On the contrast response function of adapted neural populations.

The magnitude of neural responses in sensory cortex depends on the intensity of a stimulus and its probability of being observed within the environment. How these two variables combine to influence the overall response of cortical populations remains unknown. Here we show that, in primary visual cortex, the vector magnitude of the population response is described by a separable power law that factors the intensity of a stimulus and its probability. Moreover, the discriminability between two contrast levels in a cortical population is proportional to the logarithm of the contrast ratio.NEW & NOTEWORTHY The magnitude of neural responses in sensory cortex depends on the intensity of a stimulus and its probability of being observed within the environment. The authors show that, in primary visual cortex, the vector magnitude of the population response is described by a separable power law that factors the intensity of a stimulus and its probability.

A power law describes the magnitude of adaptation in neural populations of primary visual cortex.

How do neural populations adapt to the time-varying statistics of sensory input? We used two-photon imaging to measure the activity of neurons in mouse primary visual cortex adapted to different sensory environments, each defined by a distinct probability distribution over a stimulus set. We find that two properties of adaptation capture how the population response to a given stimulus, viewed as a vector, changes across environments. First, the ratio between the response magnitudes is a power law of the ratio between the stimulus probabilities. Second, the response direction to a stimulus is largely invariant. These rules could be used to predict how cortical populations adapt to novel, sensory environments. Finally, we show how the power law enables the cortex to preferentially signal unexpected stimuli and to adjust the metabolic cost of its sensory representation to the entropy of the environment.

On the contrast response function of adapted neural populations.

The magnitude of neural responses in sensory cortex depends on the intensity of a stimulus and its probability of being observed within the environment. How these two variables combine to influence the overall response of cortical populations remains unknown. Here we show that, in primary visual cortex, the vector magnitude of the population response is described by a separable power-law that factors the intensity of a stimulus and its probability.

A power law of cortical adaptation.

How do neural populations adapt to the time-varying statistics of sensory input? To investigate, we measured the activity of neurons in primary visual cortex adapted to different environments, each associated with a distinct probability distribution over a stimulus set. Within each environment, a stimulus sequence was generated by independently sampling form its distribution. We find that two properties of adaptation capture how the population responses to a given stimulus, viewed as vectors, are linked across environments. First, the ratio between the response magnitudes is a power law of the ratio between the stimulus probabilities. Second, the response directions are largely invariant. These rules can be used to predict how cortical populations adapt to novel, sensory environments. Finally, we show how the power law enables the cortex to preferentially signal unexpected stimuli and to adjust the metabolic cost of its sensory representation to the entropy of the environment.

Thalamocortical boutons cluster by ON/OFF responses in mouse primary visual cortex.

In higher mammals, the thalamic afferents to primary visual cortex cluster according to their responses to increases (ON) or decreases (OFF) in luminance. This feature of thalamocortical wiring is thought to create columnar, ON/OFF domains in V1. We have recently shown that mice also have ON/OFF cortical domains, but the organization of their thalamic afferents remains unknown. Here we measured the visual responses of thalamocortical boutons with two-photon imaging and found that they also cluster in space according to ON/OFF responses. Moreover, fluctuations in the relative density of ON/OFF boutons mirror fluctuations in the relative density of ON/OFF receptive field positions on the visual field. These findings indicate a segregation of ON/OFF signals already present in the thalamic input. We propose that ON/OFF clustering may reflect the spatial distribution of ON/OFF responses in retinal ganglion cell mosaics.NEW & NOTEWORTHY Neurons in primary visual cortex cluster into ON and OFF domains, which have been shown to be linked to the organization of receptive fields and cortical maps. Here we show that in the mouse such clustering is already present in the geniculate input, suggesting that the cortical architecture may be shaped by the representation of ON/OFF signals in the thalamus and the retina.

ON/OFF domains shape receptive field structure in mouse visual cortex.

In higher mammals, thalamic afferents to primary visual cortex (area V1) segregate according to their responses to increases (ON) or decreases (OFF) in luminance. This organization induces columnar, ON/OFF domains postulated to provide a scaffold for the emergence of orientation tuning. To further test this idea, we asked whether ON/OFF domains exist in mouse V1. Here we show that mouse V1 is indeed parceled into ON/OFF domains. Interestingly, fluctuations in the relative density of ON/OFF neurons on the cortical surface mirror fluctuations in the relative density of ON/OFF receptive field centers on the visual field. Moreover, the local diversity of cortical receptive fields is explained by a model in which neurons linearly combine a small number of ON and OFF signals available in their cortical neighborhoods. These findings suggest that ON/OFF domains originate in fluctuations of the balance between ON/OFF responses across the visual field which, in turn, shapes the structure of cortical receptive fields.

