, reliability, and energy efficiency of synaptic transmission.

Structural Plasticity of GABAergic Pallidothalamic Terminals in MPTP-Treated Parkinsonian Monkeys: A 3D Electron Microscopic Analysis

The internal globus pallidus (GPi) is a major source of tonic GABAergic inhibition to the motor thalamus. In parkinsonism, the firing rate of GPi neurons is increased, and their pattern switches from a tonic to a burst mode, two pathophysiological changes associated with increased GABAergic pallidothalamic activity. In this study, we used high-resolution 3D electron microscopy to demonstrate that GPi terminals in the parvocellular ventral anterior nucleus (VApc) and the centromedian nucleus (CM), the two main GPi-recipient motor thalamic nuclei in monkeys, undergo significant morphometric changes in parkinsonian monkeys including (1) increased terminal volume in both nuclei; (2) increased surface area of synapses in both nuclei; (3) increased number of synapses/GPi terminals in the CM, but not VApc; and (4) increased total volume, but not number, of mitochondria/terminals in both nuclei. In contrast to GPi terminals, the ultrastructure of putative GABAergic nonpallidal terminals was not affected. Our results also revealed striking morphological differences in terminal volume, number/area of synapses, and volume/number of mitochondria between GPi terminals in VApc and CM of control monkeys. In conclusion, GABAergic pallidothalamic terminals are endowed with a high level of structural plasticity that may contribute to the development and maintenance of the abnormal increase in pallidal GABAergic outflow to the thalamus in the parkinsonian state. Furthermore, the evidence for ultrastructural differences between GPi terminals in VApc and CM suggests that morphologically distinct pallidothalamic terminals from single pallidal neurons may underlie specific physiological properties of pallidal inputs to VApc and CM in normal and diseased states.

Synaptic wiring motifs in posterior parietal cortex support decision-making

The posterior parietal cortex exhibits choice-selective activity during perceptual decision-making tasks1-10. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here we combined virtual-reality behaviour, two-photon calcium imaging, high-throughput electron microscopy and circuit modelling to analyse how synaptic connectivity between neurons in the posterior parietal cortex relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. This motif was present even between neurons with activity peaks in different task epochs. We developed neural-circuit models of the computations performed by these motifs, and found that opponent inhibition between neural populations with opposite selectivity amplifies selective inputs, thereby improving the encoding of trial-type information. The models also predict that opponent inhibition between neurons with activity peaks in different task epochs contributes to creating choice-specific sequential activity. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.

Phosphorylation-dependent membraneless organelle fusion and fission illustrated by postsynaptic density assemblies

Membraneless organelles formed by phase separation of proteins and nucleic acids play diverse cellular functions. Whether and, if yes, how membraneless organelles in ways analogous to membrane-based organelles also undergo regulated fusion and fission is unknown. Here, using a partially reconstituted mammalian postsynaptic density (PSD) condensate as a paradigm, we show that membraneless organelles can undergo phosphorylation-dependent fusion and fission. Without phosphorylation of the SAPAP guanylate kinase domain-binding repeats, the upper and lower layers of PSD protein mixtures form two immiscible sub-compartments in a phase-in-phase organization. Phosphorylation of SAPAP leads to fusion of the two sub-compartments into one condensate accompanied with an increased Stargazin density in the condensate. Dephosphorylation of SAPAP can reverse this event. Preventing SAPAP phosphorylation in vivo leads to increased separation of proteins from the lower and upper layers of PSD sub-compartments. Thus, analogous to membrane-based organelles, membraneless organelles can also undergo regulated fusion and fission.

Postsynaptic cell type and synaptic distance do not determine efficiency of monosynaptic rabies virus spread measured at synaptic resolution

Retrograde monosynaptic tracing using glycoprotein-deleted rabies virus is an important component of the toolkit for investigation of neural circuit structure and connectivity. It allows for the identification of first-order presynaptic connections to cell populations of interest across both the central and peripheral nervous system, helping to decipher the complex connectivity patterns of neural networks that give rise to brain function. Despite its utility, the factors that influence the probability of transsynaptic rabies spread are not well understood. While it is well established that expression levels of rabies glycoprotein used to trans-complement G-deleted rabies can result in large changes in numbers of inputs labeled per starter cell (convergence index [CI]), it is not known how typical values of CI relate to the proportions of synaptic contacts or input neurons labeled. And it is not known whether inputs to different cell types, or synaptic contacts that are more proximal or distal to the cell body, are labeled with different probabilities. Here, we use a new rabies virus construct that allows for the simultaneous labeling of pre- and postsynaptic specializations to quantify the proportion of synaptic contacts labeled in mouse primary visual cortex. We demonstrate that with typical conditions about 40% of first-order presynaptic excitatory synapses to cortical excitatory and inhibitory neurons are labeled. We show that using matched tracing conditions there are similar proportions of labeled contacts onto L4 excitatory pyramidal, somatostatin (Sst) inhibitory, and vasoactive intestinal peptide (Vip) starter cell types. Furthermore, we find no difference in the proportions of labeled excitatory contacts onto postsynaptic sites at different subcellular locations.

