The logic of recurrent circuits in the primary visual cortex

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.

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

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

Efficient coding in biophysically realistic excitatory-inhibitory spiking networks

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

Moculus: an immersive virtual reality system for mice incorporating stereo vision

Due to technical roadblocks, it is unclear how visual circuits represent multiple features or how behaviorally relevant representations are selected for long-term memory. Here we developed Moculus, a head-mounted virtual reality platform for mice that covers the entire visual field, and allows binocular depth perception and full visual immersion. This controllable environment, with three-dimensional (3D) corridors and 3D objects, in combination with 3D acousto-optical imaging, affords rapid visual learning and the uncovering of circuit substrates in one measurement session. Both the control and reinforcement-associated visual cue coding neuronal assemblies are transiently expanded by reinforcement feedback to near-saturation levels. This increases computational capability and allows competition among assemblies that encode behaviorally relevant information. The coding assemblies form partially orthogonal and overlapping clusters centered around hub cells with higher and earlier ramp-like responses, as well as locally increased functional connectivity.

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

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

Principles of visual cortex excitatory microcircuit organization

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

Recurrent Connectivity Shapes Spatial Coding in Hippocampal CA3 Subregions

Stable and flexible neural representations of space in the hippocampus are crucial for navigating complex environments. However, how these distinct representations emerge from the underlying local circuit architecture remains unknown. Using two-photon imaging of CA3 subareas during active behavior, we reveal opposing coding strategies within specific CA3 subregions, with proximal neurons demonstrating stable and generalized representations and distal neurons showing dynamic and context-specific activity. We show in artificial neural network models that varying the recurrence level causes these differences in coding properties to emerge. We confirmed the contribution of recurrent connectivity to functional heterogeneity by characterizing the representational geometry of neural recordings and comparing it with theoretical predictions of neural manifold dimensionality. Our results indicate that local circuit organization, particularly recurrent connectivity among excitatory neurons, plays a key role in shaping complementary spatial representations within the hippocampus.

Cellular-resolution optogenetics reveals attenuation-by-suppression in visual cortical neurons

The relationship between neurons' input and spiking output is central to brain computation. Studies in vitro and in anesthetized animals suggest that nonlinearities emerge in cells' input-output (IO; activation) functions as network activity increases, yet how neurons transform inputs in vivo has been unclear. Here, we characterize cortical principal neurons' activation functions in awake mice using two-photon optogenetics. We deliver fixed inputs at the soma while neurons' activity varies with sensory stimuli. We find that responses to fixed optogenetic input are nearly unchanged as neurons are excited, reflecting a linear response regime above neurons' resting point. In contrast, responses are dramatically attenuated by suppression. This attenuation is a powerful means to filter inputs arriving to suppressed cells, privileging other inputs arriving to excited neurons. These results have two major implications. First, somatic neural activation functions in vivo accord with the activation functions used in recent machine learning systems. Second, neurons' IO functions can filter sensory inputs-not only do sensory stimuli change neurons' spiking outputs, but these changes also affect responses to input, attenuating responses to some inputs while leaving others unchanged.

The stabilized supralinear network accounts for the contrast dependence of visual cortical gamma oscillations

When stimulated, neural populations in the visual cortex exhibit fast rhythmic activity with frequencies in the gamma band (30-80 Hz). The gamma rhythm manifests as a broad resonance peak in the power-spectrum of recorded local field potentials, which exhibits various stimulus dependencies. In particular, in macaque primary visual cortex (V1), the gamma peak frequency increases with increasing stimulus contrast. Moreover, this contrast dependence is local: when contrast varies smoothly over visual space, the gamma peak frequency in each cortical column is controlled by the local contrast in that column's receptive field. No parsimonious mechanistic explanation for these contrast dependencies of V1 gamma oscillations has been proposed. The stabilized supralinear network (SSN) is a mechanistic model of cortical circuits that has accounted for a range of visual cortical response nonlinearities and contextual modulations, as well as their contrast dependence. Here, we begin by showing that a reduced SSN model without retinotopy robustly captures the contrast dependence of gamma peak frequency, and provides a mechanistic explanation for this effect based on the observed non-saturating and supralinear input-output function of V1 neurons. Given this result, the local dependence on contrast can trivially be captured in a retinotopic SSN which however lacks horizontal synaptic connections between its cortical columns. However, long-range horizontal connections in V1 are in fact strong, and underlie contextual modulation effects such as surround suppression. We thus explored whether a retinotopically organized SSN model of V1 with strong excitatory horizontal connections can exhibit both surround suppression and the local contrast dependence of gamma peak frequency. We found that retinotopic SSNs can account for both effects, but only when the horizontal excitatory projections are composed of two components with different patterns of spatial fall-off with distance: a short-range component that only targets the source column, combined with a long-range component that targets columns neighboring the source column. We thus make a specific qualitative prediction for the spatial structure of horizontal connections in macaque V1, consistent with the columnar structure of cortex.