The Development of Receptive Field Tuning Properties in Mouse Binocular Primary Visual Cortex.

The mouse primary visual cortex is a model system for understanding the relationship between cortical structure, function, and behavior (Seabrook et al., 2017; Chaplin and Margrie, 2020; Hooks and Chen, 2020; Saleem, 2020; Flossmann and Rochefort, 2021). Binocular neurons in V1 are the cellular basis of binocular vision, which is required for predation (Scholl et al., 2013; Hoy et al., 2016; La Chioma et al., 2020; Berson, 2021; Johnson et al., 2021). The normal development of binocular responses, however, has not been systematically measured. Here, we measure tuning properties of neurons to either eye in awake mice of either sex from eye opening to the closure of the critical period. At eye opening, we find an adult-like fraction of neurons responding to the contralateral-eye stimulation, which are selective for orientation and spatial frequency; few neurons respond to ipsilateral eye, and their tuning is immature. Fraction of ipsilateral-eye responses increases rapidly in the first few days after eye opening and more slowly thereafter, reaching adult levels by critical period closure. Tuning of these responses improves with a similar time course. The development and tuning of binocular responses parallel that of ipsilateral-eye responses. Four days after eye opening, monocular neurons respond to a full range of orientations but become more biased to cardinal orientations. Binocular responses, by contrast, lose their cardinal bias with age. Together, these data provide an in-depth accounting of the development of monocular and binocular responses in the binocular region of mouse V1 using a consistent set of visual stimuli and measurements.SIGNIFICANCE STATEMENT In this manuscript, we present a full accounting of the emergence and refinement of monocular and binocular receptive field tuning properties of thousands of pyramidal neurons in mouse primary visual cortex. Our data reveal new features of monocular and binocular development that revise current models on the emergence of cortical binocularity. Given the recent interest in visually guided behaviors in mice that require binocular vision (e.g., predation), our measures will provide the basis for studies on the emergence of the neural circuitry guiding these behaviors.

Vision is required for the formation of binocular neurons prior to the classical critical period.

Depth perception emerges from the development of binocular neurons in primary visual cortex. Vision is required for these neurons to acquire their mature responses to visual stimuli. The prevailing view is that vision does not influence binocular circuitry until the onset of the critical period, about a week after eye opening, and that plasticity of visual responses is triggered by increased inhibition. Here, we show that vision is required to form binocular neurons and to improve binocular tuning and matching from eye opening until critical period closure. Enhancing inhibition does not accelerate this process. Vision soon after eye opening improves the tuning properties of binocular neurons by strengthening and sharpening ipsilateral eye cortical responses. This progressively changes the population of neurons in the binocular pool, and this plasticity is sensitive to interocular differences prior to critical period onset. Thus, vision establishes binocular circuitry and guides binocular plasticity from eye opening.

Sparse thalamocortical convergence.

How many thalamic neurons converge onto a cortical cell? This is an important question, because the organization of thalamocortical projections can influence the cortical architecture.1,2 Here, we estimate the degree of thalamocortical convergence in primary visual cortex by taking advantage of the cortical expansion-neurons within a restricted volume in primary visual cortex have overlapping receptive fields driven by a smaller set of inputs from the lateral geniculate nucleus.3-5 Under these conditions, the measurements of cortical receptive fields in a population can be used to infer the receptive fields of their geniculate inputs and the weights of their projections using non-negative matrix factorization.6 The analysis reveals sparse connectivity,7 where a handful (~2-6) of thalamic inputs account for 90% of the total synaptic weight to a cortical neuron. Together with previous findings,8 these results paint a picture consistent with the idea that convergence of a few inputs partly determine the retinotopy and tuning properties of cortical cells.8-13.

Vision Changes the Cellular Composition of Binocular Circuitry during the Critical Period.

