Spatial connectivity matches direction selectivity in visual cortex

Transformation of Motion Pattern Selectivity from Retina to Superior Colliculus

The superior colliculus (SC) is a prominent and conserved visual center in all vertebrates. In mice, the most superficial lamina of the SC is enriched with neurons that are selective for the moving direction of visual stimuli. Here, we study how these direction selective neurons respond to complex motion patterns known as plaids, using two-photon calcium imaging in awake male and female mice. The plaid pattern consists of two superimposed sinusoidal gratings moving in different directions, giving an apparent pattern direction that lies between the directions of the two component gratings. Most direction selective neurons in the mouse SC respond robustly to the plaids and show a high selectivity for the moving direction of the plaid pattern but not of its components. Pattern motion selectivity is seen in both excitatory and inhibitory SC neurons and is especially prevalent in response to plaids with large cross angles between the two component gratings. However, retinal inputs to the SC are ambiguous in their selectivity to pattern versus component motion. Modeling suggests that pattern motion selectivity in the SC can arise from a nonlinear transformation of converging retinal inputs. In contrast, the prevalence of pattern motion selective neurons is not seen in the primary visual cortex (V1). These results demonstrate an interesting difference between the SC and V1 in motion processing and reveal the SC as an important site for encoding pattern motion.

Plasticity of Response Properties of Mouse Visual Cortex Neurons Induced by Optogenetic Tetanization In Vivo

Heterosynaptic plasticity, along with Hebbian homosynaptic plasticity, is an important mechanism ensuring the stable operation of learning neuronal networks. However, whether heterosynaptic plasticity occurs in the whole brain in vivo, and what role(s) in brain function in vivo it could play, remains unclear. Here, we used an optogenetics approach to apply a model of intracellular tetanization, which was established and employed to study heterosynaptic plasticity in brain slices, to study the plasticity of response properties of neurons in the mouse visual cortex in vivo. We show that optogenetically evoked high-frequency bursts of action potentials (optogenetic tetanization) in the principal neurons of the visual cortex induce long-term changes in the responses to visual stimuli. Optogenetic tetanization had distinct effects on responses to different stimuli, as follows: responses to optimal and orthogonal orientations decreased, responses to null direction did not change, and responses to oblique orientations increased. As a result, direction selectivity of the neurons decreased and orientation tuning became broader. Since optogenetic tetanization was a postsynaptic protocol, applied in the absence of sensory stimulation, and, thus, without association of presynaptic activity with bursts of action potentials, the observed changes were mediated by mechanisms of heterosynaptic plasticity. We conclude that heterosynaptic plasticity can be induced in vivo and propose that it may play important homeostatic roles in operation of neural networks by helping to prevent runaway dynamics of responses to visual stimuli and to keep the tuning of neuronal responses within the range optimized for the encoding of multiple features in population activity.

Emergence of co-tuning in inhibitory neurons as a network phenomenon mediated by randomness, correlations, and homeostatic plasticity

Cortical excitatory neurons show clear tuning to stimulus features, but the tuning properties of inhibitory interneurons are ambiguous. While inhibitory neurons have been considered to be largely untuned, some studies show that some parvalbumin-expressing (PV) neurons do show feature selectivity and participate in co-tuned subnetworks with pyramidal neurons. In this study, we first use mean-field theory to demonstrate that a combination of homeostatic plasticity governing the synaptic dynamics of the connections from PV to excitatory neurons, heterogeneity in the excitatory postsynaptic potentials that impinge on PV neurons, and shared correlated input from layer 4 results in the functional and structural self-organization of PV subnetworks. Second, we show that structural and functional feature tuning of PV neurons emerges more clearly at the network level, i.e., that population-level measures identify functional and structural co-tuning of PV neurons that are not evident in pairwise individual-level measures. Finally, we show that such co-tuning can enhance network stability at the cost of reduced feature selectivity.

Functional specificity of recurrent inhibition in visual cortex

In the neocortex, neural activity is shaped by the interaction of excitatory and inhibitory neurons, defined by the organization of their synaptic connections. Although connections among excitatory pyramidal neurons are sparse and functionally tuned, inhibitory connectivity is thought to be dense and largely unstructured. By measuring in vivo visual responses and synaptic connectivity of parvalbumin-expressing (PV+) inhibitory cells in mouse primary visual cortex, we show that the synaptic weights of their connections to nearby pyramidal neurons are specifically tuned according to the similarity of the cells' responses. Individual PV+ cells strongly inhibit those pyramidal cells that provide them with strong excitation and share their visual selectivity. This structured organization of inhibitory synaptic weights provides a circuit mechanism for tuned inhibition onto pyramidal cells despite dense connectivity, stabilizing activity within feature-specific excitatory ensembles while supporting competition between them.

The logic of recurrent circuits in the primary visual cortex

Recurrent cortical activity sculpts visual perception by refining, amplifying or suppressing visual input. However, the rules that govern the influence of recurrent activity remain enigmatic. We used ensemble-specific two-photon optogenetics in the mouse visual cortex to isolate the impact of recurrent activity from external visual input. We found that the spatial arrangement and the visual feature preference of the stimulated ensemble and the neighboring neurons jointly determine the net effect of recurrent activity. Photoactivation of these ensembles drives suppression in all cells beyond 30 µm but uniformly drives activation in closer similarly tuned cells. In nonsimilarly tuned cells, compact, cotuned ensembles drive net suppression, while diffuse, cotuned ensembles drive activation. Computational modeling suggests that highly local recurrent excitatory connectivity and selective convergence onto inhibitory neurons explain these effects. Our findings reveal a straightforward logic in which space and feature preference of cortical ensembles determine their impact on local recurrent activity.

Excitation creates a distributed pattern of cortical suppression due to varied recurrent input

Dense local, recurrent connections are a major feature of cortical circuits, yet how they affect neurons' responses has been unclear, with some studies reporting weak recurrent effects, some reporting amplification, and others indicating local suppression. Here, we show that optogenetic input to mouse V1 excitatory neurons generates salt-and-pepper patterns of both excitation and suppression. Responses in individual neurons are not strongly predicted by that neuron's direct input. A balanced-state network model reconciles a set of diverse observations: the observed dynamics, suppressed responses, decoupling of input and output, and long tail of excited responses. The model shows recurrent excitatory-excitatory connections are strong and also variable across neurons. Together, these results demonstrate that excitatory recurrent connections can have major effects on cortical computations by shaping and changing neurons' responses to input.

