FISSA: A neuropil decontamination toolbox for calcium imaging signals

Accurate neuron segmentation method for one-photon calcium imaging videos combining convolutional neural networks and clustering

One-photon fluorescent calcium imaging helps understand brain functions by recording large-scale neural activities in freely moving animals. Automatic, fast, and accurate active neuron segmentation algorithms are essential to extract and interpret information from these videos. One-photon imaging videos' low resolution, high noise, and high background fluctuation pose significant challenges. Here, we develop a software pipeline to address the challenges of processing one-photon calcium imaging videos. We extend our previous two-photon active neuron segmentation algorithm, Shallow U-Net Neuron Segmentation (SUNS), to better suppress background fluctuations in one-photon videos. We also develop additional neuron extraction (ANE) to locate small or dim neurons missed by SUNS. To train our segmentation method, we create ground truth neurons by developing a manual labeling pipeline assisted with semi-automatic refinement. Our method is more accurate and faster than state-of-the-art techniques when processing simulated videos and multiple experimental datasets acquired over various brain regions with different imaging conditions.

Sex-specific resilience of neocortex to food restriction

Mammals have evolved sex-specific adaptations to reduce energy usage in times of food scarcity. These adaptations are well described for peripheral tissue, though much less is known about how the energy-expensive brain adapts to food restriction, and how such adaptations differ across the sexes. Here, we examined how food restriction impacts energy usage and function in the primary visual cortex (V1) of adult male and female mice. Molecular analysis and RNA sequencing in V1 revealed that in males, but not in females, food restriction significantly modulated canonical, energy-regulating pathways, including pathways associated waith AMP-activated protein kinase, peroxisome proliferator-activated receptor alpha, mammalian target of rapamycin, and oxidative phosphorylation. Moreover, we found that in contrast to males, food restriction in females did not significantly affect V1 ATP usage or visual coding precision (assessed by orientation selectivity). Decreased serum leptin is known to be necessary for triggering energy-saving changes in V1 during food restriction. Consistent with this, we found significantly decreased serum leptin in food-restricted males but no significant change in food-restricted females. Collectively, our findings demonstrate that cortical function and energy usage in female mice are more resilient to food restriction than in males. The neocortex, therefore, contributes to sex-specific, energy-saving adaptations in response to food restriction.

Cortical Zinc Signaling Is Necessary for Changes in Mouse Pupil Diameter That Are Evoked by Background Sounds with Different Contrasts

Luminance-independent changes in pupil diameter (PD) during wakefulness influence and are influenced by neuromodulatory, neuronal, and behavioral responses. However, it is unclear whether changes in neuromodulatory activity in a specific brain area are necessary for the associated changes in PD or whether some different mechanisms cause parallel fluctuations in both PD and neuromodulation. To answer this question, we simultaneously recorded PD and cortical neuronal activity in male and female mice. Namely, we measured PD and neuronal activity during adaptation to sound contrast, which is a well-described adaptation conserved in many species and brain areas. In the primary auditory cortex (A1), increases in the variability of sound level (contrast) induce a decrease in the slope of the neuronal input-output relationship, neuronal gain, which depends on cortical neuromodulatory zinc signaling. We found a previously unknown modulation of PD by changes in background sensory context: high stimulus contrast sounds evoke larger increases in evoked PD compared with low-contrast sounds. To explore whether these changes in evoked PD are controlled by cortical neuromodulatory zinc signaling, we imaged single-cell neural activity in A1, manipulated zinc signaling in the cortex, and assessed PD in the same awake mouse. We found that cortical synaptic zinc signaling is necessary for increases in PD during high-contrast background sounds compared with low-contrast sounds. This finding advances our knowledge about how cortical neuromodulatory activity affects PD changes and thus advances our understanding of the brain states, circuits, and neuromodulatory mechanisms that can be inferred from pupil size fluctuations.

Interpretable deep learning for deconvolutional analysis of neural signals

The widespread adoption of deep learning to build models that capture the dynamics of neural populations is typically based on "black-box" approaches that lack an interpretable link between neural activity and function. Here, we propose to apply algorithm unrolling, a method for interpretable deep learning, to design the architecture of sparse deconvolutional neural networks and obtain a direct interpretation of network weights in relation to stimulus-driven single-neuron activity through a generative model. We characterize our method, referred to as deconvolutional unrolled neural learning (DUNL), and show its versatility by applying it to deconvolve single-trial local signals across multiple brain areas and recording modalities. To exemplify use cases of our decomposition method, we uncover multiplexed salience and reward prediction error signals from midbrain dopamine neurons in an unbiased manner, perform simultaneous event detection and characterization in somatosensory thalamus recordings, and characterize the responses of neurons in the piriform cortex. Our work leverages the advances in interpretable deep learning to gain a mechanistic understanding of neural dynamics.

Deep-brain optical recording of neural dynamics during behavior

In vivo fluorescence recording techniques have produced landmark discoveries in neuroscience, providing insight into how single cell and circuit-level computations mediate sensory processing and generate complex behaviors. While much attention has been given to recording from cortical brain regions, deep-brain fluorescence recording is more complex because it requires additional measures to gain optical access to harder to reach brain nuclei. Here we discuss detailed considerations and tradeoffs regarding deep-brain fluorescence recording techniques and provide a comprehensive guide for all major steps involved, from project planning to data analysis. The goal is to impart guidance for new and experienced investigators seeking to use in vivo deep fluorescence optical recordings in awake, behaving rodent models.

Vectorized instructive signals in cortical dendrites during a brain-computer interface task

Backpropagation of error is the most widely used learning algorithm in artificial neural networks, forming the backbone of modern machine learning and artificial intelligence1,2. Backpropagation provides a solution to the credit assignment problem by vectorizing an error signal tailored to individual neurons. Recent theoretical models have suggested that neural circuits could implement backpropagation-like learning by semi-independently processing feedforward and feedback information streams in separate dendritic compartments3-7. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We designed a neurofeedback brain-computer interface (BCI) task with an experimenter-defined reward function to evaluate the key requirements for dendrites to implement backpropagation-like learning. We trained mice to modulate the activity of two spatially intermingled populations (4 or 5 neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic versus dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals both depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. These results provide the first biological evidence of a backpropagation-like solution to the credit assignment problem in the brain.

