Principles of connectivity among morphologically defined cell types in adult neocortex

Inhibitory gamma-aminobutyric acidergic neurons in the anterior cingulate cortex participate in the comorbidity of pain and emotion

Pain is often comorbid with emotional disorders such as anxiety and depression. Hyperexcitability of the anterior cingulate cortex has been implicated in pain and pain-related negative emotions that arise from impairments in inhibitory gamma-aminobutyric acid neurotransmission. This review primarily aims to outline the main circuitry (including the input and output connectivity) of the anterior cingulate cortex and classification and functions of different gamma-aminobutyric acidergic neurons; it also describes the neurotransmitters/neuromodulators affecting these neurons, their intercommunication with other neurons, and their importance in mental comorbidities associated with chronic pain disorders. Improving understanding on their role in pain-related mental comorbidities may facilitate the development of more effective treatments for these conditions. However, the mechanisms that regulate gamma-aminobutyric acidergic systems remain elusive. It is also unclear as to whether the mechanisms are presynaptic or postsynaptic. Further exploration of the complexities of this system may reveal new pathways for research and drug development.

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.

Inhibition of BK channels by GABAb receptors enhances intrinsic excitability of layer 2/3 vasoactive intestinal polypeptide-expressing interneurons in mouse neocortex

GABAb receptors (GABAbRs) affect many signalling pathways, and hence the net effect of the activity of these receptors depends upon the specific ion channels that they are linked to, leading to different effects on specific neuronal populations. Typically, GABAbRs suppress neuronal activity in the cerebral cortex. Previously, we found that neocortical parvalbumin-expressing cells are strongly inhibited through GABAbRs, whereas somatostatin interneurons are immune to this modulation. Here, we employed in vitro whole-cell patch-clamp recordings to study whether GABAbRs modulate the activity of vasoactive intestinal polypeptide-expressing interneurons (VIP-INs) in layer (L) 2/3 of the mouse primary somatosensory cortex. Utilizing machine learning algorithms (hierarchical clustering and principal component analysis), we revealed that one VIP-IN cluster (about 68% of all VIP-INs) was sensitive to GABAbR activation. Paradoxically, when recordings were performed in standard conditions with high extracellular Ca2+ level, GABAbRs indirectly inhibited the activity of large conductance voltage- and calcium-activated potassium (BK) channels and reduced GABAaR-mediated inhibition, leading to an increase in intrinsic excitability of these interneurons. However, a classical inhibitory effect of GABAbRs on L2/3 VIP-INs was observed in modified artificial cerebrospinal fluid with physiological (low) Ca2+ concentration. Our results are essential for a deeper understanding of mechanisms underlying the modulation of cortical networks. KEY POINTS: Layer 2/3 vasoactive intestinal polypeptide-expressing interneurons (VIP-INs) in the mouse somatosensory cortex cluster into three electrophysiological types differentially sensitive to GABAb receptors (GABAbRs). The majority of VIP-INs (type 1, about 68% of all VIP-INs) are regulated through pre- and postsynaptic GABAbRs, while a subset of these interneurons (types 2 and 3) is controlled only presynaptically. The net effect of GABAbR activation on VIP-IN excitability depends on [Ca2+] in artificial cerebrospinal fluid. When [Ca2+] is high (2.5 mM), GABAbRs indirectly inhibit BK channels and reduce GABAaR inhibition leading to increased intrinsic excitability of type 1 VIP-INs. When [Ca2+] is low (1 mM), which is more physiological, BK channels do not regulate the intrinsic excitability of VIP-INs and thus postsynaptic GABAbRs canonically decrease the intrinsic excitability of type 1 VIP-INs.

Translaminar synchronous neuronal activity is required for columnar synaptic strengthening in the mouse neocortex

Synchronous neuronal activity is a hallmark of the developing mouse primary somatosensory cortex. While the patterns of synchronous neuronal activity in cortical layer 2/3 have been well described, the source of the robust layer 2/3 activity is still unknown. Using a novel microprism preparation and in vivo 2-photon imaging in neonatal mice, we show that synchronous neuronal activity is organized in barrel columns across layers. Monosynaptic rabies tracing and slice electrophysiology experiments reveal that layer 2/3 pyramidal neurons receive significant layer 5 inputs during the first postnatal week, and silencing layer 5 synaptic outputs results in a significant reduction in spontaneous activity, abnormal sensory-evoked activity and disrupted layer 4-layer 2/3 connectivity. Our results demonstrate that translaminar layer 5-layer 2/3 connectivity plays an important role in synchronizing the developing barrel column to ensure the strengthening of layer 4-layer 2/3 connections, supporting the formation of the canonical cortical organization in barrel cortex.

Fast-spiking parvalbumin-positive interneurons: new perspectives of treatment and future challenges in dementia

Central nervous system parvalbumin-positive interneurons (PV-INs) are crucial and highly vulnerable to various stressors. They also play a significant role in the pathological processes of many neuropsychiatric diseases, especially those associated with cognitive impairment, such as Alzheimer's disease (AD), vascular dementia (VD), Lewy body dementia, and schizophrenia. Although accumulating evidence suggests that the loss of PV-INs is associated with memory impairment in dementia, the precise molecular mechanisms remain elusive. In this review, we delve into the current evidence regarding the physiological properties of PV-INs and summarize the latest insights into how their loss contributes to cognitive decline in dementia, particularly focusing on AD and VD. Additionally, we discuss the influence of PV-INs on brain development, the variations in their characteristics across different types of dementia, and how their loss affects the etiology and progression of cognitive impairments. Ultimately, our goal is to provide a comprehensive overview of PV-INs and to consider their potential as novel therapeutic targets in dementia treatment.

