Regularizing hyperparameters of interacting neural signals in the mouse cortex reflect states of arousal.

In natural behaviors, multiple neural signals simultaneously drive activation across overlapping brain networks. Due to limitations in the amount of data that can be acquired in common experimental designs, the determination of these interactions is commonly inferred via modeling approaches, which reduce overfitting by finding appropriate regularizing hyperparameters. However, it is unclear whether these hyperparameters can also be related to any aspect of the underlying biological phenomena and help interpret them. We applied a state-of-the-art regularization procedure-automatic locality determination-to interacting neural activations in the mouse posterior cortex associated with movements of the body and eyes. As expected, regularization significantly improved the determination and interpretability of the response interactions. However, regularizing hyperparameters also changed considerably, and seemingly unpredictably, from animal to animal. We found that these variations were not random; rather, they correlated with the variability in visually evoked responses and with the variability in the state of arousal of the animals measured by pupillometry-both pieces of information that were not included in the modeling framework. These observations could be generalized to another commonly used-but potentially less informative-regularization method, ridge regression. Our findings demonstrate that optimal model hyperparameters can be discovery tools that are informative of factors not a priori included in the model's design.

Probabilistic discrimination of relative stimulus features in mice.

During perceptual decision-making, the brain encodes the upcoming decision and the stimulus information in a mixed representation. Paradigms suitable for studying decision computations in isolation rely on stimulus comparisons, with choices depending on relative rather than absolute properties of the stimuli. The adoption of tasks requiring relative perceptual judgments in mice would be advantageous in view of the powerful tools available for the dissection of brain circuits. However, whether and how mice can perform a relative visual discrimination task has not yet been fully established. Here, we show that mice can solve a complex orientation discrimination task in which the choices are decoupled from the orientation of individual stimuli. Moreover, we demonstrate a typical discrimination acuity of 9°, challenging the common belief that mice are poor visual discriminators. We reached these conclusions by introducing a probabilistic choice model that explained behavioral strategies in 40 mice and demonstrated that the circularity of the stimulus space is an additional source of choice variability for trials with fixed difficulty. Furthermore, history biases in the model changed with task engagement, demonstrating behavioral sensitivity to the availability of cognitive resources. In conclusion, our results reveal that mice adopt a diverse set of strategies in a task that decouples decision-relevant information from stimulus-specific information, thus demonstrating their usefulness as an animal model for studying neural representations of relative categories in perceptual decision-making research.

Motor-related signals support localization invariance for stable visual perception.

Our ability to perceive a stable visual world in the presence of continuous movements of the body, head, and eyes has puzzled researchers in the neuroscience field for a long time. We reformulated this problem in the context of hierarchical convolutional neural networks (CNNs)-whose architectures have been inspired by the hierarchical signal processing of the mammalian visual system-and examined perceptual stability as an optimization process that identifies image-defining features for accurate image classification in the presence of movements. Movement signals, multiplexed with visual inputs along overlapping convolutional layers, aided classification invariance of shifted images by making the classification faster to learn and more robust relative to input noise. Classification invariance was reflected in activity manifolds associated with image categories emerging in late CNN layers and with network units acquiring movement-associated activity modulations as observed experimentally during saccadic eye movements. Our findings provide a computational framework that unifies a multitude of biological observations on perceptual stability under optimality principles for image classification in artificial neural networks.

Imaging the awake visual cortex with a genetically encoded voltage indicator.