Cell-type-specific plasticity of inhibitory interneurons in the rehabilitation of auditory cortex after peripheral damage

Peripheral sensory organ damage leads to compensatory cortical plasticity that is associated with a remarkable recovery of cortical responses to sound. The precise mechanisms that explain how this plasticity is implemented and distributed over a diverse collection of excitatory and inhibitory cortical neurons remain unknown. After noise trauma and persistent peripheral deficits, we found recovered sound-evoked activity in mouse A1 excitatory principal neurons (PNs), parvalbumin- and vasoactive intestinal peptide-expressing neurons (PVs and VIPs), but reduced activity in somatostatin-expressing neurons (SOMs). This cell-type-specific recovery was also associated with cell-type-specific intrinsic plasticity. These findings, along with our computational modelling results, are consistent with the notion that PV plasticity contributes to PN stability, SOM plasticity allows for increased PN and PV activity, and VIP plasticity enables PN and PV recovery by inhibiting SOMs.

Inhibitory control of sharp-wave ripple duration during learning in hippocampal recurrent networks

Recurrent excitatory connections in hippocampal regions CA3 and CA2 are thought to play a key role in the generation of sharp-wave ripples (SWRs), electrophysiological oscillations tightly linked with learning and memory consolidation. However, it remains unknown how defined populations of inhibitory interneurons regulate these events during behavior. Here, we use large-scale, three-dimensional calcium imaging and retrospective molecular identification in the mouse hippocampus to characterize molecularly identified CA3 and CA2 interneuron activity during SWR-associated memory consolidation and spatial navigation. We describe subtype- and region-specific responses during behaviorally distinct brain states and find that SWRs are preceded by decreased cholecystokinin-expressing interneuron activity and followed by increased parvalbumin-expressing basket cell activity. The magnitude of these dynamics correlates with both SWR duration and behavior during hippocampal-dependent learning. Together these results assign subtype- and region-specific roles for inhibitory circuits in coordinating operations and learning-related plasticity in hippocampal recurrent circuits.

SpecSeg is a versatile toolbox that segments neurons and neurites in chronic calcium imaging datasets based on low-frequency cross-spectral power

Imaging calcium signals in neurons of animals using single- or multi-photon microscopy facilitates the study of coding in large neural populations. Such experiments produce massive datasets requiring powerful methods to extract responses from hundreds of neurons. We present SpecSeg, an open-source toolbox for (1) segmentation of regions of interest (ROIs) representing neuronal structures, (2) inspection and manual editing of ROIs, (3) neuropil correction and signal extraction, and (4) matching of ROIs in sequential recordings. ROI segmentation in SpecSeg is based on temporal cross-correlations of low-frequency components derived by Fourier analysis of each pixel with its neighbors. The approach is user-friendly, intuitive, and insightful and enables ROI detection around neurons or neurites. It works for single- (miniscope) and multi-photon microscopy data, eliminating the need for separate toolboxes. SpecSeg thus provides an efficient and versatile approach for analyzing calcium responses in neuronal structures imaged over prolonged periods of time.

Review of data processing of functional optical microscopy for neuroscience

Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.

Fiber photometry in striatum reflects primarily nonsomatic changes in calcium

Fiber photometry enables recording of population neuronal calcium dynamics in awake mice. While the popularity of fiber photometry has grown in recent years, it remains unclear whether photometry reflects changes in action potential firing (that is, 'spiking') or other changes in neuronal calcium. In microscope-based calcium imaging, optical and analytical approaches can help differentiate somatic from neuropil calcium. However, these approaches cannot be readily applied to fiber photometry. As such, it remains unclear whether the fiber photometry signal reflects changes in somatic calcium, changes in nonsomatic calcium or a combination of the two. Here, using simultaneous in vivo extracellular electrophysiology and fiber photometry, along with in vivo endoscopic one-photon and two-photon calcium imaging, we determined that the striatal fiber photometry does not reflect spiking-related changes in calcium and instead primarily reflects nonsomatic changes in calcium.

SmaRT2P: a software for generating and processing smart line recording trajectories for population two-photon calcium imaging

Two-photon fluorescence calcium imaging allows recording the activity of large neural populations with subcellular spatial resolution, but it is typically characterized by low signal-to-noise ratio (SNR) and poor accuracy in detecting single or few action potentials when large number of neurons are imaged. We recently showed that implementing a smart line scanning approach using trajectories that optimally sample the regions of interest increases both the SNR fluorescence signals and the accuracy of single spike detection in population imaging in vivo. However, smart line scanning requires highly specialised software to design recording trajectories, interface with acquisition hardware, and efficiently process acquired data. Furthermore, smart line scanning needs optimized strategies to cope with movement artefacts and neuropil contamination. Here, we develop and validate SmaRT2P, an open-source, user-friendly and easy-to-interface Matlab-based software environment to perform optimized smart line scanning in two-photon calcium imaging experiments. SmaRT2P is designed to interface with popular acquisition software (e.g., ScanImage) and implements novel strategies to detect motion artefacts, estimate neuropil contamination, and minimize their impact on functional signals extracted from neuronal population imaging. SmaRT2P is structured in a modular way to allow flexibility in the processing pipeline, requiring minimal user intervention in parameter setting. The use of SmaRT2P for smart line scanning has the potential to facilitate the functional investigation of large neuronal populations with increased SNR and accuracy in detecting the discharge of single and few action potentials.

A gradual temporal shift of dopamine responses mirrors the progression of temporal difference error in machine learning

A large body of evidence has indicated that the phasic responses of midbrain dopamine neurons show a remarkable similarity to a type of teaching signal (temporal difference (TD) error) used in machine learning. However, previous studies failed to observe a key prediction of this algorithm: that when an agent associates a cue and a reward that are separated in time, the timing of dopamine signals should gradually move backward in time from the time of the reward to the time of the cue over multiple trials. Here we demonstrate that such a gradual shift occurs both at the level of dopaminergic cellular activity and dopamine release in the ventral striatum in mice. Our results establish a long-sought link between dopaminergic activity and the TD learning algorithm, providing fundamental insights into how the brain associates cues and rewards that are separated in time.