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.

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.

Repetitive Sensory Stimulation Potentiates and Recruits Sensory-Evoked Cortical Population Activity

Sensory experience and learning are thought to be associated with plasticity of neocortical circuits. Repetitive sensory stimulation can induce long-term potentiation (LTP) of cortical excitatory synapses in anesthetized mice; however, it is unclear if these phenomena are associated with sustained changes in activity during wakefulness. Here we used time-lapse, calcium imaging of layer (L) 2/3 neurons in the primary somatosensory cortex (S1), in awake male mice, to assess the effects of a bout of rhythmic whisker stimulation (RWS) at a frequency by which rodents sample objects. We found that RWS induced a 1 h increase in whisker-evoked L2/3 neuronal activity in most cells. This was not observed for whiskers functionally connected to distant cortical columns. We also found that RWS could heterogeneously recruit or suppress whisker-evoked activity in different populations of neurons. Vasoactive intestinal-peptide-expressing (VIP) interneurons, which promote plasticity through disinhibition of pyramidal neurons, were found to exclusively elevate activity during RWS. These findings indicate that cortical neurons' representation of sensory input can be modulated over hours through repetitive sensory stimulation, which may be gated by activation of disinhibitory circuits.

Molecular logic for cellular specializations that initiate the auditory parallel processing pathways

The cochlear nuclear complex (CN), the starting point for all central auditory processing, encompasses a suite of neuronal cell types highly specialized for neural coding of acoustic signals. However, the molecular logic governing these specializations remains unknown. By combining single-nucleus RNA sequencing and Patch-seq analysis, we reveal a set of transcriptionally distinct cell populations encompassing all previously observed types and discover multiple hitherto unknown subtypes with anatomical and physiological identity. The resulting comprehensive cell-type taxonomy reconciles anatomical position, morphological, physiological, and molecular criteria, enabling the determination of the molecular basis of the specialized cellular phenotypes in the CN. In particular, CN cell-type identity is encoded in a transcriptional architecture that orchestrates functionally congruent expression across a small set of gene families to customize projection patterns, input-output synaptic communication, and biophysical features required for encoding distinct aspects of acoustic signals. This high-resolution account of cellular heterogeneity from the molecular to the circuit level reveals the molecular logic driving cellular specializations, thus enabling the genetic dissection of auditory processing and hearing disorders with a high specificity.

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.

Cerebral blood flow patterns induced by photoactivation based on laser speckle contrast imaging

Neurovascular coupling (NVC) is crucial for maintaining brain function and holds significant implications for diagnosing neurological disorders. However, the neuron type and spatial specificity in NVC remain poorly understood. In this study, we investigated the spatiotemporal characteristics of local cerebral blood flow (CBF) driven by excitatory (VGLUT2) and inhibitory (VGAT) neurons in the mouse sensorimotor cortex. By integrating optogenetics, wavefront modulation technology, and laser speckle contrast imaging (LSCI), we achieved precise, spatially targeted photoactivation of type-specific neurons and real-time CBF monitoring. We observed three distinct CBF response patterns across different locations: unimodal, bimodal, and biphasic. While unimodal and bimodal patterns were observed in different locations for both neuron types, the biphasic pattern was exclusive to inhibitory neurons. Our results reveal the spatiotemporal complexity of NVC across different neuron types and demonstrate our method's ability to analyze this complexity in detail.

Neuronal diversity and stereotypy at multiple scales through whole brain morphometry

We conducted a large-scale whole-brain morphometry study by analyzing 3.7 peta-voxels of mouse brain images at the single-cell resolution, producing one of the largest multi-morphometry databases of mammalian brains to date. We registered 204 mouse brains of three major imaging modalities to the Allen Common Coordinate Framework (CCF) atlas, annotated 182,497 neuronal cell bodies, modeled 15,441 dendritic microenvironments, characterized the full morphology of 1876 neurons along with their axonal motifs, and detected 2.63 million axonal varicosities that indicate potential synaptic sites. Our analyzed six levels of information related to neuronal populations, dendritic microenvironments, single-cell full morphology, dendritic and axonal arborization, axonal varicosities, and sub-neuronal structural motifs, along with a quantification of the diversity and stereotypy of patterns at each level. This integrative study provides key anatomical descriptions of neurons and their types across a multiple scales and features, contributing a substantial resource for understanding neuronal diversity in mammalian brains.

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.