Genetically encoded voltage indicators (GEVIs) promise to reveal the membrane potential of genetically targeted neuronal populations through noninvasive, chronic imaging of large portions of cortical space. Here we test a promising GEVI in mouse cortex during wakefulness, a challenging condition due to large hemodynamic activity, and we introduce a straightforward projection method to separate a signal dominated by membrane voltage from a signal dominated by hemodynamic activity. We expressed VSFP-Butterfly 1.2 plasmid in layer 2/3 pyramidal cells of visual cortex through electroporation in utero. We then used wide-field imaging with two cameras to measure both fluorophores of the indicator in response to visual stimuli. By taking weighted sums and differences of the two measurements, we obtained clear separation of hemodynamic and voltage signals. The hemodynamic signal showed strong heartbeat oscillations, superimposed on slow dynamics similar to blood oxygen level-dependent (BOLD) or "intrinsic" signals. The voltage signal had fast dynamics similar to neural responses measured electrically, and showed an orderly retinotopic mapping. We compared this voltage signal with calcium signals imaged in transgenic mice that express a calcium indicator (GCaMP3) throughout cortex. The voltage signal from VSFP had similar signal-to-noise ratios as the calcium signal, it was more immune to vascular artifacts, and it integrated over larger regions of visual space, which was consistent with its reporting mostly subthreshold activity rather than the spiking activity revealed by calcium signals. These results demonstrate that GEVIs provide a powerful tool to study the dynamics of neural populations at mesoscopic spatial scales in the awake cortex.

Cortical state determines global variability and correlations in visual cortex.

The response of neurons in sensory cortex to repeated stimulus presentations is highly variable. To investigate the nature of this variability, we compared the spike activity of neurons in the primary visual cortex (V1) of cats with that of their afferents from lateral geniculate nucleus (LGN), in response to similar stimuli. We found variability to be much higher in V1 than in LGN. To investigate the sources of the additional variability, we measured the spiking activity of large V1 populations and found that much of the variability was shared across neurons: the variable portion of the responses of one neuron could be well predicted from the summed activity of the rest of the neurons. Variability thus mostly reflected global fluctuations affecting all neurons. The size and prevalence of these fluctuations, both in responses to stimuli and in ongoing activity, depended on cortical state, being larger in synchronized states than in more desynchronized states. Contrary to previous reports, these fluctuations invested the overall population, regardless of preferred orientation. The global fluctuations substantially increased variability in single neurons and correlations among pairs of neurons. Once this effect was removed, pairwise correlations were reduced and were similar regardless of cortical state. These results highlight the importance of cortical state in controlling cortical operation and can help reconcile previous studies, which differed widely in their estimate of neuronal variability and pairwise correlations.

Local and global contributions to hemodynamic activity in mouse cortex.

Imaging techniques such as functional magnetic resonance imaging seek to estimate neural signals in local brain regions through measurements of hemodynamic activity. However, hemodynamic activity is accompanied by large vascular fluctuations of unclear significance. To characterize these fluctuations and their impact on estimates of neural signals, we used optical imaging in visual cortex of awake mice. We found that hemodynamic activity can be expressed as the sum of two components, one local and one global. The local component reflected presumed neural signals driven by visual stimuli in the appropriate retinotopic region. The global component constituted large fluctuations shared by larger cortical regions, which extend beyond visual cortex. These fluctuations varied from trial to trial, but they did not constitute noise; they correlated with pupil diameter, suggesting that they reflect variations in arousal or alertness. Distinguishing local and global contributions to hemodynamic activity may help understand neurovascular coupling and interpret measurements of hemodynamic responses.

Fast hemodynamic responses in the visual cortex of the awake mouse.

Hemodynamic responses in mice and other species are typically measured under anesthesia. However, anesthesia could influence their relationship to neural activity. To investigate this relationship, we used optical imaging in mouse primary visual cortex (V1). Hemodynamic responses yielded clear maps of retinotopy in both anesthetized and awake mice. However, during wakefulness, responses were four times larger and twice as fast. These differences held whether we induced anesthesia with urethane or isoflurane and whether awake mice were stationary or running on a treadmill. With electrode recordings, we established that the effects of wakefulness reflect changes in neurovascular coupling, not in neural activity. By activating V1 directly via optogenetics, we replicated the effects of wakefulness in terms of timing but not of amplitude. We conclude that neurovascular coupling depends critically on anesthesia and wakefulness: during wakefulness, neural activity is followed by much stronger and quicker hemodynamic responses.