Postsynaptic mitochondria are positioned to support functional diversity of dendritic spines

Postsynaptic mitochondria are critical for the development, plasticity, and maintenance of synaptic inputs. However, their relationship to synaptic structure and functional activity is unknown. We examined a correlative dataset from ferret visual cortex with in vivo two-photon calcium imaging of dendritic spines during visual stimulation and electron microscopy reconstructions of spine ultrastructure, investigating mitochondrial abundance near functionally and structurally characterized spines. Surprisingly, we found no correlation to structural measures of synaptic strength. Instead, we found that mitochondria are positioned near spines with orientation preferences that are dissimilar to the somatic preference. Additionally, we found that mitochondria are positioned near groups of spines with heterogeneous orientation preferences. For a subset of spines with a mitochondrion in the head or neck, synapses were larger and exhibited greater selectivity to visual stimuli than those without a mitochondrion. Our data suggest mitochondria are not necessarily positioned to support the energy needs of strong spines, but rather support the structurally and functionally diverse inputs innervating the basal dendrites of cortical neurons.

Isochronic development of cortical synapses in primates and mice

The neotenous, or delayed, development of primate neurons, particularly human ones, is thought to underlie primate-specific abilities like cognition. We tested whether synaptic development follows suit-would synapses, in absolute time, develop slower in longer-lived, highly cognitive species like non-human primates than in shorter-lived species with less human-like cognitive abilities, e.g., the mouse? Instead, we find that excitatory and inhibitory synapses in the male Mus musculus (mouse) and Rhesus macaque (primate) cortex form at similar rates, at similar times after birth. Primate excitatory and inhibitory synapses and mouse excitatory synapses also prune in such an isochronic fashion. Mouse inhibitory synapses are the lone exception, which are not pruned and instead continuously added throughout life. The monotony of synaptic development clocks across species with disparate lifespans, experiences, and cognitive abilities argues that such programs are likely orchestrated by genetic events rather than experience.

Approaching the Zero-Power Operating Limit in a Self-Coordinated Organic Protonic Synapse

High-performance artificial synapse with nonvolatile memory and low power consumption is a perfect candidate for brainoid intelligence. Unfortunately, due to the energy barrier paradox between ultra-low power and nonvolatile modulation of device conductances, it is still a challenge at the moment to construct such ideal synapses. Herein, a proton-reservoir type 4,4',4″,4'''-(Porphine-5,10,15,20-tetrayl) tetrakis (benzenesulfonic acid) (TPPS) molecule and fabricated organic protonic memristors with device width of 10 µm to 100 nm is synthesized. The occurrence of sequential proton migration and interfacial self-coordinated doping will introduce new energy levels into the molecular bandgap, resulting in effective and nonvolatile modulation of device conductance over 64 continuous states with retention exceeding 30 min. The power consumptions of modulating and reading the device conductance approach the zero-power operating limits, which range from 16.25 pW to 2.06 nW and 6.5 fW to 0.83 pW, respectively. Finally, a robust artificial synapse is successfully demonstrated, showing spiking-rate-dependent plasticity (SRDP) and spiking-timing-dependent plasticity (STDP) characteristics with ultra-low power of 0.66 to 0.82 pW, as well as 100 long-term depression (LTD)/potentiation (LTP) cycles with 0.14%/0.30% weight variations.

Utilizing 2D-region-based CNNs for automatic dendritic spine detection in 3D live cell imaging

Dendritic spines are considered a morphological proxy for excitatory synapses, rendering them a target of many different lines of research. Over recent years, it has become possible to simultaneously image large numbers of dendritic spines in 3D volumes of neural tissue. In contrast, currently no automated method for 3D spine detection exists that comes close to the detection performance reached by human experts. However, exploiting such datasets requires new tools for the fully automated detection and analysis of large numbers of spines. Here, we developed an efficient analysis pipeline to detect large numbers of dendritic spines in volumetric fluorescence imaging data acquired by two-photon imaging in vivo. The core of our pipeline is a deep convolutional neural network that was pretrained on a general-purpose image library and then optimized on the spine detection task. This transfer learning approach is data efficient while achieving a high detection precision. To train and validate the model we generated a labeled dataset using five human expert annotators to account for the variability in human spine detection. The pipeline enables fully automated dendritic spine detection reaching a performance slightly below that of the human experts. Our method for spine detection is fast, accurate and robust, and thus well suited for large-scale datasets with thousands of spines. The code is easily applicable to new datasets, achieving high detection performance, even without any retraining or adjustment of model parameters.

Trainable Bilingual Synaptic Functions in Bio-enabled Synaptic Transistors

The signal transmission of the nervous system is regulated by neurotransmitters. Depending on the type of neurotransmitter released by presynaptic neurons, neuron cells can either be excited or inhibited. Maintaining a balance between excitatory and inhibitory synaptic responses is crucial for the nervous system's versatility, elasticity, and ability to perform parallel computing. On the way to mimic the brain's versatility and plasticity traits, creating a preprogrammed balance between excitatory and inhibitory responses is required. Despite substantial efforts to investigate the balancing of the nervous system, a complex circuit configuration has been suggested to simulate the interaction between excitatory and inhibitory synapses. As a meaningful approach, an optoelectronic synapse for balancing the excitatory and inhibitory responses assisted by light mediation is proposed here by deploying humidity-sensitive chiral nematic phases of known polysaccharide cellulose nanocrystals. The environment-induced pitch tuning changes the polarization of the helicoidal organization, affording different hysteresis effects with the subsequent excitatory and inhibitory nonvolatile behavior in the bio-electrolyte-gated transistors. By applying voltage pulses combined with stimulation of chiral light, the artificial optoelectronic synapse tunes not only synaptic functions but also learning pathways and color recognition. These multifunctional bio-based synaptic field-effect transistors exhibit potential for enhanced parallel neuromorphic computing and robot vision technology.

