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Understanding the brain requires understanding neurons’ functional responses to the circuit architecture shaping them. Here we introduce the MICrONS functional connectomics dataset with dense calcium imaging of around 75,000 neurons in primary visual cortex (VISp) and higher visual areas (VISrl, VISal and VISlm) in an awake mouse that is viewing natural and synthetic stimuli. These data are co-registered with an electron microscopy reconstruction containing more than 200,000 cells and 0.5 billion synapses. Proofreading of a subset of neurons yielded reconstructions that include complete dendritic trees as well the local and inter-areal axonal projections that map up to thousands of cell-to-cell connections per neuron. Released as an open-access resource, this dataset includes the tools for data retrieval and analysis 1 , 2 . Accompanying studies describe its use for comprehensive characterization of cell types 3 , 4 , 5 – 6 , a synaptic level connectivity diagram of a cortical column 4 , and uncovering cell-type-specific inhibitory connectivity that can be linked to gene expression data 4 , 7 . Functionally, we identify new computational principles of how information is integrated across visual space 8 , characterize novel types of neuronal invariances 9 and bring structure and function together to uncover a general principle for connectivity between excitatory neurons within and across areas 10 , 11 . Dense calcium imaging combined with co-registered high-resolution electron microscopy reconstruction of the brain of the same mouse provide a functional connectomics map of tens of thousands of neurons of a region of the primary cortex and higher visual areas.

Sensory processing in the neocortex requires both feedforward and feedback information flow between cortical areas 1 . In feedback processing, higher-level representations provide contextual information to lower levels, and facilitate perceptual functions such as contour integration and figure–ground segmentation 2 , 3 . However, we have limited understanding of the circuit and cellular mechanisms that mediate feedback influence. Here we use long-range all-optical connectivity mapping in mice to show that feedback influence from the lateromedial higher visual area (LM) to the primary visual cortex (V1) is spatially organized. When the source and target of feedback represent the same area of visual space, feedback is relatively suppressive. By contrast, when the source is offset from the target in visual space, feedback is relatively facilitating. Two-photon calcium imaging data show that this facilitating feedback is nonlinearly integrated in the apical tuft dendrites of V1 pyramidal neurons: retinotopically offset (surround) visual stimuli drive local dendritic calcium signals indicative of regenerative events, and two-photon optogenetic activation of LM neurons projecting to identified feedback-recipient spines in V1 can drive similar branch-specific local calcium signals. Our results show how neocortical feedback connectivity and nonlinear dendritic integration can together form a substrate to support both predictive and cooperative contextual interactions. Feedback influence from a higher visual area to primary visual cortex in mice engages nonlinear dendritic integration.

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 connected 1 , 2 , 3 , 4 , 5 , 6 , 7 – 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. The MICrONS mouse visual cortex dataset shows that neurons with similar response properties preferentially connect, a pattern that emerges within and across brain areas and layers, and independently emerges in artificial neural networks where these ‘like-to-like’ connections prove important for task performance.

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. The role of lateral inhibition for perception and neural computation remains unsolved. Del Rosario et al. show that distinct types of cortical interneurons in V1 drive lateral inhibition that causes subtraction or division of visual sensitivity.

The columnar organization of response properties is a fundamental feature of the mammalian visual cortex. However, columns have not been observed universally across all mammalian species. Here, we report the discovery of clusters of ipsilateral eye preferring neurons in layer 4 of the mouse primary visual cortex. These clusters extend into layer 2/3 and upper layer 5, forming a column-like pattern for ocular dominance. Our observation of such structures in this minute cortical area sets a new boundary condition for models explaining the emergence of functional organizations in the neocortex. This study reports clusters of ipsilateral eye preferring neurons in layer 4 of mouse visual cortex, extending into layer 2/3 and upper layer 5. This column-like pattern for ocular dominance expands our understanding of the functional organization in neocortex.

Artificial neural networks (ANNs) have been shown to predict neural responses in primary visual cortex (V1) better than classical models. However, this performance often comes at the expense of simplicity and interpretability. Here we introduce a new class of simplified ANN models that can predict over 70% of the response variance of V1 neurons. To achieve this high performance, we first recorded a new dataset of over 29,000 neurons responding to up to 65,000 natural image presentations in mouse V1. We found that ANN models required only two convolutional layers for good performance, with a relatively small first layer. We further found that we could make the second layer small without loss of performance, by fitting individual “minimodels” to each neuron. Similar simplifications applied for models of monkey V1 neurons. We show that the minimodels can be used to gain insight into how stimulus invariance arises in biological neurons. Mathematical models of V1 seek to explain the response properties of V1 neurons, often with more complex models providing more accurate predictions. Here, the authors show that deep neural network models of mouse and monkey V1 can be dramatically simplified to a two-layer “minimodel\" while retaining high accuracy.