High acuity stereopsis emerges during an early postnatal critical period when binocular neurons in the primary visual cortex sharpen their receptive field tuning properties. We find that this sharpening is achieved by dismantling the binocular circuit present at critical period onset and building it anew. Longitudinal imaging of receptive field tuning (e.g., orientation selectivity) of thousands of neurons reveals that most binocular neurons present in layer 2/3 at critical period onset are poorly tuned and are rendered monocular. In parallel, new binocular neurons are established by conversion of well-tuned monocular neurons as they gain matched input from the other eye. These improvements in binocular tuning in layer 2/3 are not inherited from layer 4 but are driven by the experience-dependent sharpening of ipsilateral eye responses. Thus, vision builds a new and more sharply tuned binocular circuit in layer 2/3 by cellular exchange and not by refining the original circuit.

Functional Clusters of Neurons in Layer 6 of Macaque V1.

Layer 6 appears to perform a very important role in the function of macaque primary visual cortex, V1, but not enough is understood about the functional characteristics of neurons in the layer 6 population. It is unclear to what extent the population is homogeneous with respect to their visual properties or if one can identify distinct subpopulations. Here we performed a cluster analysis based on measurements of the responses of single neurons in layer 6 of primary visual cortex in male macaque monkeys (Macaca fascicularis) to achromatic grating stimuli that varied in orientation, direction of motion, spatial and temporal frequency, and contrast. The visual stimuli were presented in a stimulus window that was also varied in size. Using the responses to parametric variation in these stimulus variables, we extracted a number of tuning response measures and used them in the cluster analysis. Six main clusters emerged along with some smaller clusters. Additionally, we asked whether parameter distributions from each of the clusters were statistically different. There were clear separations of parameters between some of the clusters, particularly for f1/f0 ratio, direction selectivity, and temporal frequency bandwidth, but other dimensions also showed differences between clusters. Our data suggest that in layer 6 there are multiple parallel circuits that provide information about different aspects of the visual stimulus.SIGNIFICANCE STATEMENT The cortex is multilayered and is involved in many high-level computations. In the current study, we have asked whether there are subpopulations of neurons, clusters, in layer 6 of cortex with different functional tuning properties that provide information about different aspects of the visual image. We identified six major functional clusters within layer 6. These findings show that there is much more complexity to the circuits in cortex than previously demonstrated and open up a new avenue for experimental investigation within layers of other cortical areas and for the elaboration of models of circuit function that incorporate many parallel pathways with different functional roles.

The geometry of masking in neural populations.

The normalization model provides an elegant account of contextual modulation in individual neurons of primary visual cortex. Understanding the implications of normalization at the population level is hindered by the heterogeneity of cortical neurons, which differ in the composition of their normalization pools and semi-saturation constants. Here we introduce a geometric approach to investigate contextual modulation in neural populations and study how the representation of stimulus orientation is transformed by the presence of a mask. We find that population responses can be embedded in a low-dimensional space and that an affine transform can account for the effects of masking. The geometric analysis further reveals a link between changes in discriminability and bias induced by the mask. We propose the geometric approach can yield new insights into the image processing computations taking place in early visual cortex at the population level while coping with the heterogeneity of single cell behavior.

Neuromodulatory control of localized dendritic spiking in critical period cortex.

Sensory experience in early postnatal life, during so-called critical periods, restructures neural circuitry to enhance information processing1. Why the cortex is susceptible to sensory instruction in early life and why this susceptibility wanes with age are unclear. Here we define a developmentally restricted engagement of inhibitory circuitry that shapes localized dendritic activity and is needed for vision to drive the emergence of binocular visual responses in the mouse primary visual cortex. We find that at the peak of the critical period for binocular plasticity, acetylcholine released from the basal forebrain during periods of heightened arousal directly excites somatostatin (SST)-expressing interneurons. Their inhibition of pyramidal cell dendrites and of fast-spiking, parvalbumin-expressing interneurons enhances branch-specific dendritic responses and somatic spike rates within pyramidal cells. By adulthood, this cholinergic sensitivity is lost, and compartmentalized dendritic responses are absent but can be re-instated by optogenetic activation of SST cells. Conversely, suppressing SST cell activity during the critical period prevents the normal development of binocular receptive fields by impairing the maturation of ipsilateral eye inputs. This transient cholinergic modulation of SST cells, therefore, seems to orchestrate two features of neural plasticity-somatic disinhibition and compartmentalized dendritic spiking. Loss of this modulation may contribute to critical period closure.

State-Dependent Subnetworks of Parvalbumin-Expressing Interneurons in Neocortex.