Synaptic interactions between stellate cells and parvalbumin interneurons in layer 2 of the medial entorhinal cortex are organized at the scale of grid cell clusters

Interactions between excitatory and inhibitory neurons are critical to computations in cortical circuits but their organization is difficult to assess with standard electrophysiological approaches. Within the medial entorhinal cortex, representation of location by grid and other spatial cells involves circuits in layer 2 in which excitatory stellate cells interact with each other via inhibitory parvalbumin expressing interneurons. Whether this connectivity is structured to support local circuit computations is unclear. Here, we introduce strategies to address the functional organization of excitatory-inhibitory interactions using crossed Cre- and Flp-driver mouse lines to direct targeted presynaptic optogenetic activation and postsynaptic cell identification. We then use simultaneous patch-clamp recordings from postsynaptic neurons to assess their shared input from optically activated presynaptic populations. We find that extensive axonal projections support spatially organized connectivity between stellate cells and parvalbumin interneurons, such that direct connections are often, but not always, shared by nearby neurons, whereas multisynaptic interactions coordinate inputs to neurons with greater spatial separation. We suggest that direct excitatory-inhibitory synaptic interactions may operate at the scale of grid cell clusters, with local modules defined by excitatory-inhibitory connectivity, while indirect interactions may coordinate activity at the scale of grid cell modules.

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.

Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma

Prior studies have described the complex interplay that exists between glioma cells and neurons; however, the electrophysiological properties endogenous to glioma cells remain obscure. To address this, we employed Patch-sequencing (Patch-seq) on human glioma specimens and found that one-third of patched cells in IDH mutant (IDHmut) tumors demonstrate properties of both neurons and glia. To define these hybrid cells (HCs), which fire single, short action potentials, and discern if they are of tumoral origin, we developed the single cell rule association mining (SCRAM) computational tool to annotate each cell individually. SCRAM revealed that HCs possess select features of GABAergic neurons and oligodendrocyte precursor cells, and include both tumor and non-tumor cells. These studies characterize the combined electrophysiological and molecular properties of human glioma cells and describe a cell type in human glioma with unique electrophysiological and transcriptomic properties that may also exist in the non-tumor brain.

Molecular logic for cellular specializations that initiate the auditory parallel processing pathways

The cochlear nuclear complex (CN), the starting point for all central auditory processing, comprises a suite of neuronal cell types that are highly specialized for neural coding of acoustic signals, yet molecular logic governing cellular specializations remains unknown. By combining single-nucleus RNA sequencing and Patch-seq analysis, we reveal a set of transcriptionally distinct cell populations encompassing all previously observed types and discover multiple new subtypes with anatomical and physiological identity. The resulting comprehensive cell-type taxonomy reconciles anatomical position, morphological, physiological, and molecular criteria, enabling the determination of the molecular basis of the remarkable cellular phenotypes in the CN. In particular, CN cell-type identity is encoded in a transcriptional architecture that orchestrates functionally congruent expression across a small set of gene families to customize projection patterns, input-output synaptic communication, and biophysical features required for encoding distinct aspects of acoustic signals. This high-resolution account of cellular heterogeneity from the molecular to the circuit level illustrates molecular logic for cellular specializations and enables genetic dissection of auditory processing and hearing disorders with unprecedented specificity.

Reconciliation of weak pairwise spike-train correlations and highly coherent local field potentials across space

Multi-electrode arrays covering several square millimeters of neural tissue provide simultaneous access to population signals such as extracellular potentials and spiking activity of one hundred or more individual neurons. The interpretation of the recorded data calls for multiscale computational models with corresponding spatial dimensions and signal predictions. Multi-layer spiking neuron network models of local cortical circuits covering about $1\,{\text{mm}^{2}}$ have been developed, integrating experimentally obtained neuron-type-specific connectivity data and reproducing features of observed in-vivo spiking statistics. Local field potentials can be computed from the simulated spiking activity. We here extend a local network and local field potential model to an area of $4\times 4\,{\text{mm}^{2}}$, preserving the neuron density and introducing distance-dependent connection probabilities and conduction delays. We find that the upscaling procedure preserves the overall spiking statistics of the original model and reproduces asynchronous irregular spiking across populations and weak pairwise spike-train correlations in agreement with experimental recordings from sensory cortex. Also compatible with experimental observations, the correlation of local field potential signals is strong and decays over a distance of several hundred micrometers. Enhanced spatial coherence in the low-gamma band around $50\,\text{Hz}$ may explain the recent report of an apparent band-pass filter effect in the spatial reach of the local field potential.

Structural influences on synaptic plasticity: The role of presynaptic connectivity in the emergence of E/I co-tuning

Cortical neurons are versatile and efficient coding units that develop strong preferences for specific stimulus characteristics. The sharpness of tuning and coding efficiency is hypothesized to be controlled by delicately balanced excitation and inhibition. These observations suggest a need for detailed co-tuning of excitatory and inhibitory populations. Theoretical studies have demonstrated that a combination of plasticity rules can lead to the emergence of excitation/inhibition (E/I) co-tuning in neurons driven by independent, low-noise signals. However, cortical signals are typically noisy and originate from highly recurrent networks, generating correlations in the inputs. This raises questions about the ability of plasticity mechanisms to self-organize co-tuned connectivity in neurons receiving noisy, correlated inputs. Here, we study the emergence of input selectivity and weight co-tuning in a neuron receiving input from a recurrent network via plastic feedforward connections. We demonstrate that while strong noise levels destroy the emergence of co-tuning in the readout neuron, introducing specific structures in the non-plastic pre-synaptic connectivity can re-establish it by generating a favourable correlation structure in the population activity. We further show that structured recurrent connectivity can impact the statistics in fully plastic recurrent networks, driving the formation of co-tuning in neurons that do not receive direct input from other areas. Our findings indicate that the network dynamics created by simple, biologically plausible structural connectivity patterns can enhance the ability of synaptic plasticity to learn input-output relationships in higher brain areas.

