Functional specificity of recurrent inhibition in visual cortex

A systematic analysis of the joint effects of ganglion cells, lagged LGN cells, and intercortical inhibition on spatiotemporal processing and direction selectivity

Simple cells in the visual cortex process spatial as well as temporal information of the visual stream and enable the perception of motion information. Previous work suggests different mechanisms associated with direction selectivity, such as a temporal offset in thalamocortical input stream through lagged and non-lagged cells of the lateral geniculate nucleus (LGN), or solely from intercortical inhibition, or through a baseline selectivity provided by the thalamocortical connection tuned by intercortical inhibition. While there exists a large corpus of models for spatiotemporal receptive fields, the majority of them built-in the spatiotemporal dynamics by utilizing a combination of spatial and temporal functions and thus, do not explain the emergence of spatiotemporal dynamics on basis of network dynamics emerging in the retina and the LGN. In order to better comprehend the emergence of spatiotemporal processing and direction selectivity, we used a spiking neural network to implement the visual pathway from the retina to the primary visual cortex. By varying different functional parts in our network, we demonstrate how the direction selectivity of simple cells emerges through the interplay between two components: tuned intercortical inhibition and a temporal offset in the feedforward path through lagged LGN cells. In contrast to previous findings, our model simulations suggest an alternative dynamic between these two mechanisms: While intercortical inhibition alone leads to bidirectional selectivity, a temporal shift in the thalamocortical pathway breaks this symmetry in favor of one direction, leading to unidirectional selectivity.

Selectively vulnerable deep cortical layer 5/6 fast-spiking interneurons in Alzheimer's disease models in vivo

Alzheimer's disease (AD) is initiated by amyloid-beta (Aβ) accumulation in the neocortex; however, the cortical layers and neuronal cell types first susceptible to Aβ remain unknown. Using in vivo two-photon Ca2+ imaging in the visual cortex of AD mouse models, we found that cortical layer 5 neurons displayed abnormally prolonged Ca2+ transients before substantial plaque formation. Neuropixels recordings revealed that these abnormal transients were associated with reduced spiking and impaired visual tuning of parvalbumin (PV)-positive fast-spiking interneurons (FSIs) in layers 5/6, whereas PV-FSIs in superficial layers remained unaffected. These dysfunctions occurred alongside a deep-layer-specific reduction in neuronal pentraxin 2 (NPTX2) within excitatory neurons, decreased GluA4 in PV-FSIs, and fewer excitatory synapses onto PV-FSIs. Notably, NPTX2 overexpression increased excitatory input onto layers 5/6 PV-FSIs and rectified their spiking activity. Thus, our findings reveal an early selective impairment of deep cortical layers 5/6 in AD models and identify deep-layer PV-FSIs as therapeutic targets.

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.

Functional connectomics reveals general wiring rule in mouse visual cortex

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain computes. In mouse primary visual cortex, excitatory neurons with similar response properties are more likely to be synaptically connected1-8; however, broader connectivity rules remain unknown. Here we leverage the millimetre-scale MICrONS dataset to analyse synaptic connectivity and functional properties of neurons across cortical layers and areas. Our results reveal that neurons with similar response properties are preferentially connected within and across layers and areas-including feedback connections-supporting the universality of 'like-to-like' connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections beyond what could be explained by the proximity of axons and dendrites. We also discovered a higher-order rule whereby postsynaptic neuron cohorts downstream of presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Recurrent neural networks trained on a simple classification task develop connectivity patterns that mirror both pairwise and higher-order rules, with magnitudes similar to those in MICrONS data. Ablation studies in these recurrent neural networks reveal that disrupting like-to-like connections impairs performance more than disrupting random connections. These findings suggest that these connectivity principles may have a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

Computational functions of precisely balanced neuronal microcircuits in an olfactory memory network

Models of balanced autoassociative memory networks predict that specific inhibition is critical to store information in connectivity. To explore these predictions, we characterized and manipulated different subtypes of fast-spiking interneurons in the posterior telencephalic area Dp (pDp) of adult zebrafish, the homolog of the piriform cortex. Modeling of recurrent networks with assemblies showed that a precise balance of excitation and inhibition is important to prevent not only excessive firing rates ("runaway activity") but also the stochastic occurrence of high pattern correlations ("runaway correlations"). Consistent with model predictions, runaway correlations emerged in pDp when synaptic balance was perturbed by optogenetic manipulations of feedback inhibition but not feedforward inhibition. Runaway correlations were driven by sparse subsets of strongly active neurons rather than by a general broadening of tuning curves. These results are consistent with balanced neuronal assemblies in pDp and reveal novel computational functions of inhibitory microcircuits in an autoassociative network.