Efficient coding of natural images in the mouse visual cortex.

How the activity of neurons gives rise to natural vision remains a matter of intense investigation. The mid-level visual areas along the ventral stream are selective to a common class of natural images-textures-but a circuit-level understanding of this selectivity and its link to perception remains unclear. We addressed these questions in mice, first showing that they can perceptually discriminate between textures and statistically simpler spectrally matched stimuli, and between texture types. Then, at the neural level, we found that the secondary visual area (LM) exhibited a higher degree of selectivity for textures compared to the primary visual area (V1). Furthermore, textures were represented in distinct neural activity subspaces whose relative distances were found to correlate with the statistical similarity of the images and the mice's ability to discriminate between them. Notably, these dependencies were more pronounced in LM, where the texture-related subspaces were smaller than in V1, resulting in superior stimulus decoding capabilities. Together, our results demonstrate texture vision in mice, finding a linking framework between stimulus statistics, neural representations, and perceptual sensitivity-a distinct hallmark of efficient coding computations.

Population rate dynamics and multineuron firing patterns in sensory cortex.

Cortical circuits encode sensory stimuli through the firing of neuronal ensembles, and also produce spontaneous population patterns in the absence of sensory drive. This population activity is often characterized experimentally by the distribution of multineuron "words" (binary firing vectors), and a match between spontaneous and evoked word distributions has been suggested to reflect learning of a probabilistic model of the sensory world. We analyzed multineuron word distributions in sensory cortex of anesthetized rats and cats, and found that they are dominated by fluctuations in population firing rate rather than precise interactions between individual units. Furthermore, cortical word distributions change when brain state shifts, and similar behavior is seen in simulated networks with fixed, random connectivity. Our results suggest that similarity or dissimilarity in multineuron word distributions could primarily reflect similarity or dissimilarity in population firing rate dynamics, and not necessarily the precise interactions between neurons that would indicate learning of sensory features.

Distributed context-dependent choice information in mouse posterior cortex.

Choice information appears in multi-area brain networks mixed with sensory, motor, and cognitive variables. In the posterior cortex-traditionally implicated in decision computations-the presence, strength, and area specificity of choice signals are highly variable, limiting a cohesive understanding of their computational significance. Examining the mesoscale activity in the mouse posterior cortex during a visual task, we found that choice signals defined a decision variable in a low-dimensional embedding space with a prominent contribution along the ventral visual stream. Their subspace was near-orthogonal to concurrently represented sensory and motor-related activations, with modulations by task difficulty and by the animals' attention state. A recurrent neural network trained with animals' choices revealed an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. Our results demonstrated an independent, multi-area decision variable in the posterior cortex, controlled by task features and cognitive demands, possibly linked to contextual inference computations in dynamic animal-environment interactions.

Thalamocortical control of cell-type specificity drives circuits for processing whisker-related information in mouse barrel cortex.

Excitatory spiny stellate neurons are prominently featured in the cortical circuits of sensory modalities that provide high salience and high acuity representations of the environment. These specialized neurons are considered developmentally linked to bottom-up inputs from the thalamus, however, the molecular mechanisms underlying their diversification and function are unknown. Here, we investigated this in mouse somatosensory cortex, where spiny stellate neurons and pyramidal neurons have distinct roles in processing whisker-evoked signals. Utilizing spatial transcriptomics, we identified reciprocal patterns of gene expression which correlated with these cell-types and were linked to innervation by specific thalamic inputs during development. Genetic manipulation that prevents the acquisition of spiny stellate fate highlighted an important role for these neurons in processing distinct whisker signals within functional cortical columns, and as a key driver in the formation of specific whisker-related circuits in the cortex.

Standing waves and traveling waves distinguish two circuits in visual cortex.