NEURD offers automated proofreading and feature extraction for connectomics

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D 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. Building on existing open-source software for mesh manipulation, here we present "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, axon-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 focused on a variety of scientific questions.

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.

Behavioral state and stimulus strength regulate the role of somatostatin interneurons in stabilizing network activity

Inhibition stabilization enables cortical circuits to encode sensory signals across diverse contexts. Somatostatin-expressing (SST) interneurons are well-suited for this role through their strong recurrent connectivity with excitatory pyramidal cells. We developed a cortical circuit model predicting that SST cells become increasingly important for stabilization as sensory input strengthens. We tested this prediction in mouse primary visual cortex by manipulating excitatory input to SST cells, a key parameter for inhibition stabilization, with a novel cell-type specific pharmacological method to selectively block glutamatergic receptors on SST cells. Consistent with our model predictions, we find antagonizing glutamatergic receptors drives a paradoxical facilitation of SST cells with increasing stimulus contrast. In addition, we find even stronger engagement of SST-dependent stabilization when the mice are aroused. Thus, we reveal that the role of SST cells in cortical processing gradually switches as a function of both input strength and behavioral state.

scConfluence: single-cell diagonal integration with regularized Inverse Optimal Transport on weakly connected features

The abundance of unpaired multimodal single-cell data has motivated a growing body of research into the development of diagonal integration methods. However, the state-of-the-art suffers from the loss of biological information due to feature conversion and struggles with modality-specific populations. To overcome these crucial limitations, we here introduce scConfluence, a method for single-cell diagonal integration. scConfluence combines uncoupled autoencoders on the complete set of features with regularized Inverse Optimal Transport on weakly connected features. We extensively benchmark scConfluence in several single-cell integration scenarios proving that it outperforms the state-of-the-art. We then demonstrate the biological relevance of scConfluence in three applications. We predict spatial patterns for Scgn, Synpr and Olah in scRNA-smFISH integration. We improve the classification of B cells and Monocytes in highly heterogeneous scRNA-scATAC-CyTOF integration. Finally, we reveal the joint contribution of Fezf2 and apical dendrite morphology in Intra Telencephalic neurons, based on morphological images and scRNA.

A layered microcircuit model of somatosensory cortex with three interneuron types and cell-type-specific short-term plasticity

Three major types of GABAergic interneurons, parvalbumin-, somatostatin-, and vasoactive intestinal peptide-expressing (PV, SOM, VIP) cells, play critical but distinct roles in the cortical microcircuitry. Their specific electrophysiology and connectivity shape their inhibitory functions. To study the network dynamics and signal processing specific to these cell types in the cerebral cortex, we developed a multi-layer model incorporating biologically realistic interneuron parameters from rodent somatosensory cortex. The model is fitted to in vivo data on cell-type-specific population firing rates. With a protocol of cell-type-specific stimulation, network responses when activating different neuron types are examined. The model reproduces the experimentally observed inhibitory effects of PV and SOM cells and disinhibitory effect of VIP cells on excitatory cells. We further create a version of the model incorporating cell-type-specific short-term synaptic plasticity (STP). While the ongoing activity with and without STP is similar, STP modulates the responses of Exc, SOM, and VIP cells to cell-type-specific stimulation, presumably by changing the dominant inhibitory pathways. With slight adjustments, the model also reproduces sensory responses of specific interneuron types recorded in vivo. Our model provides predictions on network dynamics involving cell-type-specific short-term plasticity and can serve to explore the computational roles of inhibitory interneurons in sensory functions.

Expression of activity-regulated transcripts in pyramidal neurons across the cortical visuospatial working memory network in unaffected comparison individuals and individuals with schizophrenia

Visuospatial working memory (vsWM), which is impaired in schizophrenia (SZ), is mediated by multiple cortical regions including the primary (V1) and association (V2) visual, posterior parietal (PPC) and dorsolateral prefrontal (DLPFC) cortices. In these regions, parvalbumin (PV) or somatostatin (SST) GABA neurons are altered in SZ as reflected in lower levels of activity-regulated transcripts. As PV and SST neurons receive excitatory inputs from neighboring pyramidal neurons, we hypothesized that levels of activity-regulated transcripts are also lower in pyramidal neurons in these regions. Thus, we quantified levels of four activity-regulated, pyramidal neuron-selective transcripts, namely adenylate cyclase-activating polypeptide-1 (ADCYAP1), brain-derived neurotrophic factor (BDNF), neuronal pentraxin-2 (NPTX2) and neuritin-1 (NRN1) mRNAs, in V1, V2, PPC and DLPFC from unaffected comparison and SZ individuals. In SZ, BDNF and NPTX2 mRNA levels were lower across all four regions, whereas ADCYAP1 and NRN1 mRNA levels were lower in V1 and V2. The regional pattern of deficits in BDNF and NPTX2 mRNAs was similar to that in transcripts in PV and SST neurons in SZ. These findings suggest that lower activity of pyramidal neurons expressing BDNF and/or NPTX2 mRNAs might contribute to alterations in PV and SST neurons across the vsWM network in SZ.

