Local connectivity and synaptic dynamics in mouse and human neocortex

Entrainment by transcranial alternating current stimulation: Insights from models of cortical oscillations and dynamical systems theory

Signature of neuronal oscillations can be found in nearly every brain function. However, abnormal oscillatory activity is linked with several brain disorders. Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that can potentially modulate neuronal oscillations and influence behavior both in health and disease. Yet, a complete understanding of how interacting networks of neurons are affected by tACS remains elusive. Entrainment effects by which tACS synchronizes neuronal oscillations is one of the main hypothesized mechanisms, as evidenced in animals and humans. Computational models of cortical oscillations may shed light on the entrainment effects of tACS, but current modeling studies lack specific guidelines to inform experimental investigations. This study addresses the existing gap in understanding the mechanisms of tACS effects on rhythmogenesis within the brain by providing a comprehensive overview of both theoretical and experimental perspectives. We explore the intricate interactions between oscillators and periodic stimulation through the lens of dynamical systems theory. Subsequently, we present a synthesis of experimental findings that demonstrate the effects of tACS on both individual neurons and collective oscillatory patterns in animal models and humans. Our review extends to computational investigations that elucidate the interplay between tACS and neuronal dynamics across diverse cortical network models. To illustrate these concepts, we conclude with a simple oscillatory neuron model, showcasing how fundamental theories of oscillatory behavior derived from dynamical systems, such as phase response of neurons to external perturbation, can account for the entrainment effects observed with tACS. Studies reviewed here render the necessity of integrated experimental and computational approaches for effective neuromodulation by tACS in health and disease.

Phylogenetic divergence of GABAB receptor signaling in neocortical networks over adult life

Cortical circuit activity is controlled by GABA-mediated inhibition in a spatiotemporally restricted manner. GABAB receptor (GABABR) signalling exerts powerful slow inhibition that controls synaptic, dendritic and neuronal activity. But, how GABABRs contribute to circuit-level inhibition over the lifespan of rodents and humans is poorly understood. In this study, we quantitatively determined the functional contribution of GABABR signalling to pre- and postsynaptic domains in rat and human cortical principal cells. We find that postsynaptic GABABR differentially control pyramidal cell activity within the cortical column as a function of age in rodents, but minimally change over adult life in humans. Presynaptic GABABRs exert stronger inhibition in humans than rodents. Pre- and postsynaptic GABABRs contribute to co-ordination of local information processing in a layer- and species-dependent manner. Finally, we show that GABABR signalling is elevated in patients that have received the anti-seizure medication Levetiracetam. These data directly increase our knowledge of translationally relevant local circuit dynamics, with direct impact on understanding the role of GABABRs in the treatment of seizure disorders.

Untangling stability and gain modulation in cortical circuits with multiple interneuron classes

Synaptic inhibition is the mechanistic backbone of a suite of cortical functions, not the least of which are maintaining network stability and modulating neuronal gain. In cortical models with a single inhibitory neuron class, network stabilization and gain control work in opposition to one another - meaning high gain coincides with low stability and vice versa. It is now clear that cortical inhibition is diverse, with molecularly distinguished cell classes having distinct positions within the cortical circuit. We analyze circuit models with pyramidal neurons (E) as well as parvalbumin (PV) and somatostatin (SOM) expressing interneurons. We show how, in E - PV - SOM recurrently connected networks, SOM-mediated modulation can lead to simultaneous increases in neuronal gain and network stability. Our work exposes how the impact of a modulation mediated by SOM neurons depends critically on circuit connectivity and the network state.

Disrupted ErbB4 splicing with region-specific severity across the cortical visuospatial working memory network in schizophrenia

Background

Visuospatial working memory (vsWM) depends on gamma oscillations generated in multiple areas of a cortical network, including dorsolateral prefrontal (DLPFC), posterior parietal (PPC), visual association (V2), and primary visual (V1) cortices. Gamma oscillations require parvalbumin-expressing interneurons (PVI), and deficient gamma power in the vsWM network of schizophrenia (SZ) is thought to arise from lower PVI activity. We previously proposed that PVI activity, and activity-dependent PV levels, are regulated by shifts in splicing of erb-b2 receptor tyrosine kinase 4 (ErbB4) transcripts between major variants that enhance PVI activity and inactive minor variants. Here, we investigated the region-specific pattern of this splicing shift across the vsWM network and its alterations in SZ.

Methods

Levels of ErbB4 splice variants and PV mRNA were quantified from 16 pairs of unaffected comparison (UC) and SZ subjects across DLPFC, PPC, V2, and V1.

Results

In UC, the major ErbB4 variants showed progressive enrichment relative to minor variants from the rostral to caudal regions. In SZ, this gradient was disrupted by abnormal shifts toward minor variants, with the magnitude of shifts greater in caudal regions. These major-to-minor splicing shifts correlated with lower PV mRNA levels across all regions in SZ.

Conclusion

Greater enrichment of major variants in caudal areas suggests spatially regulated molecular mechanism supporting region-specific levels of PVI activity across the vsWM network. In SZ, the region-specific magnitude of splicing shift to minor variants may drive reduced PVI activity throughout the vsWM network, leading to a network-wide dysfunction that contributes to cognitive impairments.

Accelerated signal propagation speed in human neocortical dendrites

Human-specific cognitive abilities depend on information processing in the cerebral cortex, where the neurons are significantly larger and their processes longer and sparser compared to rodents. We found that, in synaptically connected layer 2/3 pyramidal cells (L2/3 PCs), the delay in signal propagation from soma to soma is similar in humans and rodents. To compensate for the longer processes of neurons, membrane potential changes in human axons and/or dendrites must propagate faster. Axonal and dendritic recordings show that the propagation speed of action potentials (APs) is similar in human and rat axons, but the forward propagation of excitatory postsynaptic potentials (EPSPs) and the backward propagation of APs are 26 and 47% faster in human dendrites, respectively. Experimentally-based detailed biophysical models have shown that the key factor responsible for the accelerated EPSP propagation in human cortical dendrites is the large conductance load imposed at the soma by the large basal dendritic tree. Additionally, larger dendritic diameters and differences in cable and ion channel properties in humans contribute to enhanced signal propagation. Our integrative experimental and modeling study provides new insights into the scaling rules that help maintain information processing speed albeit the large and sparse neurons in the human cortex.

Lateralized local circuit tuning in female mouse auditory cortex

Most offspring are born helpless, requiring intense caregiving from parents especially during the first few days of neonatal life. For many species, infant cries are a primary signal used by parents to provide caregiving. Previously we and others documented how maternal left auditory cortex rapidly becomes sensitized to pup calls over hours of parental experience, enabled by oxytocin. The speed and robustness of this maternal plasticity suggests cortical pre-tuning or initial bias for pup call stimulus features. Here we examine the circuit basis of left-lateralized tuning to vocalization features with whole-cell recordings in brain slices. We found that layer 2/3 pyramidal cells of female left auditory cortex show selective suppression of inhibitory inputs with repeated stimulation at the fundamental pup call rate (inter-stimulus interval ∼150 msec) in pup-naïve females and expanded with maternal experience. However, optogenetic stimulation of cortical inhibitory cells showed that inputs from somatostatin-positive and oxytocin-receptor-expressing interneurons were less suppressed at these rates. This suggested that disynaptic inhibition rather than monosynaptic depression was a major mechanism underlying pre-tuning of cortical excitatory neurons, confirmed with simulations. Thus cortical interneuron specializations can augment neuroplasticity mechanisms to ensure fast appropriate caregiving in response to infant cries.