Neuromodulatory Mechanisms Underlying Contrast Gain Control in Mouse Auditory Cortex

Neural adaptation enables the brain to efficiently process sensory signals despite large changes in background noise. Previous studies have established that recent background spectro- or spatio-temporal statistics scale neural responses to sensory stimuli via a canonical normalization computation, which is conserved among species and sensory domains. In the auditory pathway, one major form of normalization, termed contrast gain control, presents as decreasing instantaneous firing-rate gain, the slope of the neural input-output relationship, with increasing variability of background sound levels (contrast) across time and frequency. Despite this gain rescaling, mean firing-rates in auditory cortex become invariant to sound level contrast, termed contrast invariance. The underlying neuromodulatory mechanisms of these two phenomena remain unknown. To study these mechanisms in male and female mice, we used a 2-photon calcium imaging preparation in layer 2/3 neurons of primary auditory cortex (A1), along with pharmacological and genetic KO approaches. We found that neuromodulatory cortical synaptic zinc signaling is necessary for contrast gain control but not contrast invariance in mouse A1.SIGNIFICANCE STATEMENT When sound levels in the acoustic environment become more variable across time and frequency, the brain decreases response gain to maintain dynamic range and thus stimulus discriminability. This gain adaptation accounts for changes in perceptual judgments in humans and mice; however, the underlying neuromodulatory mechanisms remain poorly understood. Here, we report context-dependent neuromodulatory effects of synaptic zinc that are necessary for contrast gain control in A1. Understanding context-specific neuromodulatory mechanisms, such as contrast gain control, provides insight into A1 cortical mechanisms of adaptation and also into fundamental aspects of perceptual changes that rely on gain modulation, such as attention.

Advantages, Pitfalls, and Developments of All Optical Interrogation Strategies of Microcircuits in vivo

The holy grail for every neurophysiologist is to conclude a causal relationship between an elementary behaviour and the function of a specific brain area or circuit. Our effort to map elementary behaviours to specific brain loci and to further manipulate neural activity while observing the alterations in behaviour is in essence the goal for neuroscientists. Recent advancements in the area of experimental brain imaging in the form of longer wavelength near infrared (NIR) pulsed lasers with the development of highly efficient optogenetic actuators and reporters of neural activity, has endowed us with unprecedented resolution in spatiotemporal precision both in imaging neural activity as well as manipulating it with multiphoton microscopy. This readily available toolbox has introduced a so called all-optical physiology and interrogation of circuits and has opened new horizons when it comes to precisely, fast and non-invasively map and manipulate anatomically, molecularly or functionally identified mesoscopic brain circuits. The purpose of this review is to describe the advantages and possible pitfalls of all-optical approaches in system neuroscience, where by all-optical we mean use of multiphoton microscopy to image the functional response of neuron(s) in the network so to attain flexible choice of the cells to be also optogenetically photostimulated by holography, in absence of electrophysiology. Spatio-temporal constraints will be compared toward the classical reference of electrophysiology methods. When appropriate, in relation to current limitations of current optical approaches, we will make reference to latest works aimed to overcome these limitations, in order to highlight the most recent developments. We will also provide examples of types of experiments uniquely approachable all-optically. Finally, although mechanically non-invasive, all-optical electrophysiology exhibits potential off-target effects which can ambiguate and complicate the interpretation of the results. In summary, this review is an effort to exemplify how an all-optical experiment can be designed, conducted and interpreted from the point of view of the integrative neurophysiologist.

Movement-specific signaling is differentially distributed across motor cortex layer 5 projection neuron classes

Motor cortex generates descending output necessary for executing a wide range of limb movements. Although movement-related activity has been described throughout motor cortex, the spatiotemporal organization of movement-specific signaling in deep layers remains largely unknown. Here we record layer 5B population dynamics in the caudal forelimb area of motor cortex while mice perform a forelimb push/pull task and find that most neurons show movement-invariant responses, with a minority displaying movement specificity. Using cell-type-specific imaging, we identify that invariant responses dominate pyramidal tract (PT) neuron activity, with a small subpopulation representing movement type, whereas a larger proportion of intratelencephalic (IT) neurons display movement-type-specific signaling. The proportion of IT neurons decoding movement-type peaks prior to movement initiation, whereas for PT neurons, this occurs during movement execution. Our data suggest that layer 5B population dynamics largely reflect movement-invariant signaling, with information related to movement-type being routed through relatively small, distributed subpopulations of projection neurons.

Detecting and correcting false transients in calcium imaging

Population recordings of calcium activity are a major source of insight into neural function. Large datasets require automated processing, but this can introduce errors that are difficult to detect. Here we show that popular time course-estimation algorithms often contain substantial misattribution errors affecting 10-20% of transients. Misattribution, in which fluorescence is ascribed to the wrong cell, arises when overlapping cells and processes are imperfectly defined or not identified. To diagnose misattribution, we develop metrics and visualization tools for evaluating large datasets. To correct time courses, we introduce a robust estimator that explicitly accounts for contaminating signals. In one hippocampal dataset, removing contamination reduced the number of place cells by 15%, and 19% of place fields shifted by over 10 cm. Our methods are compatible with other cell-finding techniques, empowering users to diagnose and correct a potentially widespread problem that could alter scientific conclusions.

A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging

In vivo two-photon calcium imaging is a powerful approach in neuroscience. However, processing two-photon calcium imaging data is computationally intensive and time-consuming, making online frame-by-frame analysis challenging. This is especially true for large field-of-view (FOV) imaging. Here, we present CITE-On (Cell Identification and Trace Extraction Online), a convolutional neural network-based algorithm for fast automatic cell identification, segmentation, identity tracking, and trace extraction in two-photon calcium imaging data. CITE-On processes thousands of cells online, including during mesoscopic two-photon imaging, and extracts functional measurements from most neurons in the FOV. Applied to publicly available datasets, the offline version of CITE-On achieves performance similar to that of state-of-the-art methods for offline analysis. Moreover, CITE-On generalizes across calcium indicators, brain regions, and acquisition parameters in anesthetized and awake head-fixed mice. CITE-On represents a powerful tool to speed up image analysis and facilitate closed-loop approaches, for example in combined all-optical imaging and manipulation experiments.