The chemokine Cxcl14 regulates interneuron differentiation in layer I of the somatosensory cortex

Spontaneous and sensory-evoked activity sculpts developing circuits. Yet, how these activity patterns intersect with cellular programs regulating the differentiation of neuronal subtypes is not well understood. Through electrophysiological and in vivo longitudinal analyses, we show that C-X-C motif chemokine ligand 14 (Cxcl14), a gene previously characterized for its association with tumor invasion, is expressed by single-bouquet cells (SBCs) in layer I (LI) of the somatosensory cortex during development. Sensory deprivation at neonatal stages markedly decreases Cxcl14 expression. Additionally, we report that loss of function of this gene leads to increased intrinsic excitability of SBCs-but not LI neurogliaform cells-and augments neuronal complexity. Furthermore, Cxcl14 loss impairs sensory map formation and compromises the in vivo recruitment of superficial interneurons by sensory inputs. These results indicate that Cxcl14 is required for LI differentiation and demonstrate the emergent role of chemokines as key players in cortical network development.

Cortical Layer-Dependent Signaling in Cognition: Three Computational Modes of the Canonical Circuit

The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Distinct transcriptional programs define a heterogeneous neuronal ensemble for social interaction

Social interactions are encoded by the coordinated activity of heterogeneous cell types within distributed brain regions including the medial prefrontal cortex (mPFC). However, our understanding of the cell types which comprise the social ensemble has been limited by available mouse lines and reliance on single marker genes. We identified differentially active neuronal populations during social interactions by quantifying immediate-early gene (IEG) expression using snRNA-sequencing. These studies revealed that distinct prefrontal neuron populations composed of heterogeneous cell types are activated by social interaction. Evaluation of IEG expression within these recruited neuronal populations revealed cell-type and region-specific programs, suggesting that reliance on a single molecular marker is insufficient to quantify activation across all cell types. Our findings provide a comprehensive description of cell-type specific transcriptional programs invoked by social interactions and reveal insights into the neuronal populations which compose the social ensemble.

Long-range inhibition from prelimbic to cingulate areas of the medial prefrontal cortex enhances network activity and response execution

It is well established that the medial prefrontal cortex (mPFC) exerts top-down control of many behaviors, but little is known regarding how cross-talk between distinct areas of the mPFC influences top-down signaling. We performed virus-mediated tracing and functional studies in male mice, homing in on GABAergic projections whose axons are located mainly in layer 1 and that connect two areas of the mPFC, namely the prelimbic area (PrL) with the cingulate area 1 and 2 (Cg1/2). We revealed the identity of the targeted neurons that comprise two distinct types of layer 1 GABAergic interneurons, namely single-bouquet cells (SBCs) and neurogliaform cells (NGFs), and propose that this connectivity links GABAergic projection neurons with cortical canonical circuits. In vitro electrophysiological and in vivo calcium imaging studies support the notion that the GABAergic projection neurons from the PrL to the Cg1/2 exert a crucial role in regulating the activity in the target area by disinhibiting layer 5 output neurons. Finally, we demonstrated that recruitment of these projections affects impulsivity and mechanical responsiveness, behaviors which are known to be modulated by Cg1/2 activity.

Stimulus type shapes the topology of cellular functional networks in mouse visual cortex

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 different regions of mouse visual cortex. Comparing the functional connectivity patterns during different stimulus types, we made several nontrivial observations: (1) while the frequencies of different functional motifs were preserved across stimuli, the identities of the neurons within those motifs changed; (2) the degree to which functional modules are contained within a single brain region increases with stimulus complexity. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

Neurogliaform Cells Exhibit Laminar-specific Responses in the Visual Cortex and Modulate Behavioral State-dependent Cortical Activity

Neurogliaform cells are a distinct type of GABAergic cortical interneurons known for their 'volume transmission' output property. However, their activity and function within cortical circuits remain unclear. Here, we developed two genetic tools to target these neurons and examine their function in the primary visual cortex. We found that the spontaneous activity of neurogliaform cells positively correlated with locomotion. Silencing these neurons increased spontaneous activity during locomotion and impaired visual responses in L2/3 pyramidal neurons. Furthermore, the contrast-dependent visual response of neurogliaform cells varies with their laminar location and is constrained by their morphology and input connectivity. These findings demonstrate the importance of neurogliaform cells in regulating cortical behavioral state-dependent spontaneous activity and indicate that their functional engagement during visual stimuli is influenced by their laminar positioning and connectivity.

Reverse engineering neuron type-specific and type-orthogonal splicing-regulatory networks using single-cell transcriptomes

Cell type-specific alternative splicing (AS) enables differential gene isoform expression between diverse neuron types with distinct identities and functions. Current studies linking individual RNA-binding proteins (RBPs) to AS in a few neuron types underscore the need for holistic modeling. Here, we use network reverse engineering to derive a map of the neuron type-specific AS regulatory landscape from 133 mouse neocortical cell types defined by single-cell transcriptomes. This approach reliably inferred the regulons of 350 RBPs and their cell type-specific activities. Our analysis revealed driving factors delineating neuronal identities, among which we validated Elavl2 as a key RBP for MGE-specific splicing in GABAergic interneurons using an in vitro ESC differentiation system. We also identified a module of exons and candidate regulators specific for long- and short-projection neurons across multiple neuronal classes. This study provides a resource for elucidating splicing regulatory programs that drive neuronal molecular diversity, including those that do not align with gene expression-based classifications.