Postsynaptic mitochondria are positioned to support functional diversity of dendritic spines

Postsynaptic mitochondria are critical to the development, plasticity, and maintenance of synaptic inputs. However, their relationship to synaptic structure and functional activity is unknown. We examined a correlative dataset from ferret visual cortex with in vivo two-photon calcium imaging of dendritic spines during visual stimulation and electron microscopy (EM) reconstructions of spine ultrastructure, investigating mitochondrial abundance near functionally- and structurally-characterized spines. Surprisingly, we found no correlation to structural measures of synaptic strength. Instead, we found that mitochondria are positioned near spines with orientation preferences that are dissimilar to the somatic preference. Additionally, we found that mitochondria are positioned near groups of spines with heterogeneous orientation preferences. For a subset of spines with mitochondrion in the head or neck, synapses were larger and exhibited greater selectivity to visual stimuli than those without a mitochondrion. Our data suggest mitochondria are not necessarily positioned to support the energy needs of strong spines, but rather support the structurally and functionally diverse inputs innervating the basal dendrites of cortical neurons.

Neuromorphic sequence learning with an event camera on routes through vegetation

For many robotics applications, it is desirable to have relatively low-power and efficient onboard solutions. We took inspiration from insects, such as ants, that are capable of learning and following routes in complex natural environments using relatively constrained sensory and neural systems. Such capabilities are particularly relevant to applications such as agricultural robotics, where visual navigation through dense vegetation remains a challenging task. In this scenario, a route is likely to have high self-similarity and be subject to changing lighting conditions and motion over uneven terrain, and the effects of wind on leaves increase the variability of the input. We used a bioinspired event camera on a terrestrial robot to collect visual sequences along routes in natural outdoor environments and applied a neural algorithm for spatiotemporal memory that is closely based on a known neural circuit in the insect brain. We show that this method is plausible to support route recognition for visual navigation and more robust than SeqSLAM when evaluated on repeated runs on the same route or routes with small lateral offsets. By encoding memory in a spiking neural network running on a neuromorphic computer, our model can evaluate visual familiarity in real time from event camera footage.

Perturbation of 3D nuclear architecture, epigenomic dysregulation and aging, and cannabinoid synaptopathy reconfigures conceptualization of cannabinoid pathophysiology: part 1-aging and epigenomics

Much recent attention has been directed toward the spatial organization of the cell nucleus and the manner in which three-dimensional topologically associated domains and transcription factories are epigenetically coordinated to precisely bring enhancers into close proximity with promoters to control gene expression. Twenty lines of evidence robustly implicate cannabinoid exposure with accelerated organismal and cellular aging. Aging has recently been shown to be caused by increased DNA breaks. These breaks rearrange and maldistribute the epigenomic machinery to weaken and reverse cellular differentiation, cause genome-wide DNA demethylation, reduce gene transcription, and lead to the inhibition of developmental pathways, which contribute to the progressive loss of function and chronic immune stimulation that characterize cellular aging. Both cell lineage-defining superenhancers and the superanchors that control them are weakened. Cannabis exposure phenocopies the elements of this process and reproduces DNA and chromatin breakages, reduces the DNA, RNA protein and histone synthesis, interferes with the epigenomic machinery controlling both DNA and histone modifications, induces general DNA hypomethylation, and epigenomically disrupts both the critical boundary elements and the cohesin motors that create chromatin loops. This pattern of widespread interference with developmental programs and relative cellular dedifferentiation (which is pro-oncogenic) is reinforced by cannabinoid impairment of intermediate metabolism (which locks in the stem cell-like hyper-replicative state) and cannabinoid immune stimulation (which perpetuates and increases aging and senescence programs, DNA damage, DNA hypomethylation, genomic instability, and oncogenesis), which together account for the diverse pattern of teratologic and carcinogenic outcomes reported in recent large epidemiologic studies in Europe, the USA, and elsewhere. It also accounts for the prominent aging phenotype observed clinically in long-term cannabis use disorder and the 20 characteristics of aging that it manifests. Increasing daily cannabis use, increasing use in pregnancy, and exponential dose-response effects heighten the epidemiologic and clinical urgency of these findings. Together, these findings indicate that cannabinoid genotoxicity and epigenotoxicity are prominent features of cannabis dependence and strongly indicate coordinated multiomics investigations of cannabinoid genome-epigenome-transcriptome-metabolome, chromatin conformation, and 3D nuclear architecture. Considering the well-established exponential dose-response relationships, the diversity of cannabinoids, and the multigenerational nature of the implications, great caution is warranted in community cannabinoid penetration.

Heterogeneous receptor expression underlies non-uniform peptidergic modulation of olfaction in Drosophila

Sensory systems are dynamically adjusted according to the animal's ongoing needs by neuromodulators, such as neuropeptides. Neuropeptides are often widely-distributed throughout sensory networks, but it is unclear whether such neuropeptides uniformly modulate network activity. Here, we leverage the Drosophila antennal lobe (AL) to resolve whether myoinhibitory peptide (MIP) uniformly modulates AL processing. Despite being uniformly distributed across the AL, MIP decreases olfactory input to some glomeruli, while increasing olfactory input to other glomeruli. We reveal that a heterogeneous ensemble of local interneurons (LNs) are the sole source of AL MIP, and show that differential expression of the inhibitory MIP receptor across glomeruli allows MIP to act on distinct intraglomerular substrates. Our findings demonstrate how even a seemingly simple case of modulation can have complex consequences on network processing by acting non-uniformly within different components of the overall network.