Mechanisms underlying reshuffling of visual responses by optogenetic stimulation in mice and monkeys

The ability to optogenetically perturb neural circuits opens an unprecedented window into mechanisms governing circuit function. We analyzed and theoretically modeled neuronal responses to visual and optogenetic inputs in mouse and monkey V1. In both species, optogenetic stimulation of excitatory neurons strongly modulated the activity of single neurons yet had weak or no effects on the distribution of firing rates across the population. Thus, the optogenetic inputs reshuffled firing rates across the network. Key statistics of mouse and monkey responses lay on a continuum, with mice/monkeys occupying the low-/high-rate regions, respectively. We show that neuronal reshuffling emerges generically in randomly connected excitatory/inhibitory networks, provided the coupling strength (combination of recurrent coupling and external input) is sufficient that powerful inhibitory feedback cancels the mean optogenetic input. A more realistic model, distinguishing tuned visual vs. untuned optogenetic input in a structured network, reduces the coupling strength needed to explain reshuffling.

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

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

Modeling apical and basal tree contribution to orientation selectivity in a mouse primary visual cortex layer 2/3 pyramidal cell

Pyramidal neurons, a mainstay of cortical regions, receive a plethora of inputs from various areas onto their morphologically distinct apical and basal trees. Both trees differentially contribute to the somatic response, defining distinct anatomical and possibly functional sub-units. To elucidate the contribution of each tree to the encoding of visual stimuli at the somatic level, we modeled the response pattern of a mouse L2/3 V1 pyramidal neuron to orientation tuned synaptic input. Towards this goal, we used a morphologically detailed computational model of a single cell that replicates electrophysiological and two-photon imaging data. Our simulations predict a synergistic effect of apical and basal trees on somatic action potential generation: basal tree activity, in the form of either depolarization or dendritic spiking, is necessary for producing somatic activity, despite the fact that most somatic spikes are heavily driven by apical dendritic spikes. This model provides evidence for synergistic computations taking place in the basal and apical trees of the L2/3 V1 neuron along with mechanistic explanations for tree-specific contributions and emphasizes the potential role of predictive and attentional feedback input in these cells.

Improving Multispike Learning With Plastic Synaptic Delays

Emulating the spike-based processing in the brain, spiking neural networks (SNNs) are developed and act as a promising candidate for the new generation of artificial neural networks that aim to produce efficient cognitions as the brain. Due to the complex dynamics and nonlinearity of SNNs, designing efficient learning algorithms has remained a major difficulty, which attracts great research attention. Most existing ones focus on the adjustment of synaptic weights. However, other components, such as synaptic delays, are found to be adaptive and important in modulating neural behavior. How could plasticity on different components cooperate to improve the learning of SNNs remains as an interesting question. Advancing our previous multispike learning, we propose a new joint weight-delay plasticity rule, named TDP-DL, in this article. Plastic delays are integrated into the learning framework, and as a result, the performance of multispike learning is significantly improved. Simulation results highlight the effectiveness and efficiency of our TDP-DL rule compared to baseline ones. Moreover, we reveal the underlying principle of how synaptic weights and delays cooperate with each other through a synthetic task of interval selectivity and show that plastic delays can enhance the selectivity and flexibility of neurons by shifting information across time. Due to this capability, useful information distributed away in the time domain can be effectively integrated for a better accuracy performance, as highlighted in our generalization tasks of the image, speech, and event-based object recognitions. Our work is thus valuable and significant to improve the performance of spike-based neuromorphic computing.

Truly pattern: Nonlinear integration of motion signals is required to account for the responses of pattern cells in rat visual cortex

A key feature of advanced motion processing in the primate dorsal stream is the existence of pattern cells-specialized cortical neurons that integrate local motion signals into pattern-invariant representations of global direction. Pattern cells have also been reported in rodent visual cortex, but it is unknown whether the tuning of these neurons results from truly integrative, nonlinear mechanisms or trivially arises from linear receptive fields (RFs) with a peculiar geometry. Here, we show that pattern cells in rat primary (V1) and lateromedial (LM) visual cortex process motion direction in a way that cannot be explained by the linear spatiotemporal structure of their RFs. Instead, their tuning properties are consistent with and well explained by those of units in a state-of-the-art neural network model of the dorsal stream. This suggests that similar cortical processes underlay motion representation in primates and rodents. The latter could thus serve as powerful model systems to unravel the underlying circuit-level mechanisms.

Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons

Two facts about cortex are widely accepted: neuronal responses show large spiking variability with near Poisson statistics and cortical circuits feature abundant recurrent connections between neurons. How these spiking and circuit properties combine to support sensory representation and information processing is not well understood. We build a theoretical framework showing that these two ubiquitous features of cortex combine to produce optimal sampling-based Bayesian inference. Recurrent connections store an internal model of the external world, and Poissonian variability of spike responses drives flexible sampling from the posterior stimulus distributions obtained by combining feedforward and recurrent neuronal inputs. We illustrate how this framework for sampling-based inference can be used by cortex to represent latent multivariate stimuli organized either hierarchically or in parallel. A neural signature of such network sampling are internally generated differential correlations whose amplitude is determined by the prior stored in the circuit, which provides an experimentally testable prediction for our framework.