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.

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.

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.

Experience-dependent reorganization of inhibitory neuron synaptic connectivity

Organisms continually tune their perceptual systems to the features they encounter in their environment1-3. We have studied how ongoing experience reorganizes the synaptic connectivity of neurons in the olfactory (piriform) cortex of the mouse. We developed an approach to measure synaptic connectivity in vivo, training a deep convolutional network to reliably identify monosynaptic connections from the spike-time cross-correlograms of 4.4 million single-unit pairs. This revealed that excitatory piriform neurons with similar odor tuning are more likely to be connected. We asked whether experience enhances this like-to-like connectivity but found that it was unaffected by odor exposure. Experience did, however, alter the logic of interneuron connectivity. Following repeated encounters with a set of odorants, inhibitory neurons that responded differentially to these stimuli exhibited a high degree of both incoming and outgoing synaptic connections within the cortical network. This reorganization depended only on the odor tuning of the inhibitory interneuron and not on the tuning of its pre- or postsynaptic partners. A computational model of this reorganized connectivity predicts that it increases the dimensionality of the entire network's responses to familiar stimuli, thereby enhancing their discriminability. We confirmed that this network-level property is present in physiological measurements, which showed increased dimensionality and separability of the evoked responses to familiar versus novel odorants. Thus, a simple, non-Hebbian reorganization of interneuron connectivity may selectively enhance an organism's discrimination of the features of its environment.

Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex

Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise balance of excitation and inhibition. To understand computational consequences of E/I assemblies under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp of adult zebrafish, a precisely balanced recurrent network homologous to piriform cortex. We found that E/I assemblies stabilized firing rate distributions compared to networks with excitatory assemblies and global inhibition. Unlike classical memory models, networks with E/I assemblies did not show discrete attractor dynamics. Rather, responses to learned inputs were locally constrained onto manifolds that 'focused' activity into neuronal subspaces. The covariance structure of these manifolds supported pattern classification when information was retrieved from selected neuronal subsets. Networks with E/I assemblies therefore transformed the geometry of neuronal coding space, resulting in continuous representations that reflected both relatedness of inputs and an individual's experience. Such continuous representations enable fast pattern classification, can support continual learning, and may provide a basis for higher-order learning and cognitive computations.

Exploring Cortical Interneurons in Substance Use Disorder: From Mechanisms to Therapeutic Perspectives

Interneurons (INs) play a crucial role in the regulation of neural activity within the medial prefrontal cortex (mPFC), a brain region critically involved in executive functions and behavioral control. In recent preclinical studies, dysregulation of INs in the mPFC has been implicated in the pathophysiology of substance use disorder, characterized by vulnerability to chronic drug use. Here, we explore the diversity of mPFC INs and their connectivity and roles in vulnerability to addiction. We also discuss how these INs change over time with drug exposure. Finally, we focus on noninvasive brain stimulation as a therapeutic approach for targeting INs in substance use disorder, highlighting its potential to restore neural circuits.

Ensemble priming via competitive inhibition: local mechanisms of sensory context storage and deviance detection in the neocortical column