Strength of Excitatory Inputs to Layer 3 Pyramidal Neurons During Synaptic Pruning in the Monkey Prefrontal Cortex: Relevance for the Pathogenesis of Schizophrenia

BACKGROUND

In schizophrenia, layer 3 pyramidal neurons (L3PNs) of the dorsolateral prefrontal cortex exhibit deficits in markers of excitatory synaptic inputs that are thought to disrupt the patterns of neural network activity essential for cognitive function. These deficits are usually interpreted under Irwin Feinberg's hypothesis of altered synaptic pruning, which postulates that normal periadolescent pruning, thought to preferentially eliminate weak/immature synapses, is altered in schizophrenia. However, it remains unknown whether periadolescent pruning on L3PNs in the primate dorsolateral prefrontal cortex selectively eliminates weak excitatory synapses or uniformly eliminates excitatory synapses across the full distribution of synaptic strengths.

METHODS

To distinguish between these alternative models of synaptic pruning, we assessed the densities of dendritic spines, the site of most excitatory inputs to L3PNs, and the distributions of excitatory synaptic strengths in dorsolateral prefrontal cortex L3PNs from male and female monkeys across the periadolescent period of synaptic pruning. We used patch-clamp methods in acute brain slices to record miniature excitatory synaptic currents and intracellular filling with biocytin to quantify dendritic spines.

RESULTS

On L3PNs, dendritic spines exhibited the expected age-related decline in density, but mean synaptic strength and the shape of synaptic strength distributions remained stable with age.

CONCLUSIONS

The absence of age-related differences in mean synaptic strength and synaptic strength distributions supports the model of a uniform pattern of synaptic pruning across the full range of synaptic strengths. The implications of these findings for the pathogenesis and functional consequences of dendritic spine deficits in schizophrenia are discussed.

5-MeO-DMT modifies innate behaviors and promotes structural neural plasticity in mice

Serotonergic psychedelics are gaining increasing interest as potential therapeutics for a range of mental illnesses. Compounds with short-lived subjective effects may be clinically useful because dosing time would be reduced, which may improve patient access. One short-acting psychedelic is 5-MeO-DMT, which has been associated with improvement in depression and anxiety symptoms in early phase clinical studies. However, relatively little is known about the behavioral and neural mechanisms of 5-MeO-DMT, particularly the durability of its long-term effects. Here we characterized the effects of 5-MeO-DMT on innate behaviors and dendritic architecture in mice. We showed that 5-MeO-DMT induces a dose-dependent increase in head-twitch response that is shorter in duration than that induced by psilocybin at all doses tested. 5-MeO-DMT also substantially suppresses social ultrasonic vocalizations produced during mating behavior. 5-MeO-DMT produces long-lasting increases in dendritic spine density in the mouse medial frontal cortex that are driven by an elevated rate of spine formation. However, unlike psilocybin, 5-MeO-DMT did not affect the size of dendritic spines. These data provide insights into the behavioral and neural consequences underlying the action of 5-MeO-DMT and highlight similarities and differences with those of psilocybin.

Variance analysis as a method to predict the locus of plasticity at populations of non-uniform synapses

Our knowledge on synaptic transmission in the central nervous system has often been obtained by evoking synaptic responses to populations of synapses. Analysis of the variance in synaptic responses can be applied as a method to predict whether a change in synaptic responses is a consequence of altered presynaptic neurotransmitter release or postsynaptic receptors. However, variance analysis is based on binomial statistics, which assumes that synapses are uniform. In reality, synapses are far from uniform, which questions the reliability of variance analysis when applying this method to populations of synapses. To address this, we used an in silico model for evoked synaptic responses and compared variance analysis outcomes between populations of uniform versus non-uniform synapses. This simulation revealed that variance analysis produces similar results irrespectively of the grade of uniformity of synapses. We put this variance analysis to the test with an electrophysiology experiment using a model system for which the loci of plasticity are well established: the effect of amyloid-β on synapses. Variance analysis correctly predicted that postsynaptically produced amyloid-β triggered predominantly a loss of synapses and a minor reduction of postsynaptic currents in remaining synapses with little effect on presynaptic release probability. We propose that variance analysis can be reliably used to predict the locus of synaptic changes for populations of non-uniform synapses.

Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila

The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper 1 , this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ~130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database 2 . Crucially, this resource includes 4,552 cell types. 3,094 are rigorous consensus validations of cell types previously proposed in the hemibrain connectome 3 . In addition, we propose 1,458 new cell types, arising mostly from the fact that the FlyWire connectome spans the whole brain, whereas the hemibrain derives from a subvolume. Comparison of FlyWire and the hemibrain showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire as the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types proposed in the hemibrain connectome could not yet be reliably identified in the FlyWire connectome. We therefore suggest that cell types should be defined to be robust to inter-individual variation, namely as groups of cells that are quantitatively more similar to cells in a different brain than to any other cell in the same brain. Joint analysis of the FlyWire and hemibrain connectomes demonstrates the viability and utility of this new definition. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.