Compressive sensing of functional connectivity maps from patterned optogenetic stimulation of neuronal ensembles

Mapping functional connectivity between neurons is an essential step toward probing the neural computations mediating behavior. Accurately determining synaptic connectivity maps in populations of neurons is challenging in terms of yield, accuracy, and experimental time. Here, we developed a compressive sensing approach to reconstruct synaptic connectivity maps based on random two-photon cell-targeted optogenetic stimulation and membrane voltage readout of many putative postsynaptic neurons. Using a biophysical network model of interconnected populations of excitatory and inhibitory neurons, we characterized mapping recall and precision as a function of network observability, sparsity, number of neurons stimulated, off-target stimulation, synaptic reliability, propagation latency, and network topology. We found that mapping can be achieved with far fewer measurements than the standard pairwise sequential approach, with network sparsity and synaptic reliability serving as primary determinants of the performance. Our results suggest a rapid and efficient method to reconstruct functional connectivity of sparsely connected neuronal networks.

A parcellation scheme of mouse isocortex based on reversals in connectivity gradients

The brain is composed of several anatomically clearly separated structures. This parcellation is often extended into the isocortex, based on anatomical, physiological, or functional differences. Here, we derive a parcellation scheme based purely on the spatial structure of long-range synaptic connections within the cortex. To that end, we analyzed a publicly available dataset of average mouse brain connectivity, and split the isocortex into disjunct regions. Instead of clustering connectivity based on modularity, our scheme is inspired by methods that split sensory cortices into subregions where gradients of neuronal response properties, such as the location of the receptive field, reverse. We calculated comparable gradients from voxelized brain connectivity data and automatically detected reversals in them. This approach better respects the known presence of functional gradients within brain regions than clustering-based approaches. Placing borders at the reversals resulted in a parcellation into 41 subregions that differs significantly from an established scheme in nonrandom ways, but is comparable in terms of the modularity of connectivity between regions. It reveals unexpected trends of connectivity, such as a tripartite split of somatomotor regions along an anterior to posterior gradient. The method can be readily adapted to other organisms and data sources, such as human functional connectivity.

A Novel Mouse Model for Polysynaptic Retrograde Tracing and Rabies Pathological Research

Retrograde tracing is an important method for dissecting neuronal connections and mapping neural circuits. Over the past decades, several virus-based retrograde tracers have been developed and have contributed to display multiple neural circuits in the brain. However, most of the previously widely used viral tools have focused on mono-transsynaptic neural tracing within the central nervous system, with very limited options for achieving polysynaptic tracing between the central and peripheral nervous systems. In this study, we generated a novel mouse line, GT mice, in which both glycoprotein (G) and ASLV-A receptor (TVA) were expressed throughout the body. Using this mouse model, in combination with the well-developed rabies virus tools (RABV-EnvA-ΔG) for monosynaptic retrograde tracing, polysynaptic retrograde tracing can be achieved. This allows functional forward mapping and long-term tracing. Furthermore, since the G-deleted rabies virus can travel upstream against the nervous system as the original strain, this mouse model can also be used for rabies pathological studies. Schematic illustrations about the application principles of GT mice in polysynaptic retrograde tracing and rabies pathological research.

Optical opening of the blood-brain barrier for targeted and ultra-sparse viral infection of cells in mouse cortex

Adeno-associated viruses (AAVs) are used in a wide array of experimental situations for driving expression of biosensors, recombinases, and opto-/chemo-genetic actuators in the brain. However, conventional approaches for minimally invasive, spatially precise, and ultra-sparse AAV-mediated transduction of cells during imaging experiments have remained a significant challenge. Here, we show that intravenous injection of commercially available AAVs at different doses, combined with laser-based perforation of cortical capillaries through a cranial widow, allows for ultra-sparse, titratable, and micron-level precision for delivery of viral vectors with relatively little inflammation or tissue damage. Further, we show the utility of this approach for eliciting sparse expression of GCaMP6, channelrhodopsin, or fluorescent reporters in neurons and astrocytes within specific functional domains in normal and stroke-damaged cortex. This technique represents a facile approach for targeted delivery of viral vectors that should assist in the study of cell types and circuits in the cortex.

Topological insights into the neural basis of flexible behavior

It is widely accepted that there is an inextricable link between neural computations, biological mechanisms, and behavior, but it is challenging to simultaneously relate all three. Here, we show that topological data analysis (TDA) provides an important bridge between these approaches to studying how brains mediate behavior. We demonstrate that cognitive processes change the topological description of the shared activity of populations of visual neurons. These topological changes constrain and distinguish between competing mechanistic models, are connected to subjects' performance on a visual change detection task, and, via a link with network control theory, reveal a tradeoff between improving sensitivity to subtle visual stimulus changes and increasing the chance that the subject will stray off task. These connections provide a blueprint for using TDA to uncover the biological and computational mechanisms by which cognition affects behavior in health and disease.

Recurrent pattern completion drives the neocortical representation of sensory inference

When sensory information is incomplete or ambiguous, the brain relies on prior expectations to infer perceptual objects. Despite the centrality of this process to perception, the neural mechanism of sensory inference is not known. Illusory contours (ICs) are key tools to study sensory inference because they contain edges or objects that are implied only by their spatial context. Using cellular resolution, mesoscale two-photon calcium imaging and multi-Neuropixels recordings in the mouse visual cortex, we identified a sparse subset of neurons in the primary visual cortex (V1) and higher visual areas that respond emergently to ICs. We found that these highly selective 'IC-encoders' mediate the neural representation of IC inference. Strikingly, selective activation of these neurons using two-photon holographic optogenetics was sufficient to recreate IC representation in the rest of the V1 network, in the absence of any visual stimulus. This outlines a model in which primary sensory cortex facilitates sensory inference by selectively strengthening input patterns that match prior expectations through local, recurrent circuitry. Our data thus suggest a clear computational purpose for recurrence in the generation of holistic percepts under sensory ambiguity. More generally, selective reinforcement of top-down predictions by pattern-completing recurrent circuits in lower sensory cortices may constitute a key step in sensory inference.