Processing of two-photon calcium imaging data is generally time-consuming, especially for large fields of view. Here, the authors present CITE-On, a tool based on a convolutional neural network, enabling online automatic cell identification, segmentation, identity tracking, and trace extraction.

Compartment-specific tuning of dendritic feature selectivity by intracellular Ca2+ release

Dendritic calcium signaling is central to neural plasticity mechanisms that allow animals to adapt to the environment. Intracellular calcium release (ICR) from the endoplasmic reticulum has long been thought to shape these mechanisms. However, ICR has not been investigated in mammalian neurons in vivo. We combined electroporation of single CA1 pyramidal neurons, simultaneous imaging of dendritic and somatic activity during spatial navigation, optogenetic place field induction, and acute genetic augmentation of ICR cytosolic impact to reveal that ICR supports the establishment of dendritic feature selectivity and shapes integrative properties determining output-level receptive fields. This role for ICR was more prominent in apical than in basal dendrites. Thus, ICR cooperates with circuit-level architecture in vivo to promote the emergence of behaviorally relevant plasticity in a compartment-specific manner.

Current approaches to characterize micro- and macroscale circuit mechanisms of Parkinson's disease in rodent models

Accelerating technological progress in experimental neuroscience is increasing the scale as well as specificity of both observational and perturbational approaches to study circuit physiology. While these techniques have also been used to study disease mechanisms, a wider adoption of these approaches in the field of experimental neurology would greatly facilitate our understanding of neurological dysfunctions and their potential treatments at cellular and circuit level. In this review, we will introduce classic and novel methods ranging from single-cell electrophysiological recordings to state-of-the-art calcium imaging and cell-type specific optogenetic or chemogenetic stimulation. We will focus on their application in rodent models of Parkinson’s disease while also presenting their use in the context of motor control and basal ganglia function. By highlighting the scope and limitations of each method, we will discuss how they can be used to study pathophysiological mechanisms at local and global circuit levels and how novel frameworks can help to bridge these scales.

Decontaminate Traces From Fluorescence Calcium Imaging Videos Using Targeted Non-negative Matrix Factorization

Fluorescence microscopy and genetically encoded calcium indicators help understand brain function by recording large-scale in vivo videos in assorted animal models. Extracting the fluorescent transients that represent active periods of individual neurons is a key step when analyzing imaging videos. Non-specific calcium sources and background adjacent to segmented neurons contaminate the neurons’ temporal traces with false transients. We developed and characterized a novel method, temporal unmixing of calcium traces (TUnCaT), to quickly and accurately unmix the calcium signals of neighboring neurons and background. Our algorithm used background subtraction to remove the false transients caused by background fluctuations, and then applied targeted non-negative matrix factorization to remove the false transients caused by neighboring calcium sources. TUnCaT was more accurate than existing algorithms when processing multiple experimental and simulated datasets. TUnCaT’s speed was faster than or comparable to existing algorithms.

Neocortex saves energy by reducing coding precision during food scarcity

Information processing is energetically expensive. In the mammalian brain, it is unclear how information coding and energy use are regulated during food scarcity. Using whole-cell recordings and two-photon imaging in layer 2/3 mouse visual cortex, we found that food restriction reduced AMPA receptor conductance, reducing synaptic ATP use by 29%. Neuronal excitability was nonetheless preserved by a compensatory increase in input resistance and a depolarized resting potential. Consequently, neurons spiked at similar rates as controls but spent less ATP on underlying excitatory currents. This energy-saving strategy had a cost because it amplified the variability of visually-evoked subthreshold responses, leading to a 32% broadening of orientation tuning and impaired fine visual discrimination. This reduction in coding precision was associated with reduced levels of the fat mass-regulated hormone leptin and was restored by exogenous leptin supplementation. Our findings reveal that metabolic state dynamically regulates the energy spent on coding precision in neocortex.

Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships

One of the main goals of current systems neuroscience is to understand how neuronal populations integrate sensory information to inform behavior. However, estimating stimulus or behavioral information that is encoded in high-dimensional neuronal populations is challenging. We propose a method based on parametric copulas which allows modeling joint distributions of neuronal and behavioral variables characterized by different statistics and timescales. To account for temporal or spatial changes in dependencies between variables, we model varying copula parameters by means of Gaussian Processes (GP). We validate the resulting Copula-GP framework on synthetic data and on neuronal and behavioral recordings obtained in awake mice. We show that the use of a parametric description of the high-dimensional dependence structure in our method provides better accuracy in mutual information estimation in higher dimensions compared to other non-parametric methods. Moreover, by quantifying the redundancy between neuronal and behavioral variables, our model exposed the location of the reward zone in an unsupervised manner (i.e., without using any explicit cues about the task structure). These results demonstrate that the Copula-GP framework is particularly useful for the analysis of complex multidimensional relationships between neuronal, sensory and behavioral variables.

Understanding the relationship between a set of variables is a common problem in many fields, such as weather forecast or stock market data. In neuroscience, one of the main challenges is to characterize the dependencies between neuronal activity, sensory stimuli and behavioral outputs. A method of choice for modeling such statistical dependencies is based on copulas, which disentangle dependencies from single variable statistics. To account for changes in dependencies, we model changes in copula parameters by means of Gaussian Processes, conditioned on a task-related variable. The novelty of our approach includes 1) explicit modeling of the dependencies; and 2) combining different copulas to describe experimentally observed variability. We validate the goodness-of-fit as well as information estimates on synthetic data and on recordings from the visual cortex of mice performing a behavioral task. Our parametric model demonstrates significantly better performance in describing high dimensional dependencies compared to other commonly used techniques. We demonstrate that our model can estimate information and predict behaviorally-relevant parameters of the task without providing any explicit cues to the model. Our results indicate that our model is interpretable in the context of neuroscience applications, scalable to large datasets and suitable for accurate statistical modeling and information estimation.

