Neuronal Representation of Social Information in the Medial Amygdala of Awake Behaving Mice.

The medial amygdala (MeA) plays a critical role in processing species- and sex-specific signals that trigger social and defensive behaviors. However, the principles by which this deep brain structure encodes social information is poorly understood. We used a miniature microscope to image the Ca2+ dynamics of large neural ensembles in awake behaving mice and tracked the responses of MeA neurons over several months. These recordings revealed spatially intermingled subsets of MeA neurons with distinct temporal dynamics. The encoding of social information in the MeA differed between males and females and relied on information from both individual cells and neuronal populations. By performing long-term Ca2+ imaging across different social contexts, we found that sexual experience triggers lasting and sex-specific changes in MeA activity, which, in males, involve signaling by oxytocin. These findings reveal basic principles underlying the brain's representation of social information and its modulation by intrinsic and extrinsic factors.

Experience-dependent evolution of odor mixture representations in piriform cortex.

Rodents can learn from exposure to rewarding odors to make better and quicker decisions. The piriform cortex is thought to be important for learning complex odor associations; however, it is not understood exactly how it learns to remember discriminations between many, sometimes overlapping, odor mixtures. We investigated how odor mixtures are represented in the posterior piriform cortex (pPC) of mice while they learn to discriminate a unique target odor mixture against hundreds of nontarget mixtures. We find that a significant proportion of pPC neurons discriminate between the target and all other nontarget odor mixtures. Neurons that prefer the target odor mixture tend to respond with brief increases in firing rate at odor onset compared to other neurons, which exhibit sustained and/or decreased firing. We allowed mice to continue training after they had reached high levels of performance and find that pPC neurons become more selective for target odor mixtures as well as for randomly chosen repeated nontarget odor mixtures that mice did not have to discriminate from other nontargets. These single unit changes during overtraining are accompanied by better categorization decoding at the population level, even though behavioral metrics of mice such as reward rate and latency to respond do not change. However, when difficult ambiguous trial types are introduced, the robustness of the target selectivity is correlated with better performance on the difficult trials. Taken together, these data reveal pPC as a dynamic and robust system that can optimize for both current and possible future task demands at once.

Nonequilibrium sensing of volatile compounds using active and passive analyte delivery.

Although sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch, the development of artificial noses is significantly behind their biological counterparts. This largely stems from the sophistication of natural olfaction, which relies on both fluid dynamics within the nasal anatomy and the response patterns of hundreds to thousands of unique molecular-scale receptors. We designed a sensing approach to identify volatiles inspired by the fluid dynamics of the nose, allowing us to extract information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) rather than relying on a large sensor array. By accentuating differences in the nonequilibrium mass-transport dynamics of vapors and training a machine learning algorithm on the sensor output, we clearly identified polar and nonpolar volatile compounds, determined the mixing ratios of binary mixtures, and accurately predicted the boiling point, flash point, vapor pressure, and viscosity of a number of volatile liquids, including several that had not been used for training the model. We further implemented a bioinspired active sniffing approach, in which the analyte delivery was performed in well-controlled 'inhale-exhale' sequences, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. Our results outline a strategy to build accurate and rapid artificial noses for volatile compounds that can provide useful information such as the composition and physical properties of chemicals, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.

Multi-animal pose estimation, identification and tracking with DeepLabCut.

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking-features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal's identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.

Deletion of TrkB in parvalbumin interneurons alters cortical neural dynamics.

Signaling by neurotrophins such as the brain-derived neurotrophic factor (BDNF) is known to modulate development of interneurons, but the circuit effects of this modulation remain unclear. Here, we examined the impact of deleting TrkB, a BDNF receptor, in parvalbumin-expressing (PV) interneurons on the balance of excitation and inhibition (E-I) in cortical circuits. In the mouse olfactory cortex, TrkB deletion impairs multiple aspects of PV neuronal function including synaptic excitation, intrinsic excitability, and the innervation pattern of principal neurons. Impaired PV cell function resulted in aberrant spiking patterns in principal neurons in response to stimulation of sensory inputs. Surprisingly, dampened PV neuronal function leads to a paradoxical decrease in overall excitability in cortical circuits. Our study demonstrates that, by modulating PV circuit plasticity and development, TrkB plays a critical role in shaping the evoked pattern of activity in a cortical network.