Distinct brain-wide presynaptic networks underlie the functional identity of individual cortical neurons

Neuronal connections provide the scaffolding for neuronal function. Revealing the connectivity of functionally identified individual neurons is necessary to understand how activity patterns emerge and support behavior. Yet, the brain-wide presynaptic wiring rules that lay the foundation for the functional selectivity of individual neurons remain largely unexplored. Cortical neurons, even in primary sensory cortex, are heterogeneous in their selectivity, not only to sensory stimuli but also to multiple aspects of behavior. Here, to investigate presynaptic connectivity rules underlying the selectivity of pyramidal neurons to behavioral state ^1-12 in primary somatosensory cortex (S1), we used two-photon calcium imaging, neuropharmacology, single-cell based monosynaptic input tracing, and optogenetics. We show that behavioral state-dependent neuronal activity patterns are stable over time. These are not determined by neuromodulatory inputs but are instead driven by glutamatergic inputs. Analysis of brain-wide presynaptic networks of individual neurons with distinct behavioral state-dependent activity profiles revealed characteristic patterns of anatomical input. While both behavioral state-related and unrelated neurons had a similar pattern of local inputs within S1, their long-range glutamatergic inputs differed. Individual cortical neurons, irrespective of their functional properties, received converging inputs from the main S1-projecting areas. Yet, neurons that tracked behavioral state received a smaller proportion of motor cortical inputs and a larger proportion of thalamic inputs. Optogenetic suppression of thalamic inputs reduced behavioral state-dependent activity in S1, but this activity was not externally driven. Our results revealed distinct long-range glutamatergic inputs as a substrate for preconfigured network dynamics associated with behavioral state.

Functional connectomics reveals general wiring rule in mouse visual cortex

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

Panoramic visual statistics shape retina-wide organization of receptive fields

Statistics of natural scenes are not uniform-their structure varies dramatically from ground to sky. It remains unknown whether these nonuniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. Here, by relying on the efficient coding hypothesis, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. Using the mouse (Mus musculus) as a model species, we show that receptive fields of retinal ganglion cells change their shape along the dorsoventral retinal axis, with a marked surround asymmetry at the visual horizon, in agreement with our predictions. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell types.

Efficient inference of synaptic plasticity rule with Gaussian process regression

Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings. We considered biologically plausible models fitting a wide range of in-vitro studies and examined the recovery of their firing-rate dependence from sparse and noisy data. Among the methods assuming low-rankness or smoothness of plasticity rules, Gaussian process regression (GPR), a nonparametric Bayesian approach, performs the best. Under the conditions measuring changes in synaptic weights directly or measuring changes in neural activities as indirect observables of synaptic plasticity, which leads to different inference problems, GPR performs well. Also, GPR could simultaneously recover multiple plasticity rules and robustly perform under various plasticity rules and noise levels. Such flexibility and efficiency, particularly at the low sampling regime, make GPR suitable for recent experimental developments and inferring a broader class of plasticity models.

Developmental neuronal origin regulates neocortical map formation

Sensory neurons in the neocortex exhibit distinct functional selectivity to constitute the neural map. While neocortical map of the visual cortex in higher mammals is clustered, it displays a striking "salt-and-pepper" pattern in rodents. However, little is known about the origin and basis of the interspersed neocortical map. Here we report that the intricate excitatory neuronal kinship-dependent synaptic connectivity influences precise functional map organization in the mouse primary visual cortex. While sister neurons originating from the same neurogenic radial glial progenitors (RGPs) preferentially develop synapses, cousin neurons derived from amplifying RGPs selectively antagonize horizontal synapse formation. Accordantly, cousin neurons in similar layers exhibit clear functional selectivity differences, contributing to a salt-and-pepper architecture. Removal of clustered protocadherins (cPCDHs), the largest subgroup of the diverse cadherin superfamily, eliminates functional selectivity differences between cousin neurons and alters neocortical map organization. These results suggest that developmental neuronal origin regulates neocortical map formation via cPCDHs.

Chaotic dynamics in spatially distributed neuronal networks generate population-wide shared variability

Neural activity in the cortex is highly variable in response to repeated stimuli. Population recordings across the cortex demonstrate that the variability of neuronal responses is shared among large groups of neurons and concentrates in a low dimensional space. However, the source of the population-wide shared variability is unknown. In this work, we analyzed the dynamical regimes of spatially distributed networks of excitatory and inhibitory neurons. We found chaotic spatiotemporal dynamics in networks with similar excitatory and inhibitory projection widths, an anatomical feature of the cortex. The chaotic solutions contain broadband frequency power in rate variability and have distance-dependent and low-dimensional correlations, in agreement with experimental findings. In addition, rate chaos can be induced by globally correlated noisy inputs. These results suggest that spatiotemporal chaos in cortical networks can explain the shared variability observed in neuronal population responses.

Ionic-electronic halide perovskite memdiodes enabling neuromorphic computing with a second-order complexity

With increasing computing demands, serial processing in von Neumann architectures built with zeroth-order complexity digital circuits is saturating in computational capacity and power, entailing research into alternative paradigms. Brain-inspired systems built with memristors are attractive owing to their large parallelism, low energy consumption, and high error tolerance. However, most demonstrations have thus far only mimicked primitive lower-order biological complexities using devices with first-order dynamics. Memristors with higher-order complexities are predicted to solve problems that would otherwise require increasingly elaborate circuits, but no generic design rules exist. Here, we present second-order dynamics in halide perovskite memristive diodes (memdiodes) that enable Bienenstock-Cooper-Munro learning rules capturing both timing- and rate-based plasticity. A triplet spike timing-dependent plasticity scheme exploiting ion migration, back diffusion, and modulable Schottky barriers establishes general design rules for realizing higher-order memristors. This higher order enables complex binocular orientation selectivity in neural networks exploiting the intrinsic physics of the devices, without the need for complicated circuitry.