The visual cortex represents stimuli through the activity of neuronal populations. We measured the evolution of this activity in space and time by imaging voltage-sensitive dyes in cat area V1. Contrast-reversing stimuli elicit responses that oscillate at twice the stimulus frequency, indicating that signals originate mostly in complex cells. These responses stand clear of the noise, whose amplitude decreases as 1/frequency, and yield high-resolution maps of orientation preference and retinotopy. We first show how these maps are combined to yield the responses to focal, oriented stimuli. We then study the evolution of the oscillating activity in space and time. In the orientation domain, it is a standing wave. In the spatial domain, it is a traveling wave propagating at 0.2-0.5 m/s. These different dynamics indicate a fundamental distinction in the circuits underlying selectivity for position and orientation, two key stimulus attributes.

Attention separates sensory and motor signals in the mouse visual cortex.

Visually guided behaviors depend on the activity of cortical networks receiving visual inputs and transforming these signals to guide appropriate actions. However, non-retinal inputs, carrying motor signals as well as cognitive and attentional modulatory signals, also activate these cortical regions. How these networks integrate coincident signals ensuring reliable visual behaviors is poorly understood. In this study, we observe neural responses in the dorsal-parietal cortex of mice during a visual discrimination task driven by visual stimuli and movements. We find that visual and motor signals interact according to two mechanisms: divisive normalization and separation of responses. Interactions are contextually modulated by the animal's state of sustained attention, which amplifies visual and motor signals and increases their discriminability in a low-dimensional space of neural activations. These findings reveal computational principles operating in dorsal-parietal networks that enable separation of incoming signals for reliable visually guided behaviors during interactions with the environment.

The mouse posterior parietal cortex: Anatomy and functions.

In recent years, the number of studies on decision-making in mice has increased dramatically. Many of these studies focus on the posterior parietal cortex (PPC), an area that has been implicated in sensory and multisensory processing, navigation, motion planning, and decision-making. In this review we summarize recent anatomical and functional studies of mouse PPC. First, we make a note of the existing variability in the nomenclature and its anatomical localization. Based on the commonalities across different studies we then describe the connectivity of PPC and discuss its place within several functional brain networks. In view of the examined connectivity, we go on to discuss the role of PPC for the encoding of single-modality and multimodal stimuli as well as its role in navigation. Finally, we summarize the literature on the choice-related activity: we discuss the variety of behavioral protocols and sensory modalities used in these studies, and we note that the response properties of PPC and its causal involvement in decision-making may depend substantially on these conditions. We conclude that, although more research should be devoted to creating a more complete and consistent image of the mouse PPC, this area should rightfully be considered a convenient model system for a circuit-level understanding of the mammalian parietal cortex.

Physiological Signature of Memory Age in the Prefrontal-Hippocampal Circuit.

The long-term storage of episodic memory requires communication between prefrontal cortex and hippocampus. However, how consolidation alters dynamic interactions between these regions during subsequent recall remains unexplored. Here we perform simultaneous electrophysiological recordings from anterior cingulate cortex (ACC) and hippocampal CA1 in mice during recall of recent and remote contextual fear memory. We find that, in contrast to recent memory, remote memory recall is accompanied by increased ACC-CA1 synchronization at multiple frequency bands. The augmented ACC-CA1 interaction is associated with strengthened coupling among distally spaced CA1 neurons, suggesting an ACC-driven organization of a sparse code. This robust shift in physiology permits a support vector machine classifier to accurately determine memory age on the basis of the ACC-CA1 synchronization pattern. Our findings reveal that memory consolidation alters the dynamic coupling of the prefrontal-hippocampal circuit and results in a physiological signature of memory age.

An automated platform for high-throughput mouse behavior and physiology with voluntary head-fixation.

Recording neural activity during animal behavior is a cornerstone of modern brain research. However, integration of cutting-edge technologies for neural circuit analysis with complex behavioral measurements poses a severe experimental bottleneck for researchers. Critical problems include a lack of standardization for psychometric and neurometric integration, and lack of tools that can generate large, sharable data sets for the research community in a time and cost effective way. Here, we introduce a novel mouse behavioral learning platform featuring voluntary head fixation and automated high-throughput data collection for integrating complex behavioral assays with virtually any physiological device. We provide experimental validation by demonstrating behavioral training of mice in visual discrimination and auditory detection tasks. To examine facile integration with physiology systems, we coupled the platform to a two-photon microscope for imaging of cortical networks at single-cell resolution. Our behavioral learning and recording platform is a prototype for the next generation of mouse cognitive studies.