The brain’s ability to extract temporal information from dynamic stimuli in the environment is essential for everyday behavior. To extract temporal statistical regularities, neural circuits must possess the ability to measure, produce, and anticipate sensory events. Here we report that when neural populations in macaque primary visual cortex are triggered to exhibit a periodic response to a repetitive sequence of optogenetic laser flashes, they learn to accurately reproduce the temporal sequence even when light stimulation is turned off. Despite the fact that individual cells had a poor capacity to extract temporal information, the population of neurons reproduced the periodic sequence in a temporally precise manner. The same neural population could learn different frequencies of external stimulation, and the ability to extract temporal information was found in all cortical layers. These results demonstrate a remarkable ability of sensory cortical populations to extract and reproduce complex temporal structure from unsupervised external stimulation even when stimuli are perceptually irrelevant. How the brain extracts and reproduces temporal regularities from incoming sensory information is poorly understood. Here, the authors discover the ability of neural populations in the primary visual cortex of behaving monkeys to extract precise temporal structure from unsupervised repetitive external stimulation.

Precise control of the firing of an individual neuron in vivo is a key technology to neuroscience. The laser-induced ion channel opening makes it possible to depolarize neurons and trigger the firing. In this study, we present a noninvasive, opsin-free photostimulation method for activating an individual neuron within the primary visual cortex (V1). This activation is achieved through the transient local scanning of a tightly focused femtosecond laser on the soma of the target neuron that opens the store-operated calcium channels by multiphoton excitation, induces Ca 2+ influx, and depolarizes the neurons to trigger action potentials (APs) firing. In the absence of any visual stimuli, the isolated activation of an individual neuron in a cortical ensemble in layer 2/3 of V1 is sufficient to elicit visually guided specific behaviors in awake mice, without co-activating other neurons in the ensemble. Remarkably, the disruption of a single neuron within the ensemble temporarily paralyzes the entire ensemble and suspends behavioral responses to visual stimuli. However, the ensemble rapidly recovers its responsiveness and function. In general, this opsin-free photostimulation method activates targeted individual ensemble neurons in visual cortex of awake mice enabling firing APs and eliciting behaviors. Precise control of individual neuron firing is key for neuroscience. Here, the authors show a novel opsin-free photostimulation method that activates single neurons in vivo by femtosecond laser scanning-induced Ca2+ influx and action potential firing.

When observing the surface patterns of objects delimited by smooth surfaces, the projections of the surface patterns to the image domain will be subject to substantial variabilities, as induced by variabilities in the geometric viewing conditions, and as generated by either monocular or binocular imaging conditions, or by relative motions between the object and the observer over time. To first order of approximation, the image deformations of such projected surface patterns can be modelled as local linearizations in terms of local 2-D spatial affine transformations. This paper presents a theoretical analysis of relationships between the degrees of freedom in 2-D spatial affine image transformations and the degrees of freedom in the affine Gaussian derivative model for visual receptive fields. For this purpose, we first describe a canonical decomposition of 2-D affine transformations on a product form, closely related to a singular value decomposition, while in closed form, and which reveals the degrees of freedom in terms of (i) uniform scaling transformations, (ii) an overall amount of global rotation, (iii) a complementary non-uniform scaling transformation and (iv) a relative normalization to a preferred symmetry orientation in the image domain. Then, we show how these degrees of freedom relate to the degrees of freedom in the affine Gaussian derivative model. Finally, we use these theoretical results to consider whether we could regard the biological receptive fields in the primary visual cortex of higher mammals as being able to span the degrees of freedom of 2-D spatial affine transformations, based on interpretations of existing neurophysiological experimental results.

Cognitive control guides behaviour by controlling what, when, and how information is represented in the brain 1 . For example, attention controls sensory processing; top-down signals from prefrontal and parietal cortex strengthen the representation of task-relevant stimuli 2 – 4 . A similar ‘selection’ mechanism is thought to control the representations held ‘in mind’—in working memory 5 – 10 . Here we show that shared neural mechanisms underlie the selection of items from working memory and attention to sensory stimuli. We trained rhesus monkeys to switch between two tasks, either selecting one item from a set of items held in working memory or attending to one stimulus from a set of visual stimuli. Neural recordings showed that similar representations in prefrontal cortex encoded the control of both selection and attention, suggesting that prefrontal cortex acts as a domain-general controller. By contrast, both attention and selection were represented independently in parietal and visual cortex. Both selection and attention facilitated behaviour by enhancing and transforming the representation of the selected memory or attended stimulus. Specifically, during the selection task, memory items were initially represented in independent subspaces of neural activity in prefrontal cortex. Selecting an item caused its representation to transform from its own subspace to a new subspace used to guide behaviour. A similar transformation occurred for attention. Our results suggest that prefrontal cortex controls cognition by dynamically transforming representations to control what and when cognitive computations are engaged. The prefrontal cortex in monkeys controls working memory in a similar way to attention, by selectively transforming the representations of remembered items.