Haploinsufficiency of Shank3 increases the orientation selectivity of V1 neurons

Autism spectrum disorder (ASD) is a neurodevelopmental disorder whose hallmarks are social deficits, language impairment, repetitive behaviors, and sensory alterations. It has been reported that patients with ASD show differential activity in cortical regions, for instance, increased neuronal activity in visual processing brain areas and atypical visual perception compared with healthy subjects. The causes of these alterations remain unclear, although many studies demonstrate that ASD has a strong genetic correlation. An example is Phelan-McDermid syndrome, caused by a deletion of the Shank3 gene in one allele of chromosome 22. However, the neuronal consequences relating to the haploinsufficiency of Shank3 in the brain remain unknown. Given that sensory abnormalities are often present along with the core symptoms of ASD, our goal was to study the tuning properties of the primary visual cortex to orientation and direction in awake, head-fixed Shank3^+/- mice. We recorded neural activity in vivo in response to visual gratings in the primary visual cortex from a mouse model of ASD (Shank3^+/- mice) using the genetically encoded calcium indicator GCaMP6f, imaged with a two-photon microscope through a cranial window. We found that Shank3^+/- mice showed a higher proportion of neurons responsive to drifting gratings stimuli than wild-type mice. Shank3^+/- mice also show increased responses to some specific stimuli. Furthermore, analyzing the distributions of neurons for the tuning width, we found that Shank3^+/- mice have narrower tuning widths, which was corroborated by analyzing the orientation selectivity. Regarding this, Shank3^+/- mice have a higher proportion of selective neurons, specifically neurons showing increased selectivity to orientation but not direction. Thus, the haploinsufficiency of Shank3 modified the neuronal response of the primary visual cortex.

Ascertaining cells' synaptic connections and RNA expression simultaneously with barcoded rabies virus libraries

Brain function depends on synaptic connections between specific neuron types, yet systematic descriptions of synaptic networks and their molecular properties are not readily available. Here, we introduce SBARRO (Synaptic Barcode Analysis by Retrograde Rabies ReadOut), a method that uses single-cell RNA sequencing to reveal directional, monosynaptic relationships based on the paths of a barcoded rabies virus from its "starter" postsynaptic cell to that cell's presynaptic partners. Thousands of these partner relationships can be ascertained in a single experiment, alongside genome-wide RNAs. We use SBARRO to describe synaptic networks formed by diverse mouse brain cell types in vitro, finding that different cell types have presynaptic networks with differences in average size and cell type composition. Patterns of RNA expression suggest that functioning synapses are critical for rabies virus uptake. By tracking individual rabies clones across cells, SBARRO offers new opportunities to map the synaptic organization of neural circuits.

Formation and computational implications of assemblies in neural circuits

In the brain, patterns of neural activity represent sensory information and store it in non-random synaptic connectivity. A prominent theoretical hypothesis states that assemblies, groups of neurons that are strongly connected to each other, are the key computational units underlying perception and memory formation. Compatible with these hypothesised assemblies, experiments have revealed groups of neurons that display synchronous activity, either spontaneously or upon stimulus presentation, and exhibit behavioural relevance. While it remains unclear how assemblies form in the brain, theoretical work has vastly contributed to the understanding of various interacting mechanisms in this process. Here, we review the recent theoretical literature on assembly formation by categorising the involved mechanisms into four components: synaptic plasticity, symmetry breaking, competition and stability. We highlight different approaches and assumptions behind assembly formation and discuss recent ideas of assemblies as the key computational unit in the brain. Abstract figure legend Assembly Formation. Assemblies are groups of strongly connected neurons formed by the interaction of multiple mechanisms and with vast computational implications. Four interacting components are thought to drive assembly formation: synaptic plasticity, symmetry breaking, competition and stability. This article is protected by copyright. All rights reserved.

Fast, high-throughput production of improved rabies viral vectors for specific, efficient and versatile transsynaptic retrograde labeling

To understand the function of neuronal circuits, it is crucial to disentangle the connectivity patterns within the network. However, most tools currently used to explore connectivity have low throughput, low selectivity, or limited accessibility. Here, we report the development of an improved packaging system for the production of the highly neurotropic RVdG_envA-CVS-N2c rabies viral vectors, yielding titers orders of magnitude higher with no background contamination, at a fraction of the production time, while preserving the efficiency of transsynaptic labeling. Along with the production pipeline, we developed suites of ‘starter’ AAV and bicistronic RVdG-CVS-N2c vectors, enabling retrograde labeling from a wide range of neuronal populations, tailored for diverse experimental requirements. We demonstrate the power and flexibility of the new system by uncovering hidden local and distal inhibitory connections in the mouse hippocampal formation and by imaging the functional properties of a cortical microcircuit across weeks. Our novel production pipeline provides a convenient approach to generate new rabies vectors, while our toolkit flexibly and efficiently expands the current capacity to label, manipulate and image the neuronal activity of interconnected neuronal circuits in vitro and in vivo.

Functional and structural features of L2/3 pyramidal cells continuously covary with pial depth in mouse visual cortex

Pyramidal cells of neocortical layer 2/3 (L2/3 PyrCs) integrate signals from numerous brain areas and project throughout the neocortex. These PyrCs show pial depth-dependent functional and structural specializations, indicating participation in different functional microcircuits. However, whether these depth-dependent differences result from separable PyrC subtypes or whether their features display a continuum correlated with pial depth is unknown. Here, we assessed the stimulus selectivity, electrophysiological properties, dendritic morphology, and excitatory and inhibitory connectivity across the depth of L2/3 in the binocular visual cortex of mice. We find that the apical, but not the basal dendritic tree structure, varies with pial depth, which is accompanied by variation in subthreshold electrophysiological properties. Lower L2/3 PyrCs receive increased input from L4, while upper L2/3 PyrCs receive a larger proportion of intralaminar input. In vivo calcium imaging revealed a systematic change in visual responsiveness, with deeper PyrCs showing more robust responses than superficial PyrCs. Furthermore, deeper PyrCs are more driven by contralateral than ipsilateral eye stimulation. Importantly, the property value transitions are gradual, and L2/3 PyrCs do not display discrete subtypes based on these parameters. Therefore, L2/3 PyrCs' multiple functional and structural properties systematically correlate with their depth, forming a continuum rather than discrete subtypes.