The process by which neocortical neurons and circuits amplify their response to an unexpected change in stimulus, often referred to as deviance detection (DD), has long been thought to be the product of specialized cell types and/or routing between mesoscopic brain areas. Here, we explore a different theory, whereby DD emerges from local network-level interactions within a neocortical column. We propose that deviance-driven neural dynamics can emerge through interactions between ensembles of neurons that have a fundamental inhibitory motif: competitive inhibition between reciprocally connected ensembles under modulation from feed-forward selective (dis)inhibition. Using this framework, we were able to simulate a variety of phenomena pertaining to the experimentally observed shifts in neural tuning across neurons, time, and stimulus history. Anchoring our approach in a variety of experimentally observed phenomena, we used computation modeling in two types of neural networks of vastly different levels of biophysical detail to test hypotheses on emergent dynamics and explore the robustness of underlying connectivity parameters. With a number of corollary predictions that can be tested in future in vivo studies, we show that ensemble priming via competitive inhibition under modulation from selective (dis)inhibition acts as a local mechanism for sensory context storage and that DD does not require specialized input from other brain areas-a novel theoretical paradigm that resolves previously confounding aspects of sensory encoding and predictive processing in the neocortex.

Cortical direction selectivity increases from the input to the output layers of visual cortex

Sensitivity to motion direction is a feature of visual neurons that is essential for motion perception. Recent studies have suggested that direction selectivity is re-established at multiple stages throughout the visual hierarchy, which contradicts the traditional assumption that direction selectivity in later stages largely derives from that in earlier stages. By recording laminar responses in areas 17 and 18 of anesthetized cats of both sexes, we aimed to understand how direction selectivity is processed and relayed across 2 successive stages: the input layers and the output layers within the early visual cortices. We found a strong relationship between the strength of direction selectivity in the output layers and the input layers, as well as the preservation of preferred directions across the input and output layers. Moreover, direction selectivity was enhanced in the output layers compared to the input layers, with the response strength maintained in the preferred direction but reduced in other directions and under blank stimuli. We identified a direction-tuned gain mechanism for interlaminar signal transmission, which likely originated from both feedforward connections across the input and output layers and recurrent connections within the output layers. This direction-tuned gain, coupled with nonlinearity, contributed to the enhanced direction selectivity in the output layers. Our findings suggest that direction selectivity in later cortical stages partially inherits characteristics from earlier cortical stages and is further refined by intracortical connections.

Developmental timecourse of aptitude for motor skill learning in mouse

Learning motor skills requires plasticity in the primary motor cortex (M1). But the capacity for cortical circuit plasticity varies over developmental age in sensory cortex. This study assesses the normal developmental trajectory of motor learning to assess how aptitude might vary with age. We trained mice of both sexes to run on a custom accelerating rotarod at ages from postnatal day (P) 20 to P120, tracking paw position and quantifying time to fall and changes in gait pattern. While animals of all ages were able to perform better after five training sessions, performance improved most rapidly on the first training day for mice between ages P30-60, suggesting an age with heightened plasticity. Learning this task required M1, because pharmacological inactivation of M1 prevented improvement in task performance. Paw position and gait patterns changed with learning, though differently between age groups. Successful mice learned to shift their gait from hopping to walking. Notably, this shift in gait happened earlier in the trial for forelimbs in comparison to hindlimbs. Thus, motor plasticity might more readily occur in forelimbs. Changes in gait and other kinematic parameters are an additional learning metric beyond time to fall, offering insight into how mice improve performance. Overall, these results suggest mouse motor learning has a developmental trajectory.

Specific inhibition and disinhibition in the higher-order structure of a cortical connectome

Neurons are thought to act as parts of assemblies with strong internal excitatory connectivity. Conversely, inhibition is often reduced to blanket inhibition with no targeting specificity. We analyzed the structure of excitation and inhibition in the MICrONS $mm^{3}$ dataset, an electron microscopic reconstruction of a piece of cortical tissue. We found that excitation was structured around a feed-forward flow in large non-random neuron motifs with a structure of information flow from a small number of sources to a larger number of potential targets. Inhibitory neurons connected with neurons in specific sequential positions of these motifs, implementing targeted and symmetrical competition between them. None of these trends are detectable in only pairwise connectivity, demonstrating that inhibition is structured by these large motifs. While descriptions of inhibition in cortical circuits range from non-specific blanket-inhibition to targeted, our results describe a form of targeting specificity existing in the higher-order structure of the connectome. These findings have important implications for the role of inhibition in learning and synaptic plasticity.