Simple synaptic modulations implement diverse novelty computations

Detecting novelty is ethologically useful for an organism's survival. Recent experiments characterize how different types of novelty over timescales from seconds to weeks are reflected in the activity of excitatory and inhibitory neuron types. Here, we introduce a learning mechanism, familiarity-modulated synapses (FMSs), consisting of multiplicative modulations dependent on presynaptic or pre/postsynaptic neuron activity. With FMSs, network responses that encode novelty emerge under unsupervised continual learning and minimal connectivity constraints. Implementing FMSs within an experimentally constrained model of a visual cortical circuit, we demonstrate the generalizability of FMSs by simultaneously fitting absolute, contextual, and omission novelty effects. Our model also reproduces functional diversity within cell subpopulations, leading to experimentally testable predictions about connectivity and synaptic dynamics that can produce both population-level novelty responses and heterogeneous individual neuron signals. Altogether, our findings demonstrate how simple plasticity mechanisms within a cortical circuit structure can produce qualitatively distinct and complex novelty responses.

Highly local activation of inhibition at the seizure wavefront in vivo

The propagation of a seizure wavefront in the cortex divides an intensely firing seizure core from a low-firing seizure penumbra. Seizure propagation is currently thought to generate strong activation of inhibition in the seizure penumbra that leads to its decreased neuronal firing. However, the direct measurement of neuronal excitability during seizures has been difficult to perform in vivo. We used simultaneous optogenetics and calcium imaging (all-optical interrogation) to characterize real-time neuronal excitability in an acute mouse model of seizure propagation. We find that single-neuron excitability is decreased in close proximity to the seizure wavefront but becomes increased distal to the seizure wavefront. This suggests that inhibitory neurons of the seizure wavefront create a proximal circumference of hypoexcitability but do not influence neuronal excitability in the penumbra.

Self-organization of modular activity in immature cortical networks

During development, cortical activity is organized into distributed modular patterns that are a precursor of the mature columnar functional architecture. Theoretically, such structured neural activity can emerge dynamically from local synaptic interactions through a recurrent network with effective local excitation with lateral inhibition (LE/LI) connectivity. Utilizing simultaneous widefield calcium imaging and optogenetics in juvenile ferret cortex prior to eye opening, we directly test several critical predictions of an LE/LI mechanism. We show that cortical networks transform uniform stimulations into diverse modular patterns exhibiting a characteristic spatial wavelength. Moreover, patterned optogenetic stimulation matching this wavelength selectively biases evoked activity patterns, while stimulation with varying wavelengths transforms activity towards this characteristic wavelength, revealing a dynamic compromise between input drive and the network's intrinsic tendency to organize activity. Furthermore, the structure of early spontaneous cortical activity - which is reflected in the developing representations of visual orientation - strongly overlaps that of uniform opto-evoked activity, suggesting a common underlying mechanism as a basis for the formation of orderly columnar maps underlying sensory representations in the brain.

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.

Contribution of optical resolution to the spatial precision of two-photon optogenetic photostimulation in vivo

Significance

Two-photon optogenetics combines nonlinear excitation with noninvasive activation of neurons to enable the manipulation of neural circuits with a high degree of spatial precision. Combined with two-photon population calcium imaging, these approaches comprise a flexible platform for all-optical interrogation of neural circuits. However, a multitude of optical and biological factors dictate the exact precision of this approach in vivo, where it is most usefully applied.

Aim

We aimed to assess how the optical point spread function (OPSF) contributes to the spatial precision of two-photon photostimulation in neurobiology.

Approach

We altered the axial spread of the OPSF of the photostimulation beam using a spatial light modulator. Subsequently, calcium imaging was used to monitor the axial spatial precision of two-photon photostimulation of layer 2 neurons in the mouse neocortex.

Results

We found that optical resolution is not always the limiting factor of the spatial precision of two-photon optogenetic photostimulation and, by doing so, reveal the key factors that must be improved to achieve maximal precision.

Conclusions

Our results enable future work to focus on the optimal factors by providing key insight from controlled experiments in a manner not previously reported. This research can be applied to advance the state-of-the-art of all-optical interrogation, extending the toolkit for neuroscience research to achieve spatiotemporal precision at the crucial levels in which neural circuits operate.

Bicistronic Expression of a High-Performance Calcium Indicator and Opsin for All-Optical Stimulation and Imaging at Cellular Resolution

State-of-the-art all-optical systems promise unprecedented access to neural activity in vivo, using multiphoton optogenetics to allow simultaneous imaging and control of activity in selected neurons at cellular resolution. However, to achieve wide use of all-optical stimulation and imaging, simple strategies are needed to robustly and stably express opsins and indicators in the same cells. Here, we describe a bicistronic adeno-associated virus (AAV) that expresses both the fast and bright calcium indicator jGCaMP8s, and a soma-targeted (st) and two-photon-activatable opsin, ChrimsonR. With this method, stChrimsonR stimulation with two-photon holography in the visual cortex of mice drives robust spiking in targeted cells, and neural responses to visual sensory stimuli and spontaneous activity are strong and stable. Cells expressing this bicistronic construct show responses to both photostimulation and visual stimulation that are similar to responses measured from cells expressing the same opsin and indicator via separate viruses. This approach is a simple and robust way to prepare neurons in vivo for two-photon holography and imaging.