Cascaded normalizations for spatial integration in the primary visual cortex of primates

Spatial integration of visual information is an important function in the brain. However, neural computation for spatial integration in the visual cortex remains unclear. In this study, we recorded laminar responses in V1 of awake monkeys driven by visual stimuli with grating patches and annuli of different sizes. We find three important response properties related to spatial integration that are significantly different between input and output layers: neurons in output layers have stronger surround suppression, smaller receptive field (RF), and higher sensitivity to grating annuli partially covering their RFs. These interlaminar differences can be explained by a descriptive model composed of two global divisions (normalization) and a local subtraction. Our results suggest suppressions with cascaded normalizations (CNs) are essential for spatial integration and laminar processing in the visual cortex. Interestingly, the features of spatial integration in convolutional neural networks, especially in lower layers, are different from our findings in V1.

What is neurorepresentationalism? From neural activity and predictive processing to multi-level representations and consciousness

This review provides an update on Neurorepresentationalism, a theoretical framework that defines conscious experience as multimodal, situational survey and explains its neural basis from brain systems constructing best-guess representations of sensations originating in our environment and body [1]. It posits that conscious experience is characterized by five essential hallmarks: (i) multimodal richness, (ii) situatedness and immersion, (iii) unity and integration, (iv) dynamics and stability, and (v) intentionality. Consciousness is furthermore proposed to have a biological function, framed by the contrast between reflexes and habits (not requiring consciousness) versus goal-directed, planned behavior (requiring multimodal, situational survey). Conscious experience is therefore understood as a sensorily rich, spatially encompassing representation of body and environment, while we nevertheless have the impression of experiencing external reality directly. Contributions to understanding neural mechanisms underlying consciousness are derived from models for predictive processing, which are trained in an unsupervised manner, do not necessarily require overt action, and have been extended to deep neural networks. Even with predictive processing in place, however, the question remains why this type of neural network activity would give rise to phenomenal experience. Here, I propose to tackle the Hard Problem with the concept of multi-level representations which emergently give rise to multimodal, spatially wide superinferences corresponding to phenomenal experiences. Finally, Neurorepresentationalism is compared to other neural theories of consciousness, and its implications for defining indicators of consciousness in animals, artificial intelligence devices and immobile or unresponsive patients with disorders of consciousness are discussed.

Internally generated population activity in cortical networks hinders information transmission

How neuronal variability affects sensory coding is a central question in systems neuroscience, often with complex and model-dependent answers. Many studies explore population models with a parametric structure for response tuning and variability, preventing an analysis of how synaptic circuitry establishes neural codes. We study stimulus coding in networks of spiking neuron models with spatially ordered excitatory and inhibitory connectivity. The wiring structure is capable of producing rich population-wide shared neuronal variability that agrees with many features of recorded cortical activity. While both the spatial scales of feedforward and recurrent projections strongly affect noise correlations, only recurrent projections, and in particular inhibitory projections, can introduce correlations that limit the stimulus information available to a decoder. Using a spatial neural field model, we relate the recurrent circuit conditions for information limiting noise correlations to how recurrent excitation and inhibition can form spatiotemporal patterns of population-wide activity.

All-viral tracing of monosynaptic inputs to single birthdate-defined neurons in the intact brain

Neuronal firing patterns are the result of inputs converging onto single cells. Identifying these inputs, anatomically and functionally, is essential to understand how neurons integrate information. Single-cell electroporation of helper genes and subsequent local injection of recombinant rabies viruses enable precise mapping of inputs to individual cells in superficial layers of the intact cortex. However, access to neurons in deeper structures requires more invasive procedures, including removal of overlying tissue. We developed a method that, through a combination of virus injections, allows us to target 4 or fewer hippocampal cells 48% of the time and a single cell 16% of the time in wild-type mice without use of electroporation or tissue aspiration. We identify local and distant monosynaptic inputs that can be functionally characterized in vivo. By expanding the toolbox for monosynaptic circuit tracing, this method will help further our understanding of neuronal integration at the level of single cells.

Modified viral-genetic mapping reveals local and global connectivity relationships of ventral tegmental area dopamine cells

Dopamine cells in the ventral tegmental area (VTADA) are critical for a variety of motivated behaviors. These cells receive synaptic inputs from over 100 anatomically defined brain regions, which enables control from a distributed set of inputs across the brain. Extensive efforts have been made to map inputs to VTA cells based on neurochemical phenotype and output site. However, all of these studies have the same fundamental limitation that inputs local to the VTA cannot be properly assessed due to non-Cre-dependent uptake of EnvA-pseudotyped virus. Therefore, the quantitative contribution of local inputs to the VTA, including GABAergic, DAergic, and serotonergic, is not known. Here, I used a modified viral-genetic strategy that enables examination of both local and long-range inputs to VTADA cells in mice. I found that nearly half of the total inputs to VTADA cells are located locally, revealing a substantial portion of inputs that have been missed by previous analyses. The majority of inhibition to VTADA cells arises from the substantia nigra pars reticulata, with large contributions from the VTA and the substantia nigra pars compacta. In addition to receiving inputs from VTAGABA neurons, DA neurons are connected with other DA neurons within the VTA as well as the nearby retrorubal field. Lastly, I show that VTADA neurons receive inputs from distributed serotonergic neurons throughout the midbrain and hindbrain, with the majority arising from the dorsal raphe. My study highlights the importance of using the appropriate combination of viral-genetic reagents to unmask the complexity of connectivity relationships to defined cells in the brain.

Origins of direction selectivity in the primate retina

From mouse to primate, there is a striking discontinuity in our current understanding of the neural coding of motion direction. In non-primate mammals, directionally selective cell types and circuits are a signature feature of the retina, situated at the earliest stage of the visual process. In primates, by contrast, direction selectivity is a hallmark of motion processing areas in visual cortex, but has not been found in the retina, despite significant effort. Here we combined functional recordings of light-evoked responses and connectomic reconstruction to identify diverse direction-selective cell types in the macaque monkey retina with distinctive physiological properties and synaptic motifs. This circuitry includes an ON-OFF ganglion cell type, a spiking, ON-OFF polyaxonal amacrine cell and the starburst amacrine cell, all of which show direction selectivity. Moreover, we discovered that macaque starburst cells possess a strong, non-GABAergic, antagonistic surround mediated by input from excitatory bipolar cells that is critical for the generation of radial motion sensitivity in these cells. Our findings open a door to investigation of a precortical circuitry that computes motion direction in the primate visual system.