Neurogliaform Cells Exhibit Laminar-specific Responses in the Visual Cortex and Modulate Behavioral State-dependent Cortical Activity

Neurogliaform cells are a distinct type of GABAergic cortical interneurons known for their "volume transmission" output property. However, their activity and function within cortical circuits remain unclear. Here, we developed two genetic tools to target these neurons and examine their function in the primary visual cortex. We found that the spontaneous activity of neurogliaform cells positively correlated with locomotion. Silencing these neurons increased spontaneous activity during locomotion and impaired visual responses in L2/3 pyramidal neurons. Furthermore, the contrast-dependent visual response of neurogliaform cells varies with their laminar location and is constrained by their morphology and input connectivity. These findings demonstrate the importance of neurogliaform cells in regulating cortical behavioral state-dependent spontaneous activity and indicate that their functional engagement during visual stimuli is influenced by their laminar positioning and connectivity.

Flexible gating between subspaces in a neural network model of internally guided task switching

Behavioral flexibility relies on the brain's ability to switch rapidly between multiple tasks, even when the task rule is not explicitly cued but must be inferred through trial and error. The underlying neural circuit mechanism remains poorly understood. We investigated recurrent neural networks (RNNs) trained to perform an analog of the classic Wisconsin Card Sorting Test. The networks consist of two modules responsible for rule representation and sensorimotor mapping, respectively, where each module is comprised of a circuit with excitatory neurons and three major types of inhibitory neurons. We found that rule representation by self-sustained persistent activity across trials, error monitoring and gated sensorimotor mapping emerged from training. Systematic dissection of trained RNNs revealed a detailed circuit mechanism that is consistent across networks trained with different hyperparameters. The networks' dynamical trajectories for different rules resided in separate subspaces of population activity; the subspaces collapsed and performance was reduced to chance level when dendrite-targeting somatostatin-expressing interneurons were silenced, illustrating how a phenomenological description of representational subspaces is explained by a specific circuit mechanism.

Cell-type-specific cholinergic control of granular retrosplenial cortex with implications for angular velocity coding across brain states

Cholinergic receptor activation enables the persistent firing of cortical pyramidal neurons, providing a key cellular basis for theories of spatial navigation involving working memory, path integration, and head direction encoding. The granular retrosplenial cortex (RSG) is important for spatially-guided behaviors, but how acetylcholine impacts RSG neurons is unknown. Here, we show that a transcriptomically, morphologically, and biophysically distinct RSG cell-type - the low-rheobase (LR) neuron - has a very distinct expression profile of cholinergic muscarinic receptors compared to all other neighboring excitatory neuronal subtypes. LR neurons do not fire persistently in response to cholinergic agonists, in stark contrast to all other principal neuronal subtypes examined within the RSG and across midline cortex. This lack of persistence allows LR neuron models to rapidly compute angular head velocity (AHV), independent of cholinergic changes seen during navigation. Thus, LR neurons can consistently compute AHV across brain states, highlighting the specialized RSG neural codes supporting navigation.

Layer 1 NDNF interneurons are specialized top-down master regulators of cortical circuits

Diverse types of inhibitory interneurons (INs) impart computational power and flexibility to neocortical circuits. Whereas markers for different IN types in cortical layers 2-6 (L2-L6) have been instrumental for generating a wealth of functional insights, only the recent identification of a selective marker (neuron-derived neurotrophic factor [NDNF]) has opened comparable opportunities for INs in L1 (L1INs). However, at present we know very little about the connectivity of NDNF L1INs with other IN types, their input-output conversion, and the existence of potential NDNF L1IN subtypes. Here, we report pervasive inhibition of L2/3 INs (including parvalbumin INs and vasoactive intestinal peptide INs) by NDNF L1INs. Intersectional genetics revealed similar physiology and connectivity in the NDNF L1IN subpopulation co-expressing neuropeptide Y. Finally, NDNF L1INs prominently and selectively engage in persistent firing, a physiological hallmark disconnecting their output from the current input. Collectively, our work therefore identifies NDNF L1INs as specialized master regulators of superficial neocortex according to their pervasive top-down afferents.

Organization of corticocortical and thalamocortical top-down inputs in the primary visual cortex

Unified visual perception requires integration of bottom-up and top-down inputs in the primary visual cortex (V1), yet the organization of top-down inputs in V1 remains unclear. Here, we used optogenetics-assisted circuit mapping to identify how multiple top-down inputs from higher-order cortical and thalamic areas engage V1 excitatory and inhibitory neurons. Top-down inputs overlap in superficial layers yet segregate in deep layers. Inputs from the medial secondary visual cortex (V2M) and anterior cingulate cortex (ACA) converge on L6 Pyrs, whereas ventrolateral orbitofrontal cortex (ORBvl) and lateral posterior thalamic nucleus (LP) inputs are processed in parallel in Pyr-type-specific subnetworks (Pyr←ORBvl and Pyr←LP) and drive mutual inhibition between them via local interneurons. Our study deepens understanding of the top-down modulation mechanisms of visual processing and establishes that V2M and ACA inputs in L6 employ integrated processing distinct from the parallel processing of LP and ORBvl inputs in L5.