Dynamical mechanisms of how an RNN keeps a beat, uncovered with a low-dimensional reduced model

Despite music's omnipresence, the specific neural mechanisms responsible for perceiving and anticipating temporal patterns in music are unknown. To study potential mechanisms for keeping time in rhythmic contexts, we train a biologically constrained RNN, with excitatory (E) and inhibitory (I) units, on seven different stimulus tempos (2-8 Hz) on a synchronization and continuation task, a standard experimental paradigm. Our trained RNN generates a network oscillator that uses an input current (context parameter) to control oscillation frequency and replicates key features of neural dynamics observed in neural recordings of monkeys performing the same task. We develop a reduced three-variable rate model of the RNN and analyze its dynamic properties. By treating our understanding of the mathematical structure for oscillations in the reduced model as predictive, we confirm that the dynamical mechanisms are found also in the RNN. Our neurally plausible reduced model reveals an E-I circuit with two distinct inhibitory sub-populations, of which one is tightly synchronized with the excitatory units.

Calcium-permeable AMPA receptors govern PV neuron feature selectivity

The brain helps us survive by forming internal representations of the external world1,2. Excitatory cortical neurons are often precisely tuned to specific external stimuli3,4. However, inhibitory neurons, such as parvalbumin-positive (PV) interneurons, are generally less selective5. PV interneurons differ from excitatory neurons in their neurotransmitter receptor subtypes, including AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) receptors (AMPARs)6,7. Excitatory neurons express calcium-impermeable AMPARs that contain the GluA2 subunit (encoded by GRIA2), whereas PV interneurons express receptors that lack the GluA2 subunit and are calcium-permeable (CP-AMPARs). Here we demonstrate a causal relationship between CP-AMPAR expression and the low feature selectivity of PV interneurons. We find low expression stoichiometry of GRIA2 mRNA relative to other subunits in PV interneurons that is conserved across ferrets, rodents, marmosets and humans, and causes abundant CP-AMPAR expression. Replacing CP-AMPARs in PV interneurons with calcium-impermeable AMPARs increased their orientation selectivity in the visual cortex. Manipulations to induce sparse CP-AMPAR expression demonstrated that this increase was cell-autonomous and could occur with changes beyond development. Notably, excitatory-PV interneuron connectivity rates and unitary synaptic strength were unaltered by CP-AMPAR removal, which suggested that the selectivity of PV interneurons can be altered without markedly changing connectivity. In Gria2-knockout mice, in which all AMPARs are calcium-permeable, excitatory neurons showed significantly degraded orientation selectivity, which suggested that CP-AMPARs are sufficient to drive lower selectivity regardless of cell type. Moreover, hippocampal PV interneurons, which usually exhibit low spatial tuning, became more spatially selective after removing CP-AMPARs, which indicated that CP-AMPARs suppress the feature selectivity of PV interneurons independent of modality. These results reveal a new role of CP-AMPARs in maintaining low-selectivity sensory representation in PV interneurons and implicate a conserved molecular mechanism that distinguishes this cell type in the neocortex.

Multi-scale spiking network model of human cerebral cortex

Although the structure of cortical networks provides the necessary substrate for their neuronal activity, the structure alone does not suffice to understand the activity. Leveraging the increasing availability of human data, we developed a multi-scale, spiking network model of human cortex to investigate the relationship between structure and dynamics. In this model, each area in one hemisphere of the Desikan-Killiany parcellation is represented by a $1\,\mathrm{mm^{2}}$ column with a layered structure. The model aggregates data across multiple modalities, including electron microscopy, electrophysiology, morphological reconstructions, and diffusion tensor imaging, into a coherent framework. It predicts activity on all scales from the single-neuron spiking activity to the area-level functional connectivity. We compared the model activity with human electrophysiological data and human resting-state functional magnetic resonance imaging (fMRI) data. This comparison reveals that the model can reproduce aspects of both spiking statistics and fMRI correlations if the inter-areal connections are sufficiently strong. Furthermore, we study the propagation of a single-spike perturbation and macroscopic fluctuations through the network. The open-source model serves as an integrative platform for further refinements and future in silico studies of human cortical structure, dynamics, and function.