Synapse-specific diversity of distinct postsynaptic GluN2 subtypes defines transmission strength in spinal lamina I

The unitary postsynaptic response to presynaptic quantal glutamate release is the fundamental basis of excitatory information transfer between neurons. The view, however, of individual glutamatergic synaptic connections in a population as homogenous, fixed-strength units of neural communication is becoming increasingly scrutinized. Here, we used minimal stimulation of individual glutamatergic afferent axons to evoke single synapse resolution postsynaptic responses from central sensory lamina I neurons in an ex vivo adult rat spinal slice preparation. We detected unitary events exhibiting a NMDA receptor component with distinct kinetic properties across synapses conferred by specific GluN2 subunit composition, indicative of GluN2 subtype-based postsynaptic heterogeneity. GluN2A, 2A and 2B, or 2B and 2D synaptic predominance functioned on distinct lamina I neuron types to narrowly, intermediately, or widely tune, respectively, the duration of evoked unitary depolarization events from resting membrane potential, which enabled individual synapses to grade differentially depolarizing steps during temporally patterned afferent input. Our results lead to a model wherein a core locus of proteomic complexity prevails at this central glutamatergic sensory synapse that involves distinct GluN2 subtype configurations. These findings have major implications for subthreshold integrative capacity and transmission strength in spinal lamina I and other CNS regions.

A simple method for poly-D-lysine coating to enhance adhesion and maturation of primary cortical neuron cultures in vitro

Introduction

Glass coverslips are used as a substrate since Harrison's initial nerve cell culture experiments in 1910. In 1974, the first study of brain cells seeded onto polylysine (PL) coated substrate was published. Usually, neurons adhere quickly to PL coating. However, maintaining cortical neurons in culture on PL coating for a prolonged time is challenging.

Methods

A collaborative study between chemical engineers and neurobiologists was conducted to find a simple method to enhance neuronal maturation on poly-D-lysine (PDL). In this work, a simple protocol to coat PDL efficiently on coverslips is presented, characterized, and compared to a conventional adsorption method. We studied the adhesion and maturation of primary cortical neurons with various morphological and functional approaches, including phase contrast microscopy, immunocytochemistry, scanning electron microscopy, patch clamp recordings, and calcium imaging.

Results

We observed that several parameters of neuronal maturation are influenced by the substrate: neurons develop more dense and extended networks and synaptic activity is enhanced, when seeded on covalently bound PDL compared to adsorbed PDL.

Discussion

Hence, we established reproducible and optimal conditions enhancing maturation of primary cortical neurons in vitro. Our method allows higher reliability and yield of results and could also be profitable for laboratories using PL with other cell types.

The temporal pattern of synaptic activation determines the longevity of structural plasticity at dendritic spines

Learning is thought to involve physiological and structural changes at individual synapses. Synaptic plasticity has predominantly been studied using regular stimulation patterns, but neuronal activity in the brain normally follows a Poisson distribution. We used two-photon imaging and glutamate uncaging to investigate the structural plasticity of single dendritic spines using naturalistic activation patterns sampled from a Poisson distribution. We showed that naturalistic activation patterns elicit structural plasticity that is both NMDAR and protein synthesis-dependent. Furthermore, we uncovered that the longevity of structural plasticity is dependent on the temporal structure of the naturalistic pattern. Finally, we found that during the delivery of the naturalistic activity, spines underwent rapid structural growth that predicted the longevity of plasticity. This was not observed with regularly spaced activity. These data reveal that different temporal organizations of the same number of synaptic stimulations can produce rather distinct short and long-lasting structural plasticity.

Alterations in synapses and mitochondria induced by acute or chronic intermittent hypoxia in the pre-Bötzinger complex of rats: an ultrastructural triple-labeling study with immunocytochemistry and histochemistry

Introduction

The pre-Bötzinger complex (pre-BötC), a kernel of inspiratory rhythmogenesis, is a heterogeneous network with excitatory glutamatergic and inhibitory GABAergic and glycinergic neurons. Inspiratory rhythm generation relies on synchronous activation of glutamatergic neuron, whilst inhibitory neurons play a critical role in shaping the breathing pattern, endowing the rhythm with flexibility in adapting to environmental, metabolic, and behavioral needs. Here we report ultrastructural alterations in excitatory, asymmetric synapses (AS) and inhibitory, symmetric synapses (SS), especially perforated synapses with discontinuous postsynaptic densities (PSDs) in the pre-BötC in rats exposed to daily acute intermittent hypoxia (dAIH) or chronic (C) IH.

Methods

We utilized for the first time a combination of somatostatin (SST) and neurokinin 1 receptor (NK1R) double immunocytochemistry with cytochrome oxidase histochemistry, to reveal synaptic characteristics and mitochondrial dynamic in the pre-BötC.