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.

Transcriptional and functional divergence in lateral hypothalamic glutamate neurons projecting to the lateral habenula and ventral tegmental area

The lateral hypothalamic area (LHA) regulates feeding- and reward-related behavior, but because of its molecular and anatomical heterogeneity, the functions of defined neuronal populations are largely unclear. Glutamatergic neurons within the LHA (LHA^Vglut2) negatively regulate feeding and appetitive behavior. However, this population comprises transcriptionally distinct and functionally diverse neurons that project to diverse brain regions, including the lateral habenula (LHb) and ventral tegmental area (VTA). To resolve the function of distinct LHA^Vglut2 populations, we systematically compared projections to the LHb and VTA using viral tracing, single-cell sequencing, electrophysiology, and in vivo calcium imaging. LHA^Vglut2 neurons projecting to the LHb or VTA are anatomically, transcriptionally, electrophysiologically, and functionally distinct. While both populations encode appetitive and aversive stimuli, LHb projecting neurons are especially sensitive to satiety state and feeding hormones. These data illuminate the functional heterogeneity of LHA^Vglut2 neurons, suggesting that reward and aversion are differentially processed in divergent efferent pathways.

Segmentation of Neurons from Fluorescence Calcium Recordings Beyond Real-time

Fluorescent genetically encoded calcium indicators and two-photon microscopy help understand brain function by generating large-scale in vivo recordings in multiple animal models. Automatic, fast, and accurate active neuron segmentation is critical when processing these videos. In this work, we developed and characterized a novel method, Shallow U-Net Neuron Segmentation (SUNS), to quickly and accurately segment active neurons from two-photon fluorescence imaging videos. We used temporal filtering and whitening schemes to extract temporal features associated with active neurons, and used a compact shallow U-Net to extract spatial features of neurons. Our method was both more accurate and an order of magnitude faster than state-of-the-art techniques when processing multiple datasets acquired by independent experimental groups; the difference in accuracy was enlarged when processing datasets containing few manually marked ground truths. We also developed an online version, potentially enabling real-time feedback neuroscience experiments.

A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging

Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation.

A cerebellar-thalamocortical pathway drives behavioral context-dependent movement initiation

Executing learned motor behaviors often requires the transformation of sensory cues into patterns of motor commands that generate appropriately timed actions. The cerebellum and thalamus are two key areas involved in shaping cortical output and movement, but the contribution of a cerebellar-thalamocortical pathway to voluntary movement initiation remains poorly understood. Here, we investigated how an auditory "go cue" transforms thalamocortical activity patterns and how these changes relate to movement initiation. Population responses in dentate/interpositus-recipient regions of motor thalamus reflect a time-locked increase in activity immediately prior to movement initiation that is temporally uncoupled from the go cue, indicative of a fixed-latency feedforward motor timing signal. Blocking cerebellar or motor thalamic output suppresses movement initiation, while stimulation triggers movements in a behavioral context-dependent manner. Our findings show how cerebellar output, via the thalamus, shapes cortical activity patterns necessary for learned context-dependent movement initiation.

The relationship between birth timing, circuit wiring, and physiological response properties of cerebellar granule cells

Cerebellar granule cells (GrCs) are usually regarded as a uniform cell type that collectively expands the coding space of the cerebellum by integrating diverse combinations of mossy fiber inputs. Accordingly, stable molecularly or physiologically defined GrC subtypes within a single cerebellar region have not been reported. The only known cellular property that distinguishes otherwise homogeneous GrCs is the correspondence between GrC birth timing and the depth of the molecular layer to which their axons project. To determine the role birth timing plays in GrC wiring and function, we developed genetic strategies to access early- and late-born GrCs. We initiated retrograde monosynaptic rabies virus tracing from control (birth timing unrestricted), early-born, and late-born GrCs, revealing the different patterns of mossy fiber input to GrCs in vermis lobule 6 and simplex, as well as to early- and late-born GrCs of vermis lobule 6: sensory and motor nuclei provide more input to early-born GrCs, while basal pontine and cerebellar nuclei provide more input to late-born GrCs. In vivo multidepth two-photon Ca2+ imaging of axons of early- and late-born GrCs revealed representations of diverse task variables and stimuli by both populations, with modest differences in the proportions encoding movement, reward anticipation, and reward consumption. Our results suggest neither organized parallel processing nor completely random organization of mossy fiber→GrC circuitry but instead a moderate influence of birth timing on GrC wiring and encoding. Our imaging data also provide evidence that GrCs can represent generalized responses to aversive stimuli, in addition to recently described reward representations.

Comparative Effects of Event Detection Methods on the Analysis and Interpretation of Ca2+ Imaging Data

Calcium imaging has gained substantial popularity as a tool to profile the activity of multiple simultaneously active cells at high spatiotemporal resolution. Among the diverse approaches to processing of Ca²⁺ imaging data is an often subjective decision of how to quantify baseline fluorescence or F ₀. We examine the effect of popular F ₀ determination methods on the interpretation of neuronal and astrocyte activity in a single dataset of rats trained to self-administer intravenous infusions of cocaine and compare them with an F ₀-independent wavelet ridgewalking event detection approach. We find that the choice of the processing method has a profound impact on the interpretation of widefield imaging results. All of the dF/F ₀ thresholding methods tended to introduce spurious events and fragment individual transients, leading to smaller calculated event durations and larger event frequencies. Analysis of simulated datasets confirmed these observations and indicated substantial intermethod variability as to the events classified as significant. Additionally, most dF/F ₀ methods on their own were unable to adequately account for bleaching of fluorescence, although the F ₀ smooth approach and the wavelet ridgewalking algorithm both did so. In general, the choice of the processing method led to dramatically different quantitative and sometimes opposing qualitative interpretations of the effects of cocaine self-administration both at the level of individual cells and at the level of cell networks. Significantly different distributions of event duration, amplitude, frequency, and network measures were found across the majority of dF/F ₀ approaches. The wavelet ridgewalking algorithm broadly outperformed dF/F ₀-based methods for both neuron and astrocyte recordings. These results indicate the need for heightened awareness of the limitations and tendencies associated with decisions to use particular Ca²⁺ image processing pipelines. Both quantification and interpretation of the effects of experimental manipulations are strongly sensitive to such decisions.