Inkjet-printed transparent electrodes: Design, characterization, and initial in vivo evaluation for brain stimulation

Electrical stimulation is a powerful tool for investigating and modulating brain activity, as well as for treating neurological disorders. However, understanding the precise effects of electrical stimulation on neural activity has been hindered by limitations in recording neuronal responses near the stimulating electrode, such as stimulation artifacts in electrophysiology or obstruction of the field of view in imaging. In this study, we introduce a novel stimulation device fabricated from conductive polymers that is transparent and therefore compatible with optical imaging techniques. The device is manufactured using a combination of microfabrication and inkjet printing techniques and is flexible, allowing better adherence to the brain's natural curvature. We characterized the electrical and optical properties of the electrodes, focusing on the trade-off between the maximum current that can be delivered and optical transmittance. We found that a 1 mm diameter, 350 nm thick PEDOT:PSS electrode could be used to apply a maximum current of 130 μA while maintaining 84% transmittance (approximately 50% under 2-photon imaging conditions). We then evaluated the electrode performance in the brain of an anesthetized mouse by measuring the electric field with a nearby recording electrode and found values up to 30 V/m. Finally, we combined experimental data with a finite-element model of the in vivo experimental setup to estimate the distribution of the electric field underneath the electrode in the mouse brain. Our findings indicate that the device can generate an electric field as high as 300 V/m directly beneath the electrode, demonstrating its potential for studying and manipulating neural activity using a range of electrical stimulation techniques relevant to human applications. Overall, this work presents a promising approach for developing versatile new tools to apply and study electrical brain stimulation.

NEURD offers automated proofreading and feature extraction for connectomics

We are in the era of millimetre-scale electron microscopy volumes collected at nanometre resolution1,2. Dense reconstruction of cellular compartments in these electron microscopy volumes has been enabled by recent advances in machine learning3-6. Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post hoc proofreading is still required to generate large connectomes that are free of merge and split errors. The elaborate 3D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Here, building on existing open source software for mesh manipulation, we present Neural Decomposition (NEURD), a software package that decomposes meshed neurons into compact and extensively annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state-of-the-art automated proofreading of merge errors, cell classification, spine detection, axonal-dendritic proximities and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers.

Integrating multimodal data to understand cortical circuit architecture and function

In recent years there has been a tremendous growth in new technologies that allow large-scale investigation of different characteristics of the nervous system at an unprecedented level of detail. There is a growing trend to use combinations of these new techniques to determine direct links between different modalities. In this Perspective, we focus on the mouse visual cortex, as this is one of the model systems in which much progress has been made in the integration of multimodal data to advance understanding. We review several approaches that allow integration of data regarding various properties of cortical cell types, connectivity at the level of brain areas, cell types and individual cells, and functional neural activity in vivo. The increasingly crucial contributions of computation and theory in analyzing and systematically modeling data are also highlighted. Together with open sharing of data, tools and models, integrative approaches are essential tools in modern neuroscience for improving our understanding of the brain architecture, mechanisms and function.

Lateral inhibition in V1 controls neural and perceptual contrast sensitivity

Lateral inhibition is a central principle in sensory system function. It is thought to operate by the activation of inhibitory neurons that restrict the spatial spread of sensory excitation. However, the neurons, computations and mechanisms underlying cortical lateral inhibition remain debated, and its importance for perception remains unknown. Here we show that lateral inhibition from parvalbumin neurons in mouse primary visual cortex reduced neural and perceptual sensitivity to visual contrast in a uniform subtractive manner, whereas lateral inhibition from somatostatin neurons more effectively changed the slope (or gain) of neural and perceptual contrast sensitivity. A neural circuit model, anatomical tracing and direct subthreshold measurements indicated that the larger spatial footprint for somatostatin versus parvalbumin synaptic inhibition explains this difference. Together, these results define cell-type-specific computational roles for lateral inhibition in primary visual cortex, and establish their unique consequences on sensitivity to contrast, a fundamental aspect of the visual world.

Connectomics of predicted Sst transcriptomic types in mouse visual cortex

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between them1. Neural cell types have previously been defined by morphology2,3, electrophysiology4, transcriptomic expression5,6, connectivity7-9 or a combination of such modalities10-12. The Patch-seq technique enables the characterization of morphology, electrophysiology and transcriptomic properties from individual cells13-15. These properties were integrated to define 28 inhibitory, morpho-electric-transcriptomic (MET) cell types in mouse visual cortex16, which do not include synaptic connectivity. Conversely, large-scale electron microscopy (EM) enables morphological reconstruction and a near-complete description of a neuron's local synaptic connectivity, but does not include transcriptomic or electrophysiological information. Here, we leveraged morphological information from Patch-seq to predict the transcriptomically defined cell subclass and/or MET-type of inhibitory neurons within a large-scale EM dataset. We further analysed Martinotti cells-a somatostatin (Sst)-positive17 morphological cell type18,19-which were classified successfully into Sst MET-types with distinct axon myelination and synaptic output connectivity patterns. We demonstrate that morphological features can be used to link cell types across experimental modalities, enabling further comparison of connectivity to gene expression and electrophysiology. We observe unique connectivity rules for predicted Sst cell types.

Inhibitory specificity from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties1. Synaptic connectivity shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here we used millimetre-scale volumetric electron microscopy2 to investigate the connectivity of all inhibitory neurons across a densely segmented neuronal population of 1,352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibition with more than 70,000 synapses. Inspired by classical neuroanatomy, we classified inhibitory neurons based on targeting of dendritic compartments and developed an excitatory neuron classification based on dendritic reconstructions with whole-cell maps of synaptic input. Single-cell connectivity showed a class of disinhibitory specialist that targets basket cells. Analysis of inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of spatially intermingled subpopulations. Inhibitory targeting was organized into 'motif groups', diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking contemporary multimodal neuronal atlases with the cortical wiring diagram.

Uncertainty estimation with prediction-error circuits

Neural circuits continuously integrate noisy sensory stimuli with predictions that often do not perfectly match, requiring the brain to combine these conflicting feedforward and feedback inputs according to their uncertainties. However, how the brain tracks both stimulus and prediction uncertainty remains unclear. Here, we show that a hierarchical prediction-error network can estimate both the sensory and prediction uncertainty with positive and negative prediction-error neurons. Consistent with prior hypotheses, we demonstrate that neural circuits rely more on predictions when sensory inputs are noisy and the environment is stable. By perturbing inhibitory interneurons within the prediction-error circuit, we reveal their role in uncertainty estimation and input weighting. Finally, we link our model to biased perception, showing how stimulus and prediction uncertainty contribute to the contraction bias.