Results

We found perforated synapses with synaptic vesicles accumulated in distinct pools in apposition to each discrete PSD segments. dAIH induced significant increases in the PSD size of macular AS, and the proportion of perforated synapses. AS were predominant in the dAIH group, whereas SS were in a high proportion in the CIH group. dAIH significantly increased SST and NK1R expressions, whereas CIH led to a decrease. Desmosome-like contacts (DLC) were characterized for the first time in the pre-BötC. They were distributed alongside of synapses, especially SS. Mitochondria appeared in more proximity to DLC than synapses, suggestive of a higher energy demand of the DLC. Findings of single spines with dual AS and SS innervation provide morphological evidence of excitation-inhibition interplay within a single spine in the pre-BötC. In particular, we characterized spine-shaft microdomains of concentrated synapses coupled with mitochondrial positioning that could serve as a structural basis for synchrony of spine-shaft communication. Mitochondria were found within spines and ultrastructural features of mitochondrial fusion and fission were depicted for the first time in the pre-BötC.

Conclusion

We provide ultrastructural evidence of excitation-inhibition synapses in shafts and spines, and DLC in association with synapses that coincide with mitochondrial dynamic in their contribution to respiratory plasticity in the pre-BötC.

Mechanisms of simultaneous linear and nonlinear computations at the mammalian cone photoreceptor synapse

Neurons enhance their computational power by combining linear and nonlinear transformations in extended dendritic trees. Rich, spatially distributed processing is rarely associated with individual synapses, but the cone photoreceptor synapse may be an exception. Graded voltages temporally modulate vesicle fusion at a cone's ~20 ribbon active zones. Transmitter then flows into a common, glia-free volume where bipolar cell dendrites are organized by type in successive tiers. Using super-resolution microscopy and tracking vesicle fusion and postsynaptic responses at the quantal level in the thirteen-lined ground squirrel, Ictidomys tridecemlineatus, we show that certain bipolar cell types respond to individual fusion events in the vesicle stream while other types respond to degrees of locally coincident events, creating a gradient across tiers that are increasingly nonlinear. Nonlinearities emerge from a combination of factors specific to each bipolar cell type including diffusion distance, contact number, receptor affinity, and proximity to glutamate transporters. Complex computations related to feature detection begin within the first visual synapse.

Advanced Temporally-Spatially Precise Technologies for On-Demand Neurological Disorder Intervention

Temporal-spatial precision has attracted increasing attention for the clinical intervention of neurological disorders (NDs) to mitigate adverse effects of traditional treatments and achieve point-of-care medicine. Inspiring steps forward in this field have been witnessed in recent years, giving the credit to multi-discipline efforts from neurobiology, bioengineering, chemical materials, artificial intelligence, and so on, exhibiting valuable clinical translation potential. In this review, the latest progress in advanced temporally-spatially precise clinical intervention is highlighted, including localized parenchyma drug delivery, precise neuromodulation, as well as biological signal detection to trigger closed-loop control. Their clinical potential in both central and peripheral nervous systems is illustrated meticulously related to typical diseases. The challenges relative to biosafety and scaled production as well as their future perspectives are also discussed in detail. Notably, these intelligent temporally-spatially precision intervention systems could lead the frontier in the near future, demonstrating significant clinical value to support billions of patients plagued with NDs.

How synaptic strength, short-term plasticity, and input synchrony contribute to neuronal spike output

Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few 'strong' synapses are formed between similarly tuned cells, suggesting they determine spiking output. This raises the question of how other computational primitives, including 'background' activity from the many 'weak' synapses, short-term plasticity, and temporal factors contribute to spiking. We used paired recordings and extracellular stimulation experiments to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of synaptic connections formed between pyramidal neurons in layer 2/3 (L2/3) of barrel cortex. While net short-term plasticity was weak, strong synaptic connections were exclusively depressing. Importantly, we found no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the excitatory L2/3 circuitry, which was constrained by our experiments and published in vivo data. We found that strong synapses were substantially depressed during ongoing activation and their ability to evoke correlated spiking primarily depended on their high temporal synchrony and high firing rates observed in vivo. However, despite this depression, their larger EPSP amplitudes strongly amplified information transfer and responsiveness. Thus, our results contribute to a nuanced framework of how cortical neurons exploit synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into spikes.

Functional connectomics reveals general wiring rule in mouse visual cortex

To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types ¹ . Neural cell types have previously been defined by morphology ^{2, 3} , electrophysiology ^{4, 5} , transcriptomic expression ^6-8 , connectivity ^9-13 , or even a combination of such modalities ^14-16 . More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells ^17-20 . Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex ²¹ . It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type ^{22, 23} known to be Somatostatin positive (Sst+) ^{24, 25} , were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.

Mechanical transmission at spine synapses: Short-term potentiation and working memory

Do dendritic spines, which comprise the postsynaptic component of most excitatory synapses, exist only for their structural dynamics, receptor trafficking, and chemical and electrical compartmentation? The answer is no. Simultaneous investigation of both spine and presynaptic terminals has recently revealed a novel feature of spine synapses. Spine enlargement pushes the presynaptic terminals with muscle-like force and augments the evoked glutamate release for up to 20 min. We now summarize the evidence that such mechanical transmission shares critical features in common with short-term potentiation (STP) and may represent the cellular basis of short-term and working memory. Thus, spine synapses produce the force of learning to leave structural traces for both short and long-term memories.