Enhanced modulation of cell-type specific neuronal responses in mouse dorsal auditory field during locomotion

As we move through the environment we experience constantly changing sensory input that must be merged with our ongoing motor behaviors - creating dynamic interactions between our sensory and motor systems. Active behaviors such as locomotion generally increase the sensory-evoked neuronal activity in visual and somatosensory cortices, but evidence suggests that locomotion largely suppresses neuronal responses in the auditory cortex. However, whether this effect is ubiquitous across different anatomical regions of the auditory cortex is largely unknown. In mice, auditory association fields such as the dorsal auditory cortex (AuD), have been shown to have different physiological response properties, protein expression patterns, and cortical as well as subcortical connections, in comparison to primary auditory regions (A1) - suggesting there may be important functional differences. Here we examined locomotion-related modulation of neuronal activity in cortical layers ⅔ of AuD and A1 using two-photon Ca²⁺ imaging in head-fixed behaving mice that are able to freely run on a spherical treadmill. We determined the proportion of neurons in these two auditory regions that show enhanced and suppressed sensory-evoked responses during locomotion and quantified the depth of modulation. We found that A1 shows more suppression and AuD more enhanced responses during locomotion periods. We further revealed differences in the circuitry between these auditory regions and motor cortex, and found that AuD is more highly connected to motor cortical regions. Finally, we compared the cell-type specific locomotion-evoked modulation of responses in AuD and found that, while subpopulations of PV-expressing interneurons showed heterogeneous responses, the population in general was largely suppressed during locomotion, while excitatory population responses were generally enhanced in AuD. Therefore, neurons in primary and dorsal auditory fields have distinct response properties, with dorsal regions exhibiting enhanced activity in response to movement. This functional distinction may be important for auditory processing during navigation and acoustically guided behavior.

Place Cells in Head-Fixed Mice Navigating a Floating Real-World Environment

The hippocampal place cell system in rodents has provided a major paradigm for the scientific investigation of memory function and dysfunction. Place cells have been observed in area CA1 of the hippocampus of both freely moving animals, and of head-fixed animals navigating in virtual reality environments. However, spatial coding in virtual reality preparations has been observed to be impaired. Here we show that the use of a real-world environment system for head-fixed mice, consisting of an air-floating track with proximal cues, provides some advantages over virtual reality systems for the study of spatial memory. We imaged the hippocampus of head-fixed mice injected with the genetically encoded calcium indicator GCaMP6s while they navigated circularly constrained or open environments on the floating platform. We observed consistent place tuning in a substantial fraction of cells despite the absence of distal visual cues. Place fields remapped when animals entered a different environment. When animals re-entered the same environment, place fields typically remapped over a time period of multiple days, faster than in freely moving preparations, but comparable with virtual reality. Spatial information rates were within the range observed in freely moving mice. Manifold analysis indicated that spatial information could be extracted from a low-dimensional subspace of the neural population dynamics. This is the first demonstration of place cells in head-fixed mice navigating on an air-lifted real-world platform, validating its use for the study of brain circuits involved in memory and affected by neurodegenerative disorders.

Large-Scale 3D Two-Photon Imaging of Molecularly Identified CA1 Interneuron Dynamics in Behaving Mice

Cortical computations are critically reliant on their local circuit, GABAergic cells. In the hippocampus, a large body of work has identified an unprecedented diversity of GABAergic interneurons with pronounced anatomical, molecular, and physiological differences. Yet little is known about the functional properties and activity dynamics of the major hippocampal interneuron classes in behaving animals. Here we use fast, targeted, three-dimensional (3D) two-photon calcium imaging coupled with immunohistochemistry-based molecular identification to retrospectively map in vivo activity onto multiple classes of interneurons in the mouse hippocampal area CA1 during head-fixed exploration and goal-directed learning. We find examples of preferential subtype recruitment with quantitative differences in response properties and feature selectivity during key behavioral tasks and states. These results provide new insights into the collective organization of local inhibitory circuits supporting navigational and mnemonic functions of the hippocampus.

METROID: an automated method for robust quantification of subcellular fluorescence events at low SNR

Background

In cell biology, increasing focus has been directed to fast events at subcellular space with the advent of fluorescent probes. As an example, voltage sensitive dyes (VSD) have been used to measure membrane potentials. Yet, even the most recently developed genetically encoded voltage sensors have demanded exhausting signal averaging through repeated experiments to quantify action potentials (AP). This analysis may be further hampered in subcellular signals defined by small regions of interest (ROI), where signal-to-noise ratio (SNR) may fall substantially. Signal processing techniques like blind source separation (BSS) are designed to separate a multichannel mixture of signals into uncorrelated or independent sources, whose potential to separate ROI signal from noise has been poorly explored. Our aims are to develop a method capable of retrieving subcellular events with minimal a priori information from noisy cell fluorescence images and to provide it as a computational tool to be readily employed by the scientific community.

Results

In this paper, we have developed METROID (Morphological Extraction of Transmembrane potential from Regions Of Interest Device), a new computational tool to filter fluorescence signals from multiple ROIs, whose code and graphical interface are freely available. In this tool, we developed a new ROI definition procedure to automatically generate similar-area ROIs that follow cell shape. In addition, simulations and real data analysis were performed to recover AP and electroporation signals contaminated by noise by means of four types of BSS: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and two versions with discrete wavelet transform (DWT). All these strategies allowed for signal extraction at low SNR (− 10 dB) without apparent signal distortion.

Conclusions

We demonstrate the great capability of our method to filter subcellular signals from noisy fluorescence images in a single trial, avoiding repeated experiments. We provide this novel biomedical application with a graphical user interface at 10.6084/m9.figshare.11344046.v1, and its code and datasets are available in GitHub at https://github.com/zoccoler/metroid.