VIP-to-SST Cell Circuit Motif Shows Differential Short-Term Plasticity across Sensory Areas of Mouse Cortex

Inhibition of GABAergic interneurons has been found to critically fine-tune the excitation-inhibition balance of the cortex. Inhibition is mediated by many connectivity motifs formed by GABAergic neurons. One such motif is the inhibition of somatostatin (SST)-expressing neurons by vasoactive intestinal polypeptide (VIP)-expressing neurons. We studied the synaptic properties of layer (L) 2/3 VIP cells onto L4 SST cells in somatosensory (S1) and visual (V1) cortices of mice of either sex using paired whole-cell patch-clamp recordings, followed by morphological reconstructions. We identified strong differences in the morphological features of L4 SST cells, wherein cells in S1 fell into the non-Martinotti cell (nMC) subclass, while in V1 presented with Martinotti cell (MC)-like features. Approximately 40-45% of tested SST cells were inhibited by VIP cells in both cortices. While unitary connectivity properties of the VIP-to-nMC and VIP-to-MC motifs were comparable, we observed stark differences in short-term plasticity. During high-frequency stimulation of both motifs, some connections showed short-term facilitation while others showed a stable response, with a fraction of VIP-to-nMC connections showing short-term depression. We thus provide evidence that VIP cells target morphological subclasses of SST cells differentially, forming cell-type-specific inhibitory motifs.

Computational protocol for modeling and analyzing synaptic dynamics using SRPlasticity

Transient changes in synaptic strength, known as short-term plasticity (STP), play a fundamental role in neuronal communication. Here, we present a protocol for using SRPlasticity, a software package that implements a computational model of STP. SRPlasticity supports automatic characterization of electrophysiological data and simulation of synaptic responses. We describe steps for installing and utilizing SRPlasticity, preprocessing data, fitting models, and simulating responses. We then detail procedures for analyzing spike response plasticity (SRP) model parameters to infer functional groupings of STP. For complete details on the use and execution of this protocol, please refer to Rossbroich et al.1 and Beninger et al.2.

Interleaving asynchronous and synchronous activity in balanced cortical networks with short-term synaptic depression

Cortical populations are in a broadly asynchronous state that is sporadically interrupted by brief epochs of coordinated population activity. Cortical models are at a loss to explain this combination of states. At one extreme are network models where recurrent inhibition dynamically stabilizes an asynchronous low activity state. While these networks are widely used, they cannot produce the coherent population-wide activity that is reported in a variety of datasets. At the other extreme are models in which strong recurrent excitation that is quickly tamed by short term synaptic depression between excitatory neurons leads to short epochs of population-wide activity. However, in these networks, inhibition plays only a perfunctory role in network stability, which is at odds with many reports across cortex. In this study we analyze spontaneously active in vitro preparations of primary auditory cortex that show dynamics that are emblematic of this mixture of states. To capture this complex population activity we use firing rate-based networks as well as biologically realistic networks of spiking neuron models where large excitation is balanced by recurrent inhibition, yet we include short-term synaptic depression dynamics of the excitatory connections. These models give very rich nonlinear behavior that mimics the core features of the in vitro data, including the possibility of low frequency (2-12 Hz) rhythmic dynamics within population events. In these networks, synaptic depression enables activity fluctuations to induce a weakening of inhibitory recruitment, which in turn triggers population events. In sum, our study extends balanced network models to account for nonlinear, population-wide correlated activity, thereby providing a critical step in a mechanistic theory of realistic cortical activity.

Subthreshold moment analysis of neuronal populations driven by synchronous synaptic inputs

Even when driven by the same stimulus, neuronal responses are well-known to exhibit a striking level of spiking variability. In-vivo electrophysiological recordings also reveal a surprisingly large degree of variability at the subthreshold level. In prior work, we considered biophysically relevant neuronal models to account for the observed magnitude of membrane voltage fluctuations. We found that accounting for these fluctuations requires weak but nonzero synchrony in the spiking activity, in amount that are consistent with experimentally measured spiking correlations. Here we investigate whether such synchrony can explain additional statistical features of the measured neural activity, including neuronal voltage covariability and voltage skewness. Addressing this question involves conducting a generalized moment analysis of conductance-based neurons in response to input drives modeled as correlated jump processes. Technically, we perform such an analysis using fixed-point techniques from queuing theory that are applicable in the stationary regime of activity. We found that weak but nonzero synchrony can consistently explain the experimentally reported voltage covariance and skewness. This confirms the role of synchrony as a primary driver of cortical variability and supports that physiological neural activity emerges as a population-level phenomenon, especially in the spontaneous regime.

Subthreshold variability of neuronal populations driven by synchronous synaptic inputs

Even when driven by the same stimulus, neuronal responses are well-known to exhibit a striking level of spiking variability. In-vivo electrophysiological recordings also reveal a surprisingly large degree of variability at the subthreshold level. In prior work, we considered biophysically relevant neuronal models to account for the observed magnitude of membrane voltage fluctuations. We found that accounting for these fluctuations requires weak but nonzero synchrony in the spiking activity, in amount that are consistent with experimentally measured spiking correlations. Here we investigate whether such synchrony can explain additional statistical features of the measured neural activity, including neuronal voltage covariability and voltage skewness. Addressing this question involves conducting a generalized moment analysis of conductance-based neurons in response to input drives modeled as correlated jump processes. Technically, we perform such an analysis using fixed-point techniques from queuing theory that are applicable in the stationary regime of activity. We found that weak but nonzero synchrony can consistently explain the experimentally reported voltage covariance and skewness. This confirms the role of synchrony as a primary driver of cortical variability and supports that physiological neural activity emerges as a population-level phenomenon, especially in the spontaneous regime.

Identifying the impact of local connectivity patterns on dynamics in excitatory-inhibitory networks

Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that synaptic strengths depend on the type of neurons they connect, but are otherwise statistically independent. Recent synaptic physiology datasets however highlight the prominence of specific connectivity patterns that go well beyond what is expected from independent connections. While decades of influential research have demonstrated the strong role of the basic EI cell type structure, to which extent additional connectivity features influence dynamics remains to be fully determined. Here we examine the effects of pairwise connectivity motifs on the linear dynamics in EI networks using an analytical framework that approximates the connectivity in terms of low-rank structures. This low-rank approximation is based on a mathematical derivation of the dominant eigenvalues of the connectivity matrix and predicts the impact on responses to external inputs of connectivity motifs and their interactions with cell-type structure. Our results reveal that a particular pattern of connectivity, chain motifs, have a much stronger impact on dominant eigenmodes than other pairwise motifs. An overrepresentation of chain motifs induces a strong positive eigenvalue in inhibition-dominated networks and generates a potential instability that requires revisiting the classical excitation-inhibition balance criteria. Examining effects of external inputs, we show that chain motifs can on their own induce paradoxical responses where an increased input to inhibitory neurons leads to a decrease in their activity due to the recurrent feedback. These findings have direct implications for the interpretation of experiments in which responses to optogenetic perturbations are measured and used to infer the dynamical regime of cortical circuits.