Strong and reliable synaptic communication between pyramidal neurons in adult human cerebral cortex

Synaptic transmission constitutes the primary mode of communication between neurons. It is extensively studied in rodent but not human neocortex. We characterized synaptic transmission between pyramidal neurons in layers 2 and 3 using neurosurgically resected human middle temporal gyrus (MTG, Brodmann area 21), which is part of the distributed language circuitry. We find that local connectivity is comparable with mouse layer 2/3 connections in the anatomical homologue (temporal association area), but synaptic connections in human are 3-fold stronger and more reliable (0% vs 25% failure rates, respectively). We developed a theoretical approach to quantify properties of spinous synapses showing that synaptic conductance and voltage change in human dendritic spines are 3-4-folds larger compared with mouse, leading to significant NMDA receptor activation in human unitary connections. This model prediction was validated experimentally by showing that NMDA receptor activation increases the amplitude and prolongs decay of unitary excitatory postsynaptic potentials in human but not in mouse connections. Since NMDA-dependent recurrent excitation facilitates persistent activity (supporting working memory), our data uncovers cortical microcircuit properties in human that may contribute to language processing in MTG.

Mapping thalamic innervation to individual L2/3 pyramidal neurons and modeling their 'readout' of visual input

The thalamus is the main gateway for sensory information from the periphery to the mammalian cerebral cortex. A major conundrum has been the discrepancy between the thalamus's central role as the primary feedforward projection system into the neocortex and the sparseness of thalamocortical synapses. Here we use new methods, combining genetic tools and scalable tissue expansion microscopy for whole-cell synaptic mapping, revealing the number, density and size of thalamic versus cortical excitatory synapses onto individual layer 2/3 (L2/3) pyramidal cells (PCs) of the mouse primary visual cortex. We find that thalamic inputs are not only sparse, but remarkably heterogeneous in number and density across individual dendrites and neurons. Most surprising, despite their sparseness, thalamic synapses onto L2/3 PCs are smaller than their cortical counterparts. Incorporating these findings into fine-scale, anatomically faithful biophysical models of L2/3 PCs reveals how individual neurons with sparse and weak thalamocortical synapses, embedded in small heterogeneous neuronal ensembles, may reliably 'read out' visually driven thalamic input.

Plasticity of postsynaptic nanostructure

The postsynaptic density (PSD) of excitatory synapses is built from a wide variety of scaffolding proteins, receptors, and signaling molecules that collectively orchestrate synaptic transmission. Seminal work over the past decades has led to the identification and functional characterization of many PSD components. In contrast, we know far less about how these constituents are assembled within synapses, and how this organization contributes to synapse function. Notably, recent evidence from high-resolution microscopy studies and in silico models, highlights the importance of the precise subsynaptic structure of the PSD for controlling the strength of synaptic transmission. Even further, activity-driven changes in the distribution of glutamate receptors are acknowledged to contribute to long-term changes in synaptic efficacy. Thus, defining the mechanisms that drive structural changes within the PSD are important for a molecular understanding of synaptic transmission and plasticity. Here, we review the current literature on how the PSD is organized to mediate basal synaptic transmission and how synaptic activity alters the nanoscale organization of synapses to sustain changes in synaptic strength.

The Development of Synapses in Mouse and Macaque Primary Sensory Cortices

We report that the rate of synapse development in primary sensory cortices of mice and macaques is unrelated to lifespan, as was previously thought. We analyzed 28,084 synapses over multiple developmental time points in both species and find, instead, that net excitatory synapse development of mouse and macaque neurons primarily increased at similar rates in the first few postnatal months, and then decreased over a span of 1-1.5 years of age. The development of inhibitory synapses differed qualitatively across species. In macaques, net inhibitory synapses first increase and then decrease on excitatory soma at similar ages as excitatory synapses. In mice, however, such synapses are added throughout life. These findings contradict the long-held belief that the cycle of synapse formation and pruning occurs earlier in shorter-lived animals. Instead, our results suggest more nuanced rules, with the development of different types of synapses following different timing rules or different trajectories across species.

Reconstruction of sparse recurrent connectivity and inputs from the nonlinear dynamics of neuronal networks

Reconstructing the recurrent structural connectivity of neuronal networks is a challenge crucial to address in characterizing neuronal computations. While directly measuring the detailed connectivity structure is generally prohibitive for large networks, we develop a novel framework for reverse-engineering large-scale recurrent network connectivity matrices from neuronal dynamics by utilizing the widespread sparsity of neuronal connections. We derive a linear input-output mapping that underlies the irregular dynamics of a model network composed of both excitatory and inhibitory integrate-and-fire neurons with pulse coupling, thereby relating network inputs to evoked neuronal activity. Using this embedded mapping and experimentally feasible measurements of the firing rate as well as voltage dynamics in response to a relatively small ensemble of random input stimuli, we efficiently reconstruct the recurrent network connectivity via compressive sensing techniques. Through analogous analysis, we then recover high dimensional natural stimuli from evoked neuronal network dynamics over a short time horizon. This work provides a generalizable methodology for rapidly recovering sparse neuronal network data and underlines the natural role of sparsity in facilitating the efficient encoding of network data in neuronal dynamics.