Local connectivity and synaptic dynamics in mouse and human neocortex

We present a unique, extensive, and open synaptic physiology analysis platform and dataset. Through its application, we reveal principles that relate cell type to synaptic properties and intralaminar circuit organization in the mouse and human cortex. The dynamics of excitatory synapses align with the postsynaptic cell subclass, whereas inhibitory synapse dynamics partly align with presynaptic cell subclass but with considerable overlap. Synaptic properties are heterogeneous in most subclass-to-subclass connections. The two main axes of heterogeneity are strength and variability. Cell subclasses divide along the variability axis, whereas the strength axis accounts for substantial heterogeneity within the subclass. In the human cortex, excitatory-to-excitatory synaptic dynamics are distinct from those in the mouse cortex and vary with depth across layers 2 and 3.

Orientation and direction tuning align with dendritic morphology and spatial connectivity in mouse visual cortex

The functional properties of neocortical pyramidal cells (PCs), such as direction and orientation selectivity in visual cortex, predominantly derive from their excitatory and inhibitory inputs. For layer 2/3 (L2/3) PCs, the detailed relationship between their functional properties and how they sample and integrate information across cortical space is not fully understood. Here, we study this relationship by combining functional in vivo two-photon calcium imaging, in vitro functional circuit mapping, and dendritic reconstruction of the same L2/3 PCs in mouse visual cortex. Our work reveals direct correlations between dendritic morphology and functional input connectivity and the orientation as well as direction tuning of L2/3 PCs. First, the apical dendritic tree is elongated along the postsynaptic preferred orientation, considering the representation of visual space in the cortex as determined by its retinotopic organization. Additionally, sharply orientation-tuned cells show a less complex apical tree compared with broadly tuned cells. Second, in direction-selective L2/3 PCs, the spatial distribution of presynaptic partners is offset from the soma opposite to the preferred direction. Importantly, although the presynaptic excitatory and inhibitory input distributions spatially overlap on average, the excitatory input distribution is spatially skewed along the preferred direction, in contrast to the inhibitory distribution. Finally, the degree of asymmetry is positively correlated with the direction selectivity of the postsynaptic L2/3 PC. These results show that the dendritic architecture and the spatial arrangement of excitatory and inhibitory presynaptic cells of L2/3 PCs play important roles in shaping their orientation and direction tuning.

Synaptic connectivity to L2/3 of primary visual cortex measured by two-photon optogenetic stimulation

Understanding cortical microcircuits requires thorough measurement of physiological properties of synaptic connections formed within and between diverse subclasses of neurons. Towards this goal, we combined spatially precise optogenetic stimulation with multicellular recording to deeply characterize intralaminar and translaminar monosynaptic connections to supragranular (L2/3) neurons in the mouse visual cortex. The reliability and specificity of multiphoton optogenetic stimulation were measured across multiple Cre lines, and measurements of connectivity were verified by comparison to paired recordings and targeted patching of optically identified presynaptic cells. With a focus on translaminar pathways, excitatory and inhibitory synaptic connections from genetically defined presynaptic populations were characterized by their relative abundance, spatial profiles, strength, and short-term dynamics. Consistent with the canonical cortical microcircuit, layer 4 excitatory neurons and interneurons within L2/3 represented the most common sources of input to L2/3 pyramidal cells. More surprisingly, we also observed strong excitatory connections from layer 5 intratelencephalic neurons and potent translaminar inhibition from multiple interneuron subclasses. The hybrid approach revealed convergence to and divergence from excitatory and inhibitory neurons within and across cortical layers. Divergent excitatory connections often spanned hundreds of microns of horizontal space. In contrast, divergent inhibitory connections were more frequently measured from postsynaptic targets near each other.

Local circuit amplification of spatial selectivity in the hippocampus

Local circuit architecture facilitates the emergence of feature selectivity in the cerebral cortex¹. In the hippocampus, it remains unknown whether local computations supported by specific connectivity motifs² regulate the spatial receptive fields of pyramidal cells³. Here we developed an in vivo electroporation method for monosynaptic retrograde tracing⁴ and optogenetics manipulation at single-cell resolution to interrogate the dynamic interaction of place cells with their microcircuitry during navigation. We found a local circuit mechanism in CA1 whereby the spatial tuning of an individual place cell can propagate to a functionally recurrent subnetwork⁵ to which it belongs. The emergence of place fields in individual neurons led to the development of inverse selectivity in a subset of their presynaptic interneurons, and recruited functionally coupled place cells at that location. Thus, the spatial selectivity of single CA1 neurons is amplified through local circuit plasticity to enable effective multi-neuronal representations that can flexibly scale environmental features locally without degrading the feedforward input structure.

Development of Functional Properties in the Early Visual System: New Appreciations of the Roles of Lateral Geniculate Nucleus

In the years following Hubel and Wiesel's first reports on ocular dominance plasticity and amblyopia, much attention has been focused on understanding the role of cortical circuits in developmental and experience-dependent plasticity. Initial studies found few differences between retinal ganglion cells and neurons in the lateral geniculate nucleus and uncovered little evidence for an impact of altered visual experience on the functional properties of lateral geniculate nucleus neurons. In the last two decades, however, studies have revealed that the connectivity between the retina and lateral geniculate nucleus is much richer than was previously appreciated, even revealing visual plasticity - including ocular dominance plasticity - in lateral geniculate nucleus neurons. Here we review the development of the early visual system and the impact of experience with a distinct focus on recent discoveries about lateral geniculate nucleus, its connectivity, and evidence for its plasticity and rigidity during development.

Anatomical and functional connectomes underlying hierarchical visual processing in mouse visual system

Over the last 10 years, there has been a surge in interest in the rodent visual system resulting from the discovery of visual processing functions shared with primates V1, and of a complex anatomical structure in the extrastriate visual cortex. This surprisingly intricate visual system was elucidated by recent investigations using rapidly growing genetic tools primarily available in the mouse. Here, we examine the structural and functional connections of visual areas that have been identified in mice mostly during the past decade, and the impact of these findings on our understanding of brain functions associated with vision. Special attention is paid to structure-function relationships arising from the hierarchical organization, which is a prominent feature of the primate visual system. Recent evidence supports the existence of a hierarchical organization in rodents that contains levels that are poorly resolved relative to those observed in primates. This shallowness of the hierarchy indicates that the mouse visual system incorporates abundant non-hierarchical processing. Thus, the mouse visual system provides a unique opportunity to study non-hierarchical processing and its relation to hierarchical processing.