Inhibitory cell type heterogeneity in a spatially structured mean-field model of V1

Inhibitory interneurons in the cortex are classified into cell types differing in their morphology, electrophysiology, and connectivity. Although it is known that parvalbumin (PV), somatostatin (SST), and vasoactive intestinal polypeptide-expressing neurons (VIP), the major inhibitory neuron subtypes in the cortex, have distinct modulatory effects on excitatory neurons, how heterogeneous spatial connectivity properties relate to network computations is not well understood. Here, we study the implications of heterogeneous inhibitory neurons on the dynamics and computations of spatially-structured neural networks. We develop a mean-field model of the system in order to systematically examine excitation-inhibition balance, dynamical stability, and cell-type specific gain modulations. The model incorporates three inhibitory cell types and excitatory neurons with distinct connectivity probabilities and recent evidence of long-range spatial projections of SST neurons. Position-dependent firing rate predictions are validated against simulations, and balanced solutions under Gaussian assumptions are derived from scaling arguments. Stability analysis shows that while long-range inhibitory projections in E-I circuits with a homogeneous inhibitory population result in instability, the heterogeneous network maintains stability with long-range SST projections. This suggests that a mixture of short and long-range inhibitions may be key to providing diverse computations while maintaining stability. We further find that conductance-based synaptic transmissions are necessary to reproduce experimentally observed cell-type-specific gain modulations of inhibition by PV and SST neurons. The mechanisms underlying cell-type-specific gain changes are elucidated using linear response theory. Our theoretical approach offers insight into the computational function of cell-type-specific and distance-dependent network structure.

Local Differences in Network Organization in the Auditory and Parietal Cortex, Revealed with Single Neuron Activation

The structure of local circuits is highly conserved across the cortex, yet the spatial and temporal properties of population activity differ fundamentally in sensory-level and association-level areas. In the sensory cortex, population activity has a shorter timescale and decays sharply over distance, supporting a population code for the fine-scale features of sensory stimuli. In the association cortex, population activity has a longer timescale and spreads over wider distances, a code that is suited to holding information in memory and driving behavior. We tested whether these differences in activity dynamics could be explained by differences in network structure. We targeted photostimulations to single excitatory neurons of layer 2/3, while monitoring surrounding population activity using two-photon calcium imaging. Experiments were performed in the auditory (AC) and posterior parietal cortex (PPC) within the same mice of both sexes, which also expressed a red fluorophore in somatostatin-expressing interneurons (SOM). In both cortical regions, photostimulations resulted in a spatially restricted zone of positive influence on neurons closely neighboring the targeted neuron and a more spatially diffuse zone of negative influence affecting more distant neurons (akin to a network-level "suppressive surround"). However, the relative spatial extents of positive and negative influence were different in AC and PPC. In PPC, the central zone of positive influence was wider, but the negative suppressive surround was more narrow than in AC, which could account for the larger-scale network dynamics in PPC. The more narrow central positive influence zone and wider suppressive surround in AC could serve to sharpen sensory representations.

Efficient coding in biophysically realistic excitatory-inhibitory spiking networks

The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.

A deep learning framework for automated and generalized synaptic event analysis

Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring synaptic events carry fundamental information about synaptic function and plasticity. However, their stochastic nature and low signal-to-noise ratio present major challenges for the reliable and consistent analysis. Here, we introduce miniML, a supervised deep learning-based method for accurate classification and automated detection of spontaneous synaptic events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event analysis methods in terms of both precision and recall. miniML enables precise detection and quantification of synaptic events in electrophysiological recordings. We demonstrate that the deep learning approach generalizes easily to diverse synaptic preparations, different electrophysiological and optical recording techniques, and across animal species. miniML provides not only a comprehensive and robust framework for automated, reliable, and standardized analysis of synaptic events, but also opens new avenues for high-throughput investigations of neural function and dysfunction.

Organizational Principles of the Primate Cerebral Cortex at the Single-Cell Level

The primate cerebral cortex, the major organ for cognition, consists of an immense number of neurons. However, the organizational principles governing these neurons remain unclear. By accessing the single-cell spatial transcriptome of over 25 million neuron cells across the entire macaque cortex, it is discovered that the distribution of neurons within cortical layers is highly non-random. Strikingly, over three-quarters of these neurons are located in distinct neuronal clusters. Within these clusters, different cell types tend to collaborate rather than function independently. Typically, excitatory neuron clusters mainly consist of excitatory-excitatory combinations, while inhibitory clusters primarily contain excitatory-inhibitory combinations. Both cluster types have roughly equal numbers of neurons in each layer. Importantly, most excitatory and inhibitory neuron clusters form spatial partnerships, indicating a balanced local neuronal network and correlating with specific functional regions. These organizational principles are conserved across mouse cortical regions. These findings suggest that different brain regions of the cortex may exhibit similar mechanisms at the neuronal population level.

Predictive filtering of sensory response via orbitofrontal top-down input

Habituation is a crucial sensory filtering mechanism whose dysregulation can lead to a continuously intense world in disorders with sensory overload. While habituation is considered to require top-down predictive signaling to suppress irrelevant inputs, the exact brain loci storing the internal predictive model and the circuit mechanisms of sensory filtering remain unclear. We found that daily neural habituation in the primary auditory cortex (A1) was reversed by inactivation of the orbitofrontal cortex (OFC). Top-down projections from the ventrolateral OFC, but not other frontal areas, carried predictive signals that grew with daily sound experience and suppressed A1 via somatostatin-expressing inhibitory neurons. Thus, prediction signals from the OFC cancel out behaviorally irrelevant anticipated stimuli by generating their "negative images" in sensory cortices.

Postsynaptic spiking determines anti-Hebbian LTD in visual cortex basket cells

Long-term plasticity at pyramidal cell to basket cell (PC → BC) synapses is important for the functioning of cortical microcircuits. It is well known that at neocortical PC → PC synapses, dendritic calcium (Ca2+) dynamics signal coincident pre-and postsynaptic spiking which in turn triggers long-term potentiation (LTP). However, the link between dendritic Ca2+ dynamics and long-term plasticity at PC → BC synapses of primary visual cortex (V1) is not as well known. Here, we explored if PC → BC synaptic plasticity in developing V1 is sensitive to postsynaptic spiking. Two-photon (2P) Ca2+ imaging revealed that action potentials (APs) in dendrites of V1 layer-5 (L5) BCs back-propagated decrementally but actively to the location of PC → BC putative synaptic contacts. Pairing excitatory inputs with postsynaptic APs elicited dendritic Ca2+ supralinearities for pre-before-postsynaptic but not post-before-presynaptic temporal ordering, suggesting that APs could impact synaptic plasticity. In agreement, extracellular stimulation as well as high-throughput 2P optogenetic mapping of plasticity both revealed that pre-before-postsynaptic but not post-before-presynaptic pairing resulted in anti-Hebbian long-term depression (LTD). Our results demonstrate that V1 BC dendritic Ca2+ nonlinearities and synaptic plasticity at PC → BC connections are both sensitive to somatic spiking.