Multimodal analyses reveal genes driving electrophysiological maturation of neurons in the primate prefrontal cortex

The prefrontal cortex (PFC) is critical for myriad high-cognitive functions and is associated with several neuropsychiatric disorders. Here, using Patch-seq and single-nucleus multiomic analyses, we identified genes and regulatory networks governing the maturation of distinct neuronal populations in the PFC of rhesus macaque. We discovered that specific electrophysiological properties exhibited distinct maturational kinetics and identified key genes underlying these properties. We unveiled that RAPGEF4 is important for the maturation of resting membrane potential and inward sodium current in both macaque and human. We demonstrated that knockdown of CHD8, a high-confidence autism risk gene, in human and macaque organotypic slices led to impaired maturation, via downregulation of key genes, including RAPGEF4. Restoring the expression of RAPGEF4 rescued the proper electrophysiological maturation of CHD8-deficient neurons. Our study revealed regulators of neuronal maturation during a critical period of PFC development in primates and implicated such regulators in molecular processes underlying autism.

Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma

Prior studies have described the complex interplay that exists between glioma cells and neurons, however, the electrophysiological properties endogenous to tumor cells remain obscure. To address this, we employed Patch-sequencing on human glioma specimens and found that one third of patched cells in IDH mutant (IDH mut ) tumors demonstrate properties of both neurons and glia by firing single, short action potentials. To define these hybrid cells (HCs) and discern if they are tumor in origin, we developed a computational tool, Single Cell Rule Association Mining (SCRAM), to annotate each cell individually. SCRAM revealed that HCs represent tumor and non-tumor cells that feature GABAergic neuron and oligodendrocyte precursor cell signatures. These studies are the first to characterize the combined electrophysiological and molecular properties of human glioma cells and describe a new cell type in human glioma with unique electrophysiological and transcriptomic properties that are likely also present in the non-tumor mammalian brain.

Linear and Nonlinear Behaviors of the Photoreceptor Coupled Network

Photoreceptors are electrically coupled to one another, and the spatiotemporal properties of electrical synapses in a two-dimensional retinal network are still not well studied, because of the limitation of the single electrode or pair recording techniques which do not allow simultaneously measuring responses of multiple photoreceptors at various locations in the retina. A multiple electrode recording system is needed. In this study, we investigate the network properties of the two-dimensional rod coupled array of the salamander retina (both sexes were used) by using the newly available multiple patch electrode system that allows simultaneous recordings from up to eight cells and to determine the electrical connectivity among multiple rods. We found direct evidence that voltage signal spread in the rod-rod coupling network in the absence of I h (mediated by HCN channels) is passive and follows the linear cable equation. Under physiological conditions, I h shapes the network signal by progressively shortening the response time-to-peak of distant rods, compensating the time loss of signal traveling from distant rods to bipolar cell somas and facilitating synchronization of rod output signals. Under voltage-clamp conditions, current flow within the coupled rods follows Ohm's law, supporting the idea that nonlinear behaviors of the rod network are dependent on membrane voltage. Rod-rod coupling is largely symmetrical in the 2D array, and voltage-clamp blocking the next neighboring rod largely suppresses rod signal spread into the second neighboring rod, suggesting that indirect coupling pathways play a minor role in rod-rod coupling.

Top-down modulation in canonical cortical circuits with short-term plasticity

Cortical dynamics and computations are strongly influenced by diverse GABAergic interneurons, including those expressing parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP). Together with excitatory (E) neurons, they form a canonical microcircuit and exhibit counterintuitive nonlinear phenomena. One instance of such phenomena is response reversal, whereby SST neurons show opposite responses to top-down modulation via VIP depending on the presence of bottom-up sensory input, indicating that the network may function in different regimes under different stimulation conditions. Combining analytical and computational approaches, we demonstrate that model networks with multiple interneuron subtypes and experimentally identified short-term plasticity mechanisms can implement response reversal. Surprisingly, despite not directly affecting SST and VIP activity, PV-to-E short-term depression has a decisive impact on SST response reversal. We show how response reversal relates to inhibition stabilization and the paradoxical effect in the presence of several short-term plasticity mechanisms demonstrating that response reversal coincides with a change in the indispensability of SST for network stabilization. In summary, our work suggests a role of short-term plasticity mechanisms in generating nonlinear phenomena in networks with multiple interneuron subtypes and makes several experimentally testable predictions.

Morphogenetic theory of mental and cognitive disorders: the role of neurotrophic and guidance molecules

Mental illness and cognitive disorders represent a serious problem for the modern society. Many studies indicate that mental disorders are polygenic and that impaired brain development may lay the ground for their manifestation. Neural tissue development is a complex and multistage process that involves a large number of distant and contact molecules. In this review, we have considered the key steps of brain morphogenesis, and the major molecule families involved in these process. The review provides many indications of the important contribution of the brain development process and correct functioning of certain genes to human mental health. To our knowledge, this comprehensive review is one of the first in this field. We suppose that this review may be useful to novice researchers and clinicians wishing to navigate the field.