Synaptic and Gradual Conductance Switching Behaviors in CeO2/Nb-SrTiO3 Heterojunction Memristors for Electrocardiogram Signal Recognition

The synaptic properties of memristors have been widely studied. However, researchers are still committed to solving various challenges, including the study of highly reliable memristors with comprehensive synaptic functions and memristors that simulate highly complex neurological learning rules. In this work, we report a CeO₂/Nb-SrTiO₃ heterojunction memristor whose conductance could be gradually tuned under both positive and negative pulse trains. Due to the gradual conductance switching behavior and the high switching ratio (10⁵), the CeO₂/Nb-SrTiO₃ heterojunction memristor could dutifully mimic biosynaptic functions, including excitatory/inhibitory postsynaptic current (EPSC/IPSC), paired-pulse facilitation and depression (PPF/PPD), spike amplitude-dependent plasticity (SADP), spike duration-dependent plasticity (SDDP), spike rate-dependent plasticity (SRDP), paired/triplet spiking-time-dependent plasticity (STDP), and Bienenstock-Cooper-Munro (BCM) rules. Moreover, a convolutional neural network based on the memristors is constructed to identify the electrocardiogram (ECG) data sets to realize the diagnosis of diseases with a recognition accuracy of 93%. Besides, the recognition accuracy of the handwriting digit reaches 96%. These studies broaden the research scope of high-level synaptic behavior and lay a foundation for the future full synaptic memristor networks.

Bayesian reconstruction of memories stored in neural networks from their connectivity

The advent of comprehensive synaptic wiring diagrams of large neural circuits has created the field of connectomics and given rise to a number of open research questions. One such question is whether it is possible to reconstruct the information stored in a recurrent network of neurons, given its synaptic connectivity matrix. Here, we address this question by determining when solving such an inference problem is theoretically possible in specific attractor network models and by providing a practical algorithm to do so. The algorithm builds on ideas from statistical physics to perform approximate Bayesian inference and is amenable to exact analysis. We study its performance on three different models, compare the algorithm to standard algorithms such as PCA, and explore the limitations of reconstructing stored patterns from synaptic connectivity.

SELENOM Knockout Induces Synaptic Deficits and Cognitive Dysfunction by Influencing Brain Glucose Metabolism

Selenium, a trace element associated with memory impairment and glucose metabolism, mainly exerts its function through selenoproteins. SELENOM is a selenoprotein located in the endoplasmic reticulum (ER) lumen. Our study demonstrates for the first time that SELENOM knockout decreases synaptic plasticity and causes memory impairment in 10-month-old mice. In addition, SELENOM knockout causes hyperglycaemia and disturbs glucose metabolism, which is essential for synapse formation and transmission in the brain. Further research reveals that SELENOM knockout leads to inhibition of the brain insulin signaling pathway [phosphatidylinositol 3-kinase (PI3K)/AKT/mTOR/p70 S6 kinase pathway], which may impair synaptic plasticity in mice. High-fat diet (HFD) feeding suppresses the brain insulin signaling pathway in SELENOM knockout mice and leads to earlier onset of cognitive impairment at 5 months of age. In general, our study demonstrates that SELENOM knockout induces synaptic deficits via the brain insulin signaling pathway, thus leading to cognitive dysfunction in mice. These data strongly suggest that SELENOM plays a vital role in brain glucose metabolism and contributes substantially to synaptic plasticity.

Dendritic Spines in Learning and Memory: From First Discoveries to Current Insights

The central nervous system is composed of neural ensembles, and their activity patterns are neural correlates of cognitive functions. Those ensembles are networks of neurons connected to each other by synapses. Most neurons integrate synaptic signal through a remarkable subcellular structure called spine. Dendritic spines are protrusions whose diverse shapes make them appear as a specific neuronal compartment, and they have been the focus of studies for more than a century. Soon after their first description by Ramón y Cajal, it has been hypothesized that spine morphological changes could modify neuronal connectivity and sustain cognitive abilities. Later studies demonstrated that changes in spine density and morphology occurred in experience-dependent plasticity during development, and in clinical cases of mental retardation. This gave ground for the assumption that dendritic spines are the particular locus of cerebral plasticity. With the discovery of synaptic long-term potentiation, a research program emerged with the aim to establish whether dendritic spine plasticity could explain learning and memory. The development of live imaging methods revealed on the one hand that dendritic spine remodeling is compatible with learning process and, on the other hand, that their long-term stability is compatible with lifelong memories. Furthermore, the study of the mechanisms of spine growth and maintenance shed new light on the rules of plasticity. In behavioral paradigms of memory, spine formation or elimination and morphological changes were found to correlate with learning. In a last critical step, recent experiments have provided evidence that dendritic spines play a causal role in learning and memory.

Regulation of Presynaptic Release Machinery by Cell Adhesion Molecules

The synapse is a highly specialized asymmetric structure that transmits and stores information in the brain. The size of pre- and postsynaptic structures and function is well coordinated at the individual synapse level. For example, large postsynaptic dendritic spines have a larger postsynaptic density with higher α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) number on their surface, while juxtaposing presynaptic terminals have a larger active zone and higher release probability. This indicates that pre- and postsynaptic domains bidirectionally communicate to coordinate assembly of specific molecules on both sides of the synaptic cleft. Cell adhesion molecules (CAMs) that localize at synapses form transsynaptic protein interactions across the synaptic cleft and play important roles in synapse formation and regulation. The extracellular domain of CAMs is essential for specific synapse formation and function. In contrast, the intracellular domain is necessary for binding with synaptic molecules and signal transduction. Therefore, CAMs play an essential role on synapse function and structure. In fact, ample evidence indicates that transsynaptic CAMs instruct and modulate functions at presynaptic sites. This chapter focuses on transsynaptic protein interactions that regulate presynaptic functions emphasizing the role of neuronal CAMs and the intracellular mechanism of their regulation.