Inactivation of the CMAH gene and deficiency of Neu5Gc play a role in human brain evolution

During human evolution, some genes were lost or silenced from the genome of hominins. These missing genes might be the key to the evolution of humans' unique cognitive skills. An inactivation mutation in CMP-N-acetylneuraminic acid hydroxylase (CMAH) was the result of natural selection. The inactivation of CMAH protected our ancestors from some pathogens and reduced the level of N-glycolylneuraminic acid (Neu5Gc) in brain tissue. Interestingly, the low level of Neu5Gc promoted the development of brain tissue, which may have played a role in human evolution. As a xenoantigen, Neu5Gc may have been involved in brain evolution by affecting neural conduction, neuronal development, and aging.

Modeling the Emergence of Circuit Organization and Function during Development

Developing neural circuits show unique patterns of spontaneous activity and structured network connectivity shaped by diverse activity-dependent plasticity mechanisms. Based on extensive experimental work characterizing patterns of spontaneous activity in different brain regions over development, theoretical and computational models have played an important role in delineating the generation and function of individual features of spontaneous activity and their role in the plasticity-driven formation of circuit connectivity. Here, we review recent modeling efforts that explore how the developing cortex and hippocampus generate spontaneous activity, focusing on specific connectivity profiles and the gradual strengthening of inhibition as the key drivers behind the observed developmental changes in spontaneous activity. We then discuss computational models that mechanistically explore how different plasticity mechanisms use this spontaneous activity to instruct the formation and refinement of circuit connectivity, from the formation of single neuron receptive fields to sensory feature maps and recurrent architectures. We end by highlighting several open challenges regarding the functional implications of the discussed circuit changes, wherein models could provide the missing step linking immature developmental and mature adult information processing capabilities.

Neocortical somatostatin neuron diversity in cognition and learning

Somatostatin-expressing (SST) neurons are a major class of electrophysiologically and morphologically distinct inhibitory cells in the mammalian neocortex. Transcriptomic data suggest that this class can be divided into multiple subtypes that are correlated with morpho-electric properties. At the same time, availability of transgenic tools to identify and record from SST neurons in awake, behaving mice has stimulated insights about their response properties and computational function. Neocortical SST neurons are regulated by sleep and arousal, attention, and novelty detection, and show marked response plasticity during learning. Recent studies suggest that subtype-specific analysis of SST neurons may be critical for understanding their complex roles in cortical function. In this review, we discuss and synthesize recent advances in understanding the diversity, circuit integration, and functional properties of this important group of GABAergic neurons.

Contextual modulation emerges by integrating feedforward and feedback processing in mouse visual cortex

Sensory systems use context to infer meaning. Accordingly, context profoundly influences neural responses to sensory stimuli. However, a cohesive understanding of the circuit mechanisms governing contextual effects across different stimulus conditions is still lacking. Here we present a unified circuit model of mouse visual cortex that accounts for the main standard forms of contextual modulation. This data-driven and biologically realistic circuit, including three primary inhibitory cell types, sheds light on how bottom-up, top-down, and recurrent inputs are integrated across retinotopic space to generate contextual effects in layer 2/3. We establish causal relationships between neural responses, geometrical features of the inputs, and the connectivity patterns. The model not only reveals how a single canonical cortical circuit differently modulates sensory response depending on context but also generates multiple testable predictions, offering insights that apply to broader neural circuitry.

Layer-specific control of inhibition by NDNF interneurons

Neuronal processing of external sensory input is shaped by internally generated top-down information. In the neocortex, top-down projections primarily target layer 1, which contains NDNF (neuron-derived neurotrophic factor)-expressing interneurons and the dendrites of pyramidal cells. Here, we investigate the hypothesis that NDNF interneurons shape cortical computations in an unconventional, layer-specific way, by exerting presynaptic inhibition on synapses in layer 1 while leaving synapses in deeper layers unaffected. We first confirm experimentally that in the auditory cortex, synapses from somatostatin-expressing (SOM) onto NDNF neurons are indeed modulated by ambient Gamma-aminobutyric acid (GABA). Shifting to a computational model, we then show that this mechanism introduces a distinct mutual inhibition motif between NDNF interneurons and the synaptic outputs of SOM interneurons. This motif can control inhibition in a layer-specific way and introduces competition between NDNF and SOM interneurons for dendritic inhibition onto pyramidal cells on different timescales. NDNF interneurons can thereby control cortical information flow by redistributing dendritic inhibition from fast to slow timescales and by gating different sources of dendritic inhibition.

Exact linear theory of perturbation response in a space- and feature-dependent cortical circuit model

What are the principles that govern the responses of cortical networks to their inputs and the emergence of these responses from recurrent connectivity? Recent experiments have probed these questions by measuring cortical responses to two-photon optogenetic perturbations of single cells in the mouse primary visual cortex. A robust theoretical framework is needed to determine the implications of these responses for cortical recurrence. Here we propose a novel analytical approach: a formulation of the dependence of cell-type-specific connectivity on spatial distance that yields an exact solution for the linear perturbation response of a model with multiple cell types and space- and feature-dependent connectivity. Importantly and unlike previous approaches, the solution is valid in regimes of strong as well as weak intra-cortical coupling. Analysis reveals the structure of connectivity implied by various features of single-cell perturbation responses, such as the surprisingly narrow spatial radius of nearby excitation beyond which inhibition dominates, the number of transitions between mean excitation and inhibition thereafter, and the dependence of these responses on feature preferences. Comparison of these results to existing optogenetic perturbation data yields constraints on cell-type-specific connection strengths and their tuning dependence. Finally, we provide experimental predictions regarding the response of inhibitory neurons to single-cell perturbations and the modulation of perturbation response by neuronal gain; the latter can explain observed differences in the feature-tuning of perturbation responses in the presence vs. absence of visual stimuli.

Human hippocampal CA3 uses specific functional connectivity rules for efficient associative memory

Our brain has remarkable computational power, generating sophisticated behaviors, storing memories over an individual's lifetime, and producing higher cognitive functions. However, little of our neuroscience knowledge covers the human brain. Is this organ truly unique, or is it a scaled version of the extensively studied rodent brain? Combining multicellular patch-clamp recording with expansion-based superresolution microscopy and full-scale modeling, we determined the cellular and microcircuit properties of the human hippocampal CA3 region, a fundamental circuit for memory storage. In contrast to neocortical networks, human hippocampal CA3 displayed sparse connectivity, providing a circuit architecture that maximizes associational power. Human synapses showed unique reliability, high precision, and long integration times, exhibiting both species- and circuit-specific properties. Together with expanded neuronal numbers, these circuit characteristics greatly enhanced the memory storage capacity of CA3. Our results reveal distinct microcircuit properties of the human hippocampus and begin to unravel the inner workings of our most complex organ.