Brain state determines patterns of spiking output that underlie behavior. In neocortex, brain state is reflected in the spontaneous activity of the network, which is regulated in part by neuromodulatory input from the brain stem and by local inhibition. We find that fast-spiking, parvalbumin-expressing inhibitory neurons, which exert state-dependent control of network gain and spike patterns, cluster into two stable and functionally distinct subnetworks that are differentially engaged by ascending neuromodulation. One group is excited as a function of increased arousal state; this excitation is driven in part by the increase in cortical norepinephrine that occurs when the locus coeruleus is active. A second group is suppressed during movement when acetylcholine is released into the cortex via projections from the nucleus basalis. These data establish the presence of functionally independent subnetworks of Parvalbumin (PV) cells in the upper layers of the neocortex that are differentially engaged by the ascending reticular activating system.

Active learning of cortical connectivity from two-photon imaging data.

Understanding how groups of neurons interact within a network is a fundamental question in system neuroscience. Instead of passively observing the ongoing activity of a network, we can typically perturb its activity, either by external sensory stimulation or directly via techniques such as two-photon optogenetics. A natural question is how to use such perturbations to identify the connectivity of the network efficiently. Here we introduce a method to infer sparse connectivity graphs from in-vivo, two-photon imaging of population activity in response to external stimuli. A novel aspect of the work is the introduction of a recommended distribution, incrementally learned from the data, to optimally refine the inferred network. Unlike existing system identification techniques, this "active learning" method automatically focuses its attention on key undiscovered areas of the network, instead of targeting global uncertainty indicators like parameter variance. We show how active learning leads to faster inference while, at the same time, provides confidence intervals for the network parameters. We present simulations on artificial small-world networks to validate the methods and apply the method to real data. Analysis of frequency of motifs recovered show that cortical networks are consistent with a small-world topology model.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

Local tuning biases in mouse primary visual cortex.

Neurons in primary visual cortex are selective to the orientation and spatial frequency of sinusoidal gratings. In the classic model of cortical organization, a population of neurons responding to the same region of the visual field but tuned to all possible feature combinations provides a detailed representation of the local image. Such a functional module is assumed to be replicated across primary visual cortex to provide a uniform representation of the image across the entire visual field. In contrast, it has been hypothesized that the tiling properties of ON- and OFF-center receptive fields in the retina, largely mirrored in the geniculate, may constrain cortical tuning at each location in the visual field. This model predicts the existence of local biases in tuning that vary across the visual field and would prevent the cortex from developing a uniform, modular representation as postulated by the classic model. Here, we confirm the existence of local tuning biases in the primary visual cortex of the mouse, lending support to the notion that cortical tuning may be constrained by signals from the periphery. NEW & NOTEWORTHY Populations of cortical neurons responding to the same part of the visual field are shown to have similar tuning. Such local biases are consistent with the hypothesis that cortical tuning, in mouse primary visual cortex, is constrained by signals from the periphery.

On the Subspace Invariance of Population Responses.

In cat visual cortex, the response of a neural population to the linear combination of two sinusoidal gratings (a plaid) can be well approximated by a weighted sum of the population responses to the individual gratings - a property we refer to as subspace invariance. We tested subspace invariance in mouse primary visual cortex by measuring the angle between the population response to a plaid and the plane spanned by the population responses to its individual components. We found robust violations of subspace invariance arising from a strong, negative correlation between the responses of neurons to individual gratings and their responses to the plaid. Contrast invariance, a special case of subspace invariance, also failed. The responses of some neurons decreased with increasing contrast, while others increased. Altogether the data show that subspace and contrast invariance do not hold in mouse primary visual cortex. These findings rule out some models of population coding, including vector averaging, some versions of normalization and temporal multiplexing.

An inhibitory pull-push circuit in frontal cortex.

Push-pull is a canonical computation of excitatory cortical circuits. By contrast, we identify a pull-push inhibitory circuit in frontal cortex that originates in vasoactive intestinal polypeptide (VIP)-expressing interneurons. During arousal, VIP cells rapidly and directly inhibit pyramidal neurons; VIP cells also indirectly excite these pyramidal neurons via parallel disinhibition. Thus, arousal exerts a feedback pull-push influence on excitatory neurons-an inversion of the canonical push-pull of feedforward input.

Animal Research in Neuroscience: A Duty to Engage.

Activists opposed to the use of animals in scientific research are increasingly dominating the public discourse and pressuring government officials to severely limit the scope of the work. Polls show that public support for animal research is in decline. Scientists must respond by engaging with the public and policymakers to explain their research and its importance and by addressing moral concerns and objections.