Soma-Targeted Imaging of Neural Circuits by Ribosome Tethering

Neuroscience relies on techniques for imaging the structure and dynamics of neural circuits, but the cell bodies of individual neurons are often obscured by overlapping fluorescence from axons and dendrites in surrounding neuropil. Here, we describe two strategies for using the ribosome to restrict the expression of fluorescent proteins to the neuronal soma. We show first that a ribosome-tethered nanobody can be used to trap GFP in the cell body, thereby enabling direct visualization of previously undetectable GFP fluorescence. We then design a ribosome-tethered GCaMP for imaging calcium dynamics. We show that this reporter faithfully tracks somatic calcium dynamics in the mouse brain while eliminating cross-talk between neurons caused by contaminating neuropil. In worms, this reporter enables whole-brain imaging with faster kinetics and brighter fluorescence than commonly used nuclear GCaMPs. These two approaches provide a general way to enhance the specificity of imaging in neurobiology.

The gut-brain axis mediates sugar preference

The taste of sugar is one of the most basic sensory percepts for humans and other animals. Animals can develop a strong preference for sugar even if they lack sweet taste receptors, indicating a mechanism independent of taste^1-3. Here we examined the neural basis for sugar preference and demonstrate that a population of neurons in the vagal ganglia and brainstem are activated via the gut-brain axis to create preference for sugar. These neurons are stimulated in response to sugar but not artificial sweeteners, and are activated by direct delivery of sugar to the gut. Using functional imaging we monitored activity of the gut-brain axis, and identified the vagal neurons activated by intestinal delivery of glucose. Next, we engineered mice in which synaptic activity in this gut-to-brain circuit was genetically silenced, and prevented the development of behavioural preference for sugar. Moreover, we show that co-opting this circuit by chemogenetic activation can create preferences to otherwise less-preferred stimuli. Together, these findings reveal a gut-to-brain post-ingestive sugar-sensing pathway critical for the development of sugar preference. In addition, they explain the neural basis for differences in the behavioural effects of sweeteners versus sugar, and uncover an essential circuit underlying the highly appetitive effects of sugar.

Reward Association Enhances Stimulus-Specific Representations in Primary Visual Cortex

The potential for neuronal representations of external stimuli to be modified by previous experience is critical for efficient sensory processing and improved behavioral outcomes. To investigate how repeated exposure to a visual stimulus affects its representation in mouse primary visual cortex (V1), we performed two-photon calcium imaging of layer 2/3 neurons and assessed responses before, during, and after the presentation of a repetitive stimulus over 5 consecutive days. We found a stimulus-specific enhancement of the neuronal representation of the repetitively presented stimulus when it was associated with a reward. This was observed both after mice actively learned a rewarded task and when the reward was randomly received. Stimulus-specific enhanced representation resulted both from neurons gaining selectivity and from increased response reliability in previously selective neurons. In the absence of reward, there was either no change in stimulus representation or a decreased representation when the stimulus was viewed at a fixed temporal frequency. Pairing a second stimulus with a reward led to a similar enhanced representation and increased discriminability between the equally rewarded stimuli. Single-neuron responses showed that separate subpopulations discriminated between the two rewarded stimuli depending on whether the stimuli were displayed in a virtual environment or viewed on a single screen. We suggest that reward-associated responses enable the generalization of enhanced stimulus representation across these V1 subpopulations. We propose that this dynamic regulation of visual processing based on the behavioral relevance of sensory input ultimately enhances and stabilizes the representation of task-relevant features while suppressing responses to non-relevant stimuli.

The gut-brain axis mediates sugar preference

The taste of sugar is one of the most basic sensory percepts for humans and other animals. Animals can develop a strong preference for sugar even if they lack sweet taste receptors, indicating a mechanism independent of taste1-3. Here we examined the neural basis for sugar preference and demonstrate that a population of neurons in the vagal ganglia and brainstem are activated via the gut-brain axis to create preference for sugar. These neurons are stimulated in response to sugar but not artificial sweeteners, and are activated by direct delivery of sugar to the gut. Using functional imaging we monitored activity of the gut-brain axis, and identified the vagal neurons activated by intestinal delivery of glucose. Next, we engineered mice in which synaptic activity in this gut-to-brain circuit was genetically silenced, and prevented the development of behavioural preference for sugar. Moreover, we show that co-opting this circuit by chemogenetic activation can create preferences to otherwise less-preferred stimuli. Together, these findings reveal a gut-to-brain post-ingestive sugar-sensing pathway critical for the development of sugar preference. In addition, they explain the neural basis for differences in the behavioural effects of sweeteners versus sugar, and uncover an essential circuit underlying the highly appetitive effects of sugar.

Probing the coding logic of thermosensation using spinal cord calcium imaging

The spinal cord dorsal horn is the first relay station of the neural network for processing somatosensory information. High-throughput optical recording methods facilitate the study of sensory coding in the cortex but have not been successfully applied to study spinal cord circuitry until recently. Here, we review the development of an in vivo two-photon spinal calcium imaging preparation and biological findings from the first systematic characterization of the spinal response to cutaneous thermal stimuli, focusing on the difference between the coding of heat and cold, and the contribution of different peripheral inputs to thermosensory response in the spinal cord. Here we also report that knockout of TRPV1 channel impairs sensation of warmth, and somatostatin- and calbindin2-expressing neurons in the spinal dorsal horn preferentially respond to heat. Future work combining this technology with genetic tools and animal models of chronic pain will further elucidate the role of each neuronal type in the spinal thermosensory coding and their plasticity under pathological condition.

Benchmarking miniaturized microscopy against two-photon calcium imaging using single-cell orientation tuning in mouse visual cortex

Miniaturized microscopes are lightweight imaging devices that allow optical recordings from neurons in freely moving animals over the course of weeks. Despite their ubiquitous use, individual neuronal responses measured with these microscopes have not been directly compared to those obtained with established in vivo imaging techniques such as bench-top two-photon microscopes. To achieve this, we performed calcium imaging in mouse primary visual cortex while presenting animals with drifting gratings. We identified the same neurons in image stacks acquired with both microscopy methods and quantified orientation tuning of individual neurons. The response amplitude and signal-to-noise ratio of calcium transients recorded upon visual stimulation were highly correlated between both microscopy methods, although influenced by neuropil contamination in miniaturized microscopy. Tuning properties, calculated for individual orientation tuned neurons, were strongly correlated between imaging techniques. Thus, neuronal tuning features measured with a miniaturized microscope are quantitatively similar to those obtained with a two-photon microscope.