Transgenic mice for intersectional targeting of neural sensors and effectors with high specificity and performance.

An increasingly powerful approach for studying brain circuits relies on targeting genetically encoded sensors and effectors to specific cell types. However, current approaches for this are still limited in functionality and specificity. Here we utilize several intersectional strategies to generate multiple transgenic mouse lines expressing high levels of novel genetic tools with high specificity. We developed driver and double reporter mouse lines and viral vectors using the Cre/Flp and Cre/Dre double recombinase systems and established a new, retargetable genomic locus, TIGRE, which allowed the generation of a large set of Cre/tTA-dependent reporter lines expressing fluorescent proteins, genetically encoded calcium, voltage, or glutamate indicators, and optogenetic effectors, all at substantially higher levels than before. High functionality was shown in example mouse lines for GCaMP6, YCX2.60, VSFP Butterfly 1.2, and Jaws. These novel transgenic lines greatly expand the ability to monitor and manipulate neuronal activities with increased specificity.

High-order events in cortical networks: a lower bound.

It is commonly believed that information processing in cortical networks involves the collective spiking activity of neuronal assemblies. Nevertheless, due to current technical limitations in multielectrodes recording methods, it is not possible to tackle this issue with direct experimental measurements. In this study we simulate spiking activity of large ensembles of cells focusing on the temporal correlation properties of the neuronal dynamics, and demonstrate that transient, fast synchronization of large groups of cells is a natural phenomenon of the cortical activity. To prove this result we use a statistical approach (based on combinatorics), and knowledge derived from a previous research work [A. Benucci et al., Phys. Rev. E 68, 041905 (2003)]. We quantify the degree of synchronous activity by computing a lower bound for the fraction of cells participating in fast (few milliseconds) synchronous events. Finally we discuss the implications of the results found in terms of cortical coding mechanisms.

Existence of high-order correlations in cortical activity.

Neurons collect signals originating from a large number of other cells. The variability of this integrated population activity at the millisecond time scale is a critical constraint on the degree of signal integration and processing performed by single neurons. Optical imaging, EEG, and fMRI studies have indicated that cortical activity shows a high degree of variability at a time scale of hundreds of ms. However, currently no experimental methods are available to directly assess the variability in the activity of populations of neurons at a time scale closer to that of the characteristic time constants of neurons, i.e., around 10 ms. Here we integrate pertinent experimental data in one rigorous mathematical framework to demonstrate that (1) the high temporal variability in the spiking activity of individual neurons, (2) the second-order correlation properties of the spiking activity of cortical neurons, and (3) the correlations of the subthreshold dynamics, all impose high amplitude, fast variability in the population activity of cortical neurons. This implies that higher order correlations, a necessary condition for temporal coding models, must be a central feature of cortical dynamics.

Adaptation maintains population homeostasis in primary visual cortex.

Sensory systems exhibit mechanisms of neural adaptation, which adjust neuronal activity on the basis of recent stimulus history. In primary visual cortex (V1) in particular, adaptation controls the responsiveness of individual neurons and shifts their visual selectivity. What benefits does adaptation confer on a neuronal population? We measured adaptation in the responses of populations of cat V1 neurons to stimulus ensembles with markedly different statistics of stimulus orientation. We found that adaptation served two homeostatic goals. First, it maintained equality in the time-averaged responses across the population. Second, it maintained independence in selectivity across the population. Adaptation scaled and distorted population activity according to a simple multiplicative rule that depended on neuronal orientation preference and on stimulus orientation. We conclude that adaptation in V1 acts as a mechanism of homeostasis, enforcing a tendency toward equality and independence in neural activity across the population.