Simultaneous interneuron labeling reveals cell type-specific, population-level interactions in cortex

Cortical interneurons shape network activity in cell type-specific ways, and interact with other cell types. These interactions are understudied, as current methods typically restrict in vivo labeling to one neuron type. Although post-hoc identification of many cell types has been accomplished, the method is not available to many labs. We present a method to distinguish two red fluorophores in vivo, allowing imaging of activity in somatostatin (SOM), parvalbumin (PV), and the rest of the neural population in mouse cortex. We compared population events in PV and SOM neurons and observed that local network states reflected the ratio of SOM to PV neuron activity, demonstrating the importance of simultaneous labeling to explain dynamics. Activity became sparser and less correlated when the ratio between SOM and PV activity was high. Our simple method can be flexibly applied to study interactions among any combination of distinct cell type populations across brain areas.

Input specificity of NMDA-dependent GABAergic plasticity in the hippocampus

Sensory experiences and learning induce long-lasting changes in both excitatory and inhibitory synapses, thereby providing a crucial substrate for memory. However, the co-tuning of excitatory long-term potentiation (eLTP) or depression (eLTD) with the simultaneous changes at inhibitory synapses (iLTP/iLTD) remains unclear. Herein, we investigated the co-expression of NMDA-induced synaptic plasticity at excitatory and inhibitory synapses in hippocampal CA1 pyramidal cells (PCs) using a combination of electrophysiological, optogenetic, and pharmacological approaches. We found that inhibitory inputs from somatostatin (SST) and parvalbumin (PV)-positive interneurons onto CA1 PCs display input-specific long-term plastic changes following transient NMDA receptor activation. Notably, synapses from SST-positive interneurons consistently exhibited iLTP, irrespective of the direction of excitatory plasticity, whereas synapses from PV-positive interneurons predominantly showed iLTP concurrent with eLTP, rather than eLTD. As neuroplasticity is known to depend on the extracellular matrix, we tested the impact of metalloproteinases (MMP) inhibition. MMP3 blockade interfered with GABAergic plasticity for all inhibitory inputs, whereas MMP9 inhibition selectively blocked eLTP and iLTP in SST-CA1PC synapses co-occurring with eLTP but not eLTD. These findings demonstrate the dissociation of excitatory and inhibitory plasticity co-expression. We propose that these mechanisms of plasticity co-expression may be involved in maintaining excitation-inhibition balance and modulating neuronal integration modes.

Cooperative thalamocortical circuit mechanism for sensory prediction errors

The brain functions as a prediction machine, utilizing an internal model of the world to anticipate sensations and the outcomes of our actions. Discrepancies between expected and actual events, referred to as prediction errors, are leveraged to update the internal model and guide our attention towards unexpected events1-10. Despite the importance of prediction-error signals for various neural computations across the brain, surprisingly little is known about the neural circuit mechanisms responsible for their implementation. Here we describe a thalamocortical disinhibitory circuit that is required for generating sensory prediction-error signals in mouse primary visual cortex (V1). We show that violating animals' predictions by an unexpected visual stimulus preferentially boosts responses of the layer 2/3 V1 neurons that are most selective for that stimulus. Prediction errors specifically amplify the unexpected visual input, rather than representing non-specific surprise or difference signals about how the visual input deviates from the animal's predictions. This selective amplification is implemented by a cooperative mechanism requiring thalamic input from the pulvinar and cortical vasoactive-intestinal-peptide-expressing (VIP) inhibitory interneurons. In response to prediction errors, VIP neurons inhibit a specific subpopulation of somatostatin-expressing inhibitory interneurons that gate excitatory pulvinar input to V1, resulting in specific pulvinar-driven response amplification of the most stimulus-selective neurons in V1. Therefore, the brain prioritizes unpredicted sensory information by selectively increasing the salience of unpredicted sensory features through the synergistic interaction of thalamic input and neocortical disinhibitory circuits.