Pianno: a probabilistic framework automating semantic annotation for spatial transcriptomics

Spatial transcriptomics has revolutionized the study of gene expression within tissues, while preserving spatial context. However, annotating spatial spots' biological identity remains a challenge. To tackle this, we introduce Pianno, a Bayesian framework automating structural semantics annotation based on marker genes. Comprehensive evaluations underscore Pianno's remarkable prowess in precisely annotating a wide array of spatial semantics, ranging from diverse anatomical structures to intricate tumor microenvironments, as well as in estimating cell type distributions, across data generated from various spatial transcriptomics platforms. Furthermore, Pianno, in conjunction with clustering approaches, uncovers a region- and species-specific excitatory neuron subtype in the deep layer 3 of the human neocortex, shedding light on cellular evolution in the human neocortex. Overall, Pianno equips researchers with a robust and efficient tool for annotating diverse biological structures, offering new perspectives on spatial transcriptomics data.

Multimodal Nature of the Single-cell Primate Brain Atlas: Morphology, Transcriptome, Electrophysiology, and Connectivity

Primates exhibit complex brain structures that augment cognitive function. The neocortex fulfills high-cognitive functions through billions of connected neurons. These neurons have distinct transcriptomic, morphological, and electrophysiological properties, and their connectivity principles vary. These features endow the primate brain atlas with a multimodal nature. The recent integration of next-generation sequencing with modified patch-clamp techniques is revolutionizing the way to census the primate neocortex, enabling a multimodal neuronal atlas to be established in great detail: (1) single-cell/single-nucleus RNA-seq technology establishes high-throughput transcriptomic references, covering all major transcriptomic cell types; (2) patch-seq links the morphological and electrophysiological features to the transcriptomic reference; (3) multicell patch-clamp delineates the principles of local connectivity. Here, we review the applications of these technologies in the primate neocortex and discuss the current advances and tentative gaps for a comprehensive understanding of the primate neocortex.

Data-driven multiscale computational models of cortical and subcortical regions

Data-driven computational models of neurons, synapses, microcircuits, and mesocircuits have become essential tools in modern brain research. The goal of these multiscale models is to integrate and synthesize information from different levels of brain organization, from cellular properties, dendritic excitability, and synaptic dynamics to microcircuits, mesocircuits, and ultimately behavior. This article surveys recent advances in the genesis of data-driven computational models of mammalian neural networks in cortical and subcortical areas. I discuss the challenges and opportunities in developing data-driven multiscale models, including the need for interdisciplinary collaborations, the importance of model validation and comparison, and the potential impact on basic and translational neuroscience research. Finally, I highlight future directions and emerging technologies that will enable more comprehensive and predictive data-driven models of brain function and dysfunction.

Conditioning and pseudoconditioning differently change intrinsic excitability of inhibitory interneurons in the neocortex

Many studies indicate a broad role of various classes of GABAergic interneurons in the processes related to learning. However, little is known about how the learning process affects intrinsic excitability of specific classes of interneurons in the neocortex. To determine this, we employed a simple model of conditional learning in mice where vibrissae stimulation was used as a conditioned stimulus and a tail shock as an unconditioned one. In vitro whole-cell patch-clamp recordings showed an increase in intrinsic excitability of low-threshold spiking somatostatin-expressing interneurons (SST-INs) in layer 4 (L4) of the somatosensory (barrel) cortex after the conditioning paradigm. In contrast, pseudoconditioning reduced intrinsic excitability of SST-LTS, parvalbumin-expressing interneurons (PV-INs), and vasoactive intestinal polypeptide-expressing interneurons (VIP-INs) with accommodating pattern in L4 of the barrel cortex. In general, increased intrinsic excitability was accompanied by narrowing of action potentials (APs), whereas decreased intrinsic excitability coincided with AP broadening. Altogether, these results show that both conditioning and pseudoconditioning lead to plastic changes in intrinsic excitability of GABAergic interneurons in a cell-specific manner. In this way, changes in intrinsic excitability can be perceived as a common mechanism of learning-induced plasticity in the GABAergic system.

Cell-Type-Specific Effects of Somatostatin on Synaptic Transmission in the Anterior Cingulate Cortex

Inhibitory modulation of glutamatergic information processing is a prerequisite for proper network function. Among the many groups of interneurons (INs), somatostatin-expressing interneurons (SOM-INs) play an important role in the maintenance of physiological brain activity. We have previously shown that somatostatin (SOM) causes a reduction in pyramidal cell (PC) excitability. However, the mechanisms of action of the peptide on cortical synaptic circuits are still unclear. To understand the effects of the neuropeptide SOM on cortical synaptic circuits, we performed a detailed side-by-side comparison of its postsynaptic effects on PCs, SOM-INs, and layer 1 interneurons (L1-INs) in the anterior cingulate cortex of male and female mice and found that SOM produced pronounced postsynaptic effects in PCs while having little to no effect on either IN type. This comparison allowed us to link the observed postsynaptic effects to SOM-induced modulations of glutamatergic and GABAergic synaptic transmission and to trace the impact of the neuropeptide on the neuronal circuitry between these three cell types. We show here that SOM depresses glutamatergic synaptic transmission via a presynaptic mechanism while exerting a differential impact on GABAA receptor- and GABAB receptor-mediated transmission at the pre- and postsynaptic level resulting in a shift of inhibition in L2/3 PCs from L1-INs to SOM-INs. In summary, this study unravels a novel aspect by which SOM modulates synaptic signaling between PCs, L1-INs, and SOM-INs.