Punctuated evolution of visual cortical circuits? Evidence from the large rodent Dasyprocta leporina, and the tiny primate Microcebus murinus

Recent reports of the lack of periodic orientation columns in a very large rodent species, the red-rumped agouti, and the existence of incompressible hypercolumns in the lineage of primates, as demonstrated in one of the smallest primates, the mouse lemur, strengthen the interpretation that salt-and-pepper and columns-and-pinwheel mosaics are two distinct functional layouts. These layouts do neither depend on lifestyle nor scale with body size, brain size, absolute neuron numbers, binocular overlap, or visual acuity, but are primarily distinguishable by phylogenetic traits. The predictive value of other biological signatures such as V1 neuronal surface density and the central-peripheral density ratio of retinal ganglion cells are reconsidered, and experiments elucidating the intracortical connectivity in rodents are proposed.

A human-specific modifier of cortical connectivity and circuit function

The cognitive abilities that characterize humans are thought to emerge from unique features of the cortical circuit architecture of the human brain, which include increased cortico-cortical connectivity. However, the evolutionary origin of these changes in connectivity and how they affected cortical circuit function and behaviour are currently unknown. The human-specific gene duplication SRGAP2C emerged in the ancestral genome of the Homo lineage before the major phase of increase in brain size1,2. SRGAP2C expression in mice increases the density of excitatory and inhibitory synapses received by layer 2/3 pyramidal neurons (PNs)3-5. Here we show that the increased number of excitatory synapses received by layer 2/3 PNs induced by SRGAP2C expression originates from a specific increase in local and long-range cortico-cortical connections. Mice humanized for SRGAP2C expression in all cortical PNs displayed a shift in the fraction of layer 2/3 PNs activated by sensory stimulation and an enhanced ability to learn a cortex-dependent sensory-discrimination task. Computational modelling revealed that the increased layer 4 to layer 2/3 connectivity induced by SRGAP2C expression explains some of the key changes in sensory coding properties. These results suggest that the emergence of SRGAP2C at the birth of the Homo lineage contributed to the evolution of specific structural and functional features of cortical circuits in the human cortex.

The physiological basis for contrast opponency in motion computation in Drosophila

In Drosophila, direction-selective neurons implement a mechanism of motion computation similar to cortical neurons, using contrast-opponent receptive fields with ON and OFF subfields. It is not clear how the presynaptic circuitry of direction-selective neurons in the OFF pathway supports this computation if all major inputs are OFF-rectified neurons. Here, we reveal the biological substrate for motion computation in the OFF pathway. Three interneurons, Tm2, Tm9 and CT1, provide information about ON stimuli to the OFF direction-selective neuron T5 across its receptive field, supporting a contrast-opponent receptive field organization. Consistent with its prominent role in motion detection, variability in Tm9 receptive field properties transfers to T5, and calcium decrements in Tm9 in response to ON stimuli persist across behavioral states, while spatial tuning is sharpened by active behavior. Together, our work shows how a key neuronal computation is implemented by its constituent neuronal circuit elements to ensure direction selectivity.

How Cortical Circuits Implement Cortical Computations: Mouse Visual Cortex as a Model

The mouse, as a model organism to study the brain, gives us unprecedented experimental access to the mammalian cerebral cortex. By determining the cortex's cellular composition, revealing the interaction between its different components, and systematically perturbing these components, we are obtaining mechanistic insight into some of the most basic properties of cortical function. In this review, we describe recent advances in our understanding of how circuits of cortical neurons implement computations, as revealed by the study of mouse primary visual cortex. Further, we discuss how studying the mouse has broadened our understanding of the range of computations performed by visual cortex. Finally, we address how future approaches will fulfill the promise of the mouse in elucidating fundamental operations of cortex.

Emergence of local and global synaptic organization on cortical dendrites

Synaptic inputs on cortical dendrites are organized with remarkable subcellular precision at the micron level. This organization emerges during early postnatal development through patterned spontaneous activity and manifests both locally where nearby synapses are significantly correlated, and globally with distance to the soma. We propose a biophysically motivated synaptic plasticity model to dissect the mechanistic origins of this organization during development and elucidate synaptic clustering of different stimulus features in the adult. Our model captures local clustering of orientation in ferret and receptive field overlap in mouse visual cortex based on the receptive field diameter and the cortical magnification of visual space. Including action potential back-propagation explains branch clustering heterogeneity in the ferret and produces a global retinotopy gradient from soma to dendrite in the mouse. Therefore, by combining activity-dependent synaptic competition and species-specific receptive fields, our framework explains different aspects of synaptic organization regarding stimulus features and spatial scales.

Spatially structured inhibition defined by polarized parvalbumin interneuron axons promotes head direction tuning

In cortical microcircuits, it is generally assumed that fast-spiking parvalbumin interneurons mediate dense and nonselective inhibition. Some reports indicate sparse and structured inhibitory connectivity, but the computational relevance and the underlying spatial organization remain unresolved. In the rat superficial presubiculum, we find that inhibition by fast-spiking interneurons is organized in the form of a dominant super-reciprocal microcircuit motif where multiple pyramidal cells recurrently inhibit each other via a single interneuron. Multineuron recordings and subsequent 3D reconstructions and analysis further show that this nonrandom connectivity arises from an asymmetric, polarized morphology of fast-spiking interneuron axons, which individually cover different directions in the same volume. Network simulations assuming topographically organized input demonstrate that such polarized inhibition can improve head direction tuning of pyramidal cells in comparison to a "blanket of inhibition." We propose that structured inhibition based on asymmetrical axons is an overarching spatial connectivity principle for tailored computation across brain regions.