Spiking attractor model of motor cortex explains modulation of neural and behavioral variability by prior target information

When preparing a movement, we often rely on partial or incomplete information, which can decrement task performance. In behaving monkeys we show that the degree of cued target information is reflected in both, neural variability in motor cortex and behavioral reaction times. We study the underlying mechanisms in a spiking motor-cortical attractor model. By introducing a biologically realistic network topology where excitatory neuron clusters are locally balanced with inhibitory neuron clusters we robustly achieve metastable network activity across a wide range of network parameters. In application to the monkey task, the model performs target-specific action selection and accurately reproduces the task-epoch dependent reduction of trial-to-trial variability in vivo where the degree of reduction directly reflects the amount of processed target information, while spiking irregularity remained constant throughout the task. In the context of incomplete cue information, the increased target selection time of the model can explain increased behavioral reaction times. We conclude that context-dependent neural and behavioral variability is a signum of attractor computation in the motor cortex.

The mechanics of correlated variability in segregated cortical excitatory subnetworks

Understanding the genesis of shared trial-to-trial variability in neuronal population activity within the sensory cortex is critical to uncovering the biological basis of information processing in the brain. Shared variability is often a reflection of the structure of cortical connectivity since it likely arises, in part, from local circuit inputs. A series of experiments from segregated networks of (excitatory) pyramidal neurons in the mouse primary visual cortex challenge this view. Specifically, the across-network correlations were found to be larger than predicted given the known weak cross-network connectivity. We aim to uncover the circuit mechanisms responsible for these enhanced correlations through biologically motivated cortical circuit models. Our central finding is that coupling each excitatory subpopulation with a specific inhibitory subpopulation provides the most robust network-intrinsic solution in shaping these enhanced correlations. This result argues for the existence of excitatory-inhibitory functional assemblies in early sensory areas which mirror not just response properties but also connectivity between pyramidal cells. Furthermore, our findings provide theoretical support for recent experimental observations showing that cortical inhibition forms structural and functional subnetworks with excitatory cells, in contrast to the classical view that inhibition is a nonspecific blanket suppression of local excitation.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Synapse-type-specific competitive Hebbian learning forms functional recurrent networks

Cortical networks exhibit complex stimulus-response patterns that are based on specific recurrent interactions between neurons. For example, the balance between excitatory and inhibitory currents has been identified as a central component of cortical computations. However, it remains unclear how the required synaptic connectivity can emerge in developing circuits where synapses between excitatory and inhibitory neurons are simultaneously plastic. Using theory and modeling, we propose that a wide range of cortical response properties can arise from a single plasticity paradigm that acts simultaneously at all excitatory and inhibitory connections-Hebbian learning that is stabilized by the synapse-type-specific competition for a limited supply of synaptic resources. In plastic recurrent circuits, this competition enables the formation and decorrelation of inhibition-balanced receptive fields. Networks develop an assembly structure with stronger synaptic connections between similarly tuned excitatory and inhibitory neurons and exhibit response normalization and orientation-specific center-surround suppression, reflecting the stimulus statistics during training. These results demonstrate how neurons can self-organize into functional networks and suggest an essential role for synapse-type-specific competitive learning in the development of cortical circuits.

Cortical determinants of loudness perception and auditory hypersensitivity

Parvalbumin-expressing inhibitory neurons (PVNs) stabilize cortical network activity, generate gamma rhythms, and regulate experience-dependent plasticity. Here, we observed that activation or inactivation of PVNs functioned like a volume knob in the mouse auditory cortex (ACtx), turning neural and behavioral classification of sound level up or down over a 20dB range. PVN loudness adjustments were "sticky", such that a single bout of 40Hz PVN stimulation sustainably suppressed ACtx sound responsiveness, potentiated feedforward inhibition, and behaviorally desensitized mice to loudness. Sensory sensitivity is a cardinal feature of autism, aging, and peripheral neuropathy, prompting us to ask whether PVN stimulation can persistently desensitize mice with ACtx hyperactivity, PVN hypofunction, and loudness hypersensitivity triggered by cochlear sensorineural damage. We found that a single 16-minute bout of 40Hz PVN stimulation session restored normal loudness perception for one week, showing that perceptual deficits triggered by irreversible peripheral injuries can be reversed through targeted cortical circuit interventions.

Mirrored might: A vision for inhibition

In this issue of Neuron, Znamenskiy et al.1 unveil functional connection specificity between PV+ inhibitory interneurons and excitatory pyramidal neurons in mouse visual cortex, providing a circuit mechanism for stable amplification of cortical subpopulations.