Alterations in Prefrontal Cortical Somatostatin Neurons in Schizophrenia: Evidence for Weaker Inhibition of Pyramidal Neuron Dendrites

BACKGROUND

Certain cognitive processes require inhibition provided by the somatostatin (SST) class of GABA (gamma-aminobutyric acid) neurons in the dorsolateral prefrontal cortex (DLPFC). This inhibition onto pyramidal neuron dendrites depends on both SST and GABA signaling. Although SST messenger RNA (mRNA) levels are lower in the DLPFC in schizophrenia, it is not known whether SST neurons exhibit alterations in the capacity to synthesize GABA, principally via the 67-kilodalton isoform of glutamic acid decarboxylase (GAD67).

METHODS

GAD67 and SST mRNA levels were quantified in individual SST neurons using fluorescence in situ hybridization in DLPFC layers 2/superficial 3, where SST neurons are enriched, in individuals with schizophrenia (n = 46) and unaffected comparison (n = 46) individuals. Findings were compared with GAD67 and SST mRNA levels quantified by polymerase chain reaction and to final educational attainment, a proxy measure for cognitive functioning.

RESULTS

GAD67 (F1,84 = 13.1, p = .0005, Cohen's d = -0.78) and SST (F1,84 = 10.1, p = .002, Cohen's d = -0.64) mRNA levels in SST neurons were lower in schizophrenia, with no group differences in the relative density of SST neurons (F1,84 = 0.21, p = .65). A presynaptic index of dendritic inhibition, derived by summing the alterations in GAD67 and SST mRNAs, was lower in 80.4% of individuals with schizophrenia and was associated with final educational attainment (adjusted odds ratio = 1.44, p = .022).

CONCLUSIONS

Deficits in both GAD67 and SST mRNAs within SST neurons indicate that these neurons have a markedly reduced ability to inhibit postsynaptic pyramidal neuron dendrites in schizophrenia. These alterations likely contribute to cognitive dysfunction in schizophrenia.

Efficient coding in biophysically realistic excitatory-inhibitory spiking networks

The principle of efficient coding posits that sensory cortical networks are designed to encode maximal sensory information with minimal metabolic cost. Despite the major influence of efficient coding in neuroscience, it has remained unclear whether fundamental empirical properties of neural network activity can be explained solely based on this normative principle. Here, we derive the structural, coding, and biophysical properties of excitatory-inhibitory recurrent networks of spiking neurons that emerge directly from imposing that the network minimizes an instantaneous loss function and a time-averaged performance measure enacting efficient coding. We assumed that the network encodes a number of independent stimulus features varying with a time scale equal to the membrane time constant of excitatory and inhibitory neurons. The optimal network has biologically-plausible biophysical features, including realistic integrate-and-fire spiking dynamics, spike-triggered adaptation, and a non-specific excitatory external input. The excitatory-inhibitory recurrent connectivity between neurons with similar stimulus tuning implements feature-specific competition, similar to that recently found in visual cortex. Networks with unstructured connectivity cannot reach comparable levels of coding efficiency. The optimal ratio of excitatory vs inhibitory neurons and the ratio of mean inhibitory-to-inhibitory vs excitatory-to-inhibitory connectivity are comparable to those of cortical sensory networks. The efficient network solution exhibits an instantaneous balance between excitation and inhibition. The network can perform efficient coding even when external stimuli vary over multiple time scales. Together, these results suggest that key properties of biological neural networks may be accounted for by efficient coding.

CONSTRUCTING BIOLOGICALLY CONSTRAINED RNNS VIA DALE'S BACKPROP AND TOPOLOGICALLY-INFORMED PRUNING

Recurrent neural networks (RNNs) have emerged as a prominent tool for modeling cortical function, and yet their conventional architecture is lacking in physiological and anatomical fidelity. In particular, these models often fail to incorporate two crucial biological constraints: i) Dale's law, i.e., sign constraints that preserve the "type" of projections from individual neurons, and ii) Structured connectivity motifs, i.e., highly sparse yet defined connections amongst various neuronal populations. Both constraints are known to impair learning performance in artificial neural networks, especially when trained to perform complicated tasks; but as modern experimental methodologies allow us to record from diverse neuronal populations spanning multiple brain regions, using RNN models to study neuronal interactions without incorporating these fundamental biological properties raises questions regarding the validity of the insights gleaned from them. To address these concerns, our work develops methods that let us train RNNs which respect Dale's law whilst simultaneously maintaining a specific sparse connectivity pattern across the entire network. We provide mathematical grounding and guarantees for our approaches incorporating both types of constraints, and show empirically that our models match the performance of RNNs trained without any constraints. Finally, we demonstrate the utility of our methods for inferring multi-regional interactions by training RNN models of the cortical network to reconstruct 2-photon calcium imaging data during visual behaviour in mice, whilst enforcing data-driven, cell-type specific connectivity constraints between various neuronal populations spread across multiple cortical layers and brain areas. In doing so, we find that the interactions inferred by our model corroborate experimental findings in agreement with the theory of predictive coding, thus validating the applicability of our methods.

Distinct Inhibitory Neurons Differently Shape Neuronal Codes for Sound Intensity in the Auditory Cortex

Cortical circuits contain multiple types of inhibitory neurons which shape how information is processed within neuronal networks. Here, we asked whether somatostatin-expressing (SST) and vasoactive intestinal peptide-expressing (VIP) inhibitory neurons have distinct effects on population neuronal responses to noise bursts of varying intensities. We optogenetically stimulated SST or VIP neurons while simultaneously measuring the calcium responses of populations of hundreds of neurons in the auditory cortex (AC) of male and female awake, head-fixed mice to sounds. Upon SST neuronal activation, noise burst representations became more discrete for different intensity levels, relying on cell identity rather than strength. By contrast, upon VIP neuronal activation, noise bursts of different intensity levels activated overlapping neuronal populations, albeit at different response strengths. At the single-cell level, SST and VIP neuronal activation differentially modulated the response-level curves of monotonic and nonmonotonic neurons. SST neuronal activation effects were consistent with a shift of the neuronal population responses toward a more localist code with different cells responding to sounds of different intensities. By contrast, VIP neuronal activation shifted responses toward a more distributed code, in which sounds of different intensity levels are encoded in the relative response of similar populations of cells. These results delineate how distinct inhibitory neurons in the AC dynamically control cortical population codes. Different inhibitory neuronal populations may be recruited under different behavioral demands, depending on whether categorical or invariant representations are advantageous for the task.