Vasoactive Intestinal Polypeptide-Expressing Interneurons in the Hippocampus Support Goal-Oriented Spatial Learning

Diverse computations in the neocortex are aided by specialized GABAergic interneurons (INs), which selectively target other INs. However, much less is known about how these canonical disinhibitory circuit motifs contribute to network operations supporting spatial navigation and learning in the hippocampus. Using chronic two-photon calcium imaging in mice performing random foraging or goal-oriented learning tasks, we found that vasoactive intestinal polypeptide-expressing (VIP+), disinhibitory INs in hippocampal area CA1 form functional subpopulations defined by their modulation by behavioral states and task demands. Optogenetic manipulations of VIP+ INs and computational modeling further showed that VIP+ disinhibition is necessary for goal-directed learning and related reorganization of hippocampal pyramidal cell population dynamics. Our results demonstrate that disinhibitory circuits in the hippocampus play an active role in supporting spatial learning. VIDEO ABSTRACT.

Analysis pipelines for calcium imaging data

Calcium imaging is a popular tool among neuroscientists because of its capability to monitor in vivo large neural populations across weeks with single neuron and single spike resolution. Before any downstream analysis, the data needs to be pre-processed to extract the location and activity of the neurons and processes in the observed field of view. The ever increasing size of calcium imaging datasets necessitates scalable analysis pipelines that are reproducible and fully automated. This review focuses on recent methods for addressing the pre-processing problems that arise in calcium imaging data analysis, and available software tools for high throughput analysis pipelines.

Leveraging calcium imaging to illuminate circuit dysfunction in addiction

Alcohol and drug use can dysregulate neural circuit function to produce a wide range of neuropsychiatric disorders, including addiction. To understand the neural circuit computations that mediate behavior, and how substances of abuse may transform them, we must first be able to observe the activity of circuits. While many techniques have been utilized to measure activity in specific brain regions, these regions are made up of heterogeneous sub-populations, and assessing activity from neuronal populations of interest has been an ongoing challenge. To fully understand how neural circuits mediate addiction-related behavior, we must be able to reveal the cellular granularity within brain regions and circuits by overlaying functional information with the genetic and anatomical identity of the cells involved. The development of genetically encoded calcium indicators, which can be targeted to populations of interest, allows for in vivo visualization of calcium dynamics, a proxy for neuronal activity, thus providing an avenue for real-time assessment of activity in genetically and anatomically defined populations during behavior. Here, we highlight recent advances in calcium imaging technology, compare the current technology with other state-of-the-art approaches for in vivo monitoring of neural activity, and discuss the strengths, limitations, and practical concerns for observing neural circuit activity in preclinical addiction models.

The Impact of Visual Cues, Reward, and Motor Feedback on the Representation of Behaviorally Relevant Spatial Locations in Primary Visual Cortex

The integration of visual stimuli and motor feedback is critical for successful visually guided navigation. These signals have been shown to shape neuronal activity in the primary visual cortex (V1), in an experience-dependent manner. Here, we examined whether visual, reward, and self-motion-related inputs are integrated in order to encode behaviorally relevant locations in V1 neurons. Using a behavioral task in a virtual environment, we monitored layer 2/3 neuronal activity as mice learned to locate a reward along a linear corridor. With learning, a subset of neurons became responsive to the expected reward location. Without a visual cue to the reward location, both behavioral and neuronal responses relied on self-motion-derived estimations. However, when visual cues were available, both neuronal and behavioral responses were driven by visual information. Therefore, a population of V1 neurons encode behaviorally relevant spatial locations, based on either visual cues or on self-motion feedback when visual cues are absent.

Optimization of interneuron function by direct coupling of cell migration and axonal targeting

Neural circuit assembly relies on the precise synchronization of developmental processes, such as cell migration and axon targeting, but the cell-autonomous mechanisms coordinating these events remain largely unknown. Here we found that different classes of interneurons use distinct routes of migration to reach the embryonic cerebral cortex. Somatostatin-expressing interneurons that migrate through the marginal zone develop into Martinotti cells, one of the most distinctive classes of cortical interneurons. For these cells, migration through the marginal zone is linked to the development of their characteristic layer 1 axonal arborization. Altering the normal migratory route of Martinotti cells by conditional deletion of Mafb-a gene that is preferentially expressed by these cells-cell-autonomously disrupts axonal development and impairs the function of these cells in vivo. Our results suggest that migration and axon targeting programs are coupled to optimize the assembly of inhibitory circuits in the cerebral cortex.

Action and learning shape the activity of neuronal circuits in the visual cortex

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Arousal and locomotion modulate neuronal activity in primary visual cortex.

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Neurons in primary visual cortex respond to visuomotor mismatch.

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Experience shapes neuronal responses to familiar stimuli, reward and object location.

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Neuronal representations of visual stimuli are modulated according to the behavioural relevance of the stimuli.

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Neuromodulatory, top-down and thalamocortical inputs convey arousal-related and motor-related signals to primary visual cortex.

Nonsensory variables strongly influence neuronal activity in the adult mouse primary visual cortex. Neuronal responses to visual stimuli are modulated by behavioural state, such as arousal and motor activity, and are shaped by experience. This dynamic process leads to neural representations in the visual cortex that reflect stimulus familiarity, expectations of reward and object location, and mismatch between self-motion and visual-flow. The recent development of genetic tools and recording techniques in awake behaving mice has enabled the investigation of the circuit mechanisms underlying state-dependent and experience-dependent neuronal representations in primary visual cortex. These neuronal circuits involve neuromodulatory, top-down cortico-cortical and thalamocortical pathways. The functions of nonsensory signals at this early stage of visual information processing are now beginning to be unravelled.