Principles of visual cortex excitatory microcircuit organization

Synapse-specific connectivity and dynamics determine microcircuit function but are challenging to explore with classic paired recordings due to their low throughput. We therefore implemented optomapping, a ∼100-fold faster two-photon optogenetic method. In mouse primary visual cortex (V1), we optomapped 30,454 candidate inputs to reveal 1,790 excitatory inputs to pyramidal, basket, and Martinotti cells. Across these cell types, log-normal distribution of synaptic efficacies emerged as a principle. For pyramidal cells, optomapping reproduced the canonical circuit but unexpectedly uncovered that the excitation of basket cells concentrated to layer 5 and that of Martinotti cells dominated in layer 2/3. The excitation of basket cells was stronger and reached farther than the excitation of pyramidal cells, which may promote stability. Short-term plasticity surprisingly depended on cortical layer in addition to target cell. Finally, optomapping revealed an overrepresentation of shared inputs for interconnected layer-6 pyramidal cells. Thus, by resolving the throughput problem, optomapping uncovered hitherto unappreciated principles of V1 structure.

Understanding GABAergic synapse diversity and its implications for GABAergic pharmacotherapy

Despite the substantial contribution of disruptions in GABAergic inhibitory neurotransmission to the etiology of psychiatric, neurodevelopmental, and neurodegenerative disorders, surprisingly few drugs targeting the GABAergic system are currently available, partly due to insufficient understanding of circuit-specific GABAergic synapse biology. In addition to GABA receptors, GABAergic synapses contain an elaborate organizational protein machinery that regulates the properties of synaptic transmission. Until recently, this machinery remained largely unexplored, but key methodological advances have now led to the identification of a wealth of new GABAergic organizer proteins. Notably, many of these proteins appear to function only at specific subsets of GABAergic synapses, creating a diversity of organizer complexes that may serve as circuit-specific targets for pharmacotherapies. The present review aims to summarize the methodological developments that underlie this newfound knowledge and provide a current overview of synapse-specific GABAergic organizer complexes, as well as outlining future avenues and challenges in translating this knowledge into clinical applications.

A neuronal least-action principle for real-time learning in cortical circuits

One of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neuronal least-action principle for cortical processing of sensory streams to produce appropriate behavioral outputs in real time. The principle postulates that the voltage dynamics of cortical pyramidal neurons prospectively minimizes the local somato-dendritic mismatch error within individual neurons. For output neurons, the principle implies minimizing an instantaneous behavioral error. For deep network neurons, it implies the prospective firing to overcome integration delays and correct for possible output errors right in time. The neuron-specific errors are extracted in the apical dendrites of pyramidal neurons through a cortical microcircuit that tries to explain away the feedback from the periphery, and correct the trajectory on the fly. Any motor output is in a moving equilibrium with the sensory input and the motor feedback during the ongoing sensory-motor transform. Online synaptic plasticity reduces the somatodendritic mismatch error within each cortical neuron and performs gradient descent on the output cost at any moment in time. The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic laws for global real-time computation and learning in the brain.

Of mice and men: Dendritic architecture differentiates human from mice neuronal networks

The organizational principles that distinguish the human brain from other species have been a long-standing enigma in neuroscience. Focusing on the uniquely evolved human cortical layers 2 and 3, we computationally reconstruct the cortical architecture for mice and humans. We show that human pyramidal cells form highly complex networks, demonstrated by the increased number and simplex dimension compared to mice. This is surprising because human pyramidal cells are much sparser in the cortex. We show that the number and size of neurons fail to account for this increased network complexity, suggesting that another morphological property is a key determinant of network connectivity. Topological comparison of dendritic structure reveals much higher perisomatic (basal and oblique) branching density in human pyramidal cells. Using topological tools we quantitatively show that this neuronal structural property directly impacts network complexity, including the formation of a rich subnetwork structure. We conclude that greater dendritic complexity, a defining attribute of human L2 and 3 neurons, may provide the human cortex with enhanced computational capacity and cognitive flexibility.

Manganese exposure leads to depressive-like behavior through disruption of the Gln-Glu-GABA metabolic cycle

There is a correlation between long-term manganese (Mn) exposure and the Parkinson's-like disease (PD), with depression as an early symptom of PD. However, the direct relationship between Mn exposure and depression, and the mechanisms involved, remain unclear. We found that Mn exposure led to depressive-like behavior and mild cognitive impairment in mice, with Mn primarily accumulating in the cornu ammonis 3 (CA3) area of the hippocampus. Mice displayed a reduction in neuronal dendritic spines and damage to astrocytes specifically in the CA3 area. Spatial metabolomics revealed that Mn downregulated glutamic acid decarboxylase 1 (GAD1) expression in astrocytes, disrupting the Glutamine-Glutamate-γ-aminobutyric acid (GlnGluGABA) metabolic cycle in the hippocampus, leading to neurotoxicity. We established an in vitro astrocyte Gad1 overexpression (OEX) model and found that the cultured medium from Gad1 OEX astrocytes reversed neuronal synaptic damage and the expression of gamma-aminobutyric acid (GABA) related receptors. Using the astrocyte Gad1 OEX mouse model, results showed that OEX of Gad1 ameliorated depressive-like behavior and cognitive dysfunction in mice. These findings provide new insight into the important role of GAD1 mediated GlnGluGABA metabolism disorder in Mn exposure induced depressive-like behavior. This study offers a novel sight to understanding abnormal emotional states following central nervous system damage induced by Mn exposure.

Transcriptomic cell-type specificity of local cortical circuits

Complex neocortical functions rely on networks of diverse excitatory and inhibitory neurons. While local connectivity rules between major neuronal subclasses have been established, the specificity of connections at the level of transcriptomic subtypes remains unclear. We introduce single transcriptome assisted rabies tracing (START), a method combining monosynaptic rabies tracing and single-nuclei RNA sequencing to identify transcriptomic cell types, providing inputs to defined neuron populations. We employ START to transcriptomically characterize inhibitory neurons providing monosynaptic input to 5 different layer-specific excitatory cortical neuron populations in mouse primary visual cortex (V1). At the subclass level, we observe results consistent with findings from prior studies that resolve neuronal subclasses using antibody staining, transgenic mouse lines, and morphological reconstruction. With improved neuronal subtype granularity achieved with START, we demonstrate transcriptomic subtype specificity of inhibitory inputs to various excitatory neuron subclasses. These results establish local connectivity rules at the resolution of transcriptomic inhibitory cell types.

Uncovering population contributions to the extracellular potential in the mouse visual system using Laminar Population Analysis

The local field potential (LFP), the low-frequency part of the extracellular potential, reflects transmembrane currents in the vicinity of the recording electrode. Thought mainly to stem from currents caused by synaptic input, it provides information about neural activity complementary to that of spikes, the output of neurons. However, the many neural sources contributing to the LFP, and likewise the derived current source density (CSD), can often make it challenging to interpret. Efforts to improve its interpretability have included the application of statistical decomposition tools like principal component analysis (PCA) and independent component analysis (ICA) to disentangle the contributions from different neural sources. However, their underlying assumptions of, respectively, orthogonality and statistical independence are not always valid for the various processes or pathways generating LFP. Here, we expand upon and validate a decomposition algorithm named Laminar Population Analysis (LPA), which is based on physiological rather than statistical assumptions. LPA utilizes the multiunit activity (MUA) and LFP jointly to uncover the contributions of different populations to the LFP. To perform the validation of LPA, we used data simulated with the large-scale, biophysically detailed model of mouse V1 developed by the Allen Institute. We find that LPA can identify laminar positions within V1 and the temporal profiles of laminar population firing rates from the MUA. We also find that LPA can estimate the salient current sinks and sources generated by feedforward input from the lateral geniculate nucleus (LGN), recurrent activity in V1, and feedback input from the lateromedial (LM) area of visual cortex. LPA identifies and distinguishes these contributions with a greater accuracy than the alternative statistical decomposition methods, PCA and ICA. The contributions from different cortical layers within V1 could however not be robustly separated and identified with LPA. This is likely due to substantial synchrony in population firing rates across layers, which may be reduced with other stimulus protocols in the future. Lastly, we also demonstrate the application of LPA on experimentally recorded MUA and LFP from 24 animals in the publicly available Visual Coding dataset. Our results suggest that LPA can be used both as a method to estimate positions of laminar populations and to uncover salient features in LFP/CSD contributions from different populations.

Molecular mechanisms of the specialization of human synapses in the neocortex

Synapses of the neocortex specialized during human evolution to develop over extended timescales, process vast amounts of information and increase connectivity, which is thought to underlie our advanced social and cognitive abilities. These features reflect species-specific regulations of neuron and synapse cell biology. However, despite growing understanding of the human genome and the brain transcriptome at the single-cell level, linking human-specific genetic changes to the specialization of human synapses has remained experimentally challenging. In this review, we describe recent progress in characterizing divergent morphofunctional and developmental properties of human synapses, and we discuss new insights into the underlying molecular mechanisms. We also highlight intersections between evolutionary innovations and disorder-related dysfunctions at the synapse.

Integrated multimodal cell atlas of Alzheimer's disease

Alzheimer's disease (AD) is the leading cause of dementia in older adults. Although AD progression is characterized by stereotyped accumulation of proteinopathies, the affected cellular populations remain understudied. Here we use multiomics, spatial genomics and reference atlases from the BRAIN Initiative to study middle temporal gyrus cell types in 84 donors with varying AD pathologies. This cohort includes 33 male donors and 51 female donors, with an average age at time of death of 88 years. We used quantitative neuropathology to place donors along a disease pseudoprogression score. Pseudoprogression analysis revealed two disease phases: an early phase with a slow increase in pathology, presence of inflammatory microglia, reactive astrocytes, loss of somatostatin+ inhibitory neurons, and a remyelination response by oligodendrocyte precursor cells; and a later phase with exponential increase in pathology, loss of excitatory neurons and Pvalb+ and Vip+ inhibitory neuron subtypes. These findings were replicated in other major AD studies.

Patch-walking, a coordinated multi-pipette patch clamp for efficiently finding synaptic connections

Significant technical challenges exist when measuring synaptic connections between neurons in living brain tissue. The patch clamping technique, when used to probe for synaptic connections, is manually laborious and time-consuming. To improve its efficiency, we pursued another approach: instead of retracting all patch clamping electrodes after each recording attempt, we cleaned just one of them and reused it to obtain another recording while maintaining the others. With one new patch clamp recording attempt, many new connections can be probed. By placing one pipette in front of the others in this way, one can 'walk' across the mouse brain slice, termed 'patch-walking.' We performed 136 patch clamp attempts for two pipettes, achieving 71 successful whole cell recordings (52.2%). Of these, we probed 29 pairs (i.e. 58 bidirectional probed connections) averaging 91 μm intersomatic distance, finding three connections. Patch-walking yields 80-92% more probed connections, for experiments with 10-100 cells than the traditional synaptic connection searching method.

Learning-dependent gating of hippocampal inputs by frontal interneurons

The hippocampus is a brain region that is essential for the initial encoding of episodic memories. However, the consolidation of these memories is thought to occur in the neocortex, under guidance of the hippocampus, over the course of days and weeks. Communication between the hippocampus and the neocortex during hippocampal sharp wave-ripple oscillations is believed to be critical for this memory consolidation process. Yet, the synaptic and circuit basis of this communication between brain areas is largely unclear. To address this problem, we perform in vivo whole-cell patch-clamp recordings in the frontal neocortex and local field potential recordings in CA1 of head-fixed mice exposed to a virtual-reality environment. In mice trained in a goal-directed spatial task, we observe a depolarization in frontal principal neurons during hippocampal ripple oscillations. Both this ripple-associated depolarization and goal-directed task performance can be disrupted by chemogenetic inactivation of somatostatin-positive (SOM+) interneurons. In untrained mice, a ripple-associated depolarization is not observed, but it emerges when frontal parvalbumin-positive (PV+) interneurons are inactivated. These results support a model where SOM+ interneurons inhibit PV+ interneurons during hippocampal activity, thereby acting as a disinhibitory gate for hippocampal inputs to neocortical principal neurons during learning.

Transcriptomic Correlates of State Modulation in GABAergic Interneurons: A Cross-Species Analysis

GABAergic inhibitory interneurons comprise many subtypes that differ in their molecular, anatomical, and functional properties. In mouse visual cortex, they also differ in their modulation with an animal's behavioral state, and this state modulation can be predicted from the first principal component (PC) of the gene expression matrix. Here, we ask whether this link between transcriptome and state-dependent processing generalizes across species. To this end, we analysed seven single-cell and single-nucleus RNA sequencing datasets from mouse, human, songbird, and turtle forebrains. Despite homology at the level of cell types, we found clear differences between transcriptomic PCs, with greater dissimilarities between evolutionarily distant species. These dissimilarities arise from two factors: divergence in gene expression within homologous cell types and divergence in cell-type abundance. We also compare the expression of cholinergic receptors, which are thought to causally link transcriptome and state modulation. Several cholinergic receptors predictive of state modulation in mouse interneurons are differentially expressed between species. Circuit modelling and mathematical analyses suggest conditions under which these expression differences could translate into functional differences.

Synaptic composition, activity, mRNA translation and dynamics in combined single-synapse profiling using multimodal imaging

The function of neuronal circuits, and its perturbation by psychoactive molecules or disease-associated genetic variants, is governed by the interplay between synapse activity and synaptic protein localization and synthesis across a heterogeneous synapse population. Here, we combine in situ measurement of synaptic multiprotein compositions and activation states, synapse activity in calcium traces or glutamate spiking, and local translation of specific genes, across the same individual synapses. We demonstrate how this high-dimensional data enables identification of interdependencies in the multiprotein-activity network, and causal dissection of complex synaptic phenotypes in disease-relevant chemical and genetic NMDAR loss of function that translate in vivo . We show how this method generalizes to other subcellular systems by deriving mitochondrial protein networks, and, using support vector machines, its value in overcoming animal variability in phenotyping. Integrating multiple synapse information modalities enables deep structure-function characterization of synapse populations and their responses to genetic and chemical perturbations.