Rapid Learning of Odor-Value Association in the Olfactory Striatum.

Rodents can successfully learn multiple novel stimulus-response associations after only a few repetitions when the contingencies predict reward. The circuits modified during such reinforcement learning to support decision-making are not known, but the olfactory tubercle (OT) and posterior piriform cortex (pPC) are candidates for decoding reward category from olfactory sensory input and relaying this information to cognitive and motor areas. Through single-cell recordings in behaving male and female C57BL/6 mice, we show here that an explicit representation for reward category emerges in the OT within minutes of learning a novel odor-reward association, whereas the pPC lacks an explicit representation even after weeks of overtraining. The explicit reward category representation in OT is visible in the first sniff (50-100 ms) of an odor on each trial, and precedes the motor action. Together, these results suggest that the coding of stimulus information required for reward prediction does not occur within olfactory cortex, but rather in circuits involving the olfactory striatum.SIGNIFICANCE STATEMENT Rodents are olfactory specialists and can use odors to learn contingencies quickly and well. We have found that mice can readily learn to place multiple odors into rewarded and unrewarded categories. Once they have learned the rule, they can do such categorization in a matter of minutes (<10 trials). We found that neural activity in olfactory cortex largely reflects sensory coding, with very little explicit information about categories. By contrast, neural activity in a brain region in the ventral striatum is rapidly modified in a matter of minutes to reflect reward category. Our experiments set up a paradigm for studying rapid sensorimotor reinforcement in a circuit that is right at the interface of sensory input and reward areas.

Olfactory maps in the brain.

The responses of neural elements in many sensory areas of the brain vary systematically with their physical position, leading to a topographic representation of the outside world. Sensory representation in the olfactory system has been harder to decipher, in part because it is difficult to find appropriate metrics to characterize odor space and to sample this space densely. Recent experiments have shown that the arrangement of glomeruli, the elementary units of processing, is relatively invariant across individuals in a species, yet it is flexible enough to accommodate new sensors that might be added. Evidence supports the existence of coarse spatial domains carved out on a genetic or functional basis, but a systematic organization of odor responses or neural circuits on a local scale is not evident. Experiments and theory that relate the properties of odorant receptors to the detailed wiring diagram of the downstream olfactory circuits and to behaviors they trigger may reveal the design principles that have emerged during evolution.

How to monitor breathing in laboratory rodents: a review of the current methods.

Accurately measuring respiration in laboratory rodents is essential for many fields of research, including olfactory neuroscience, social behavior, learning and memory, and respiratory physiology. However, choosing the right technique to monitor respiration can be tricky, given the many criteria to take into account: reliability, precision, and invasiveness, to name a few. This review aims to assist experimenters in choosing the technique that will best fit their needs, by surveying the available tools, discussing their strengths and weaknesses, and offering suggestions for future improvements.

Carpenter ants use diverse antennae sampling strategies to track odor trails.

Directed and meaningful animal behavior depends on the ability to sense key features in the environment. Among the different environmental signals, olfactory cues are critically important for foraging, navigation and social communication in many species, including ants. Ants use their two antennae to explore the olfactory world, but how they do so remains largely unknown. In this study, we used high-resolution videography to characterize the antennae dynamics of carpenter ants (Camponotus pennsylvanicus). Antennae are highly active during both odor tracking and exploratory behavior. When tracking, ants used several distinct behavioral strategies with stereotyped antennae sampling patterns (which we call 'sinusoidal', 'probing' and 'trail following'). In all behaviors, left and right antennae movements were anti-correlated, and tracking ants exhibited biases in the use of left versus right antenna to sample the odor trail. These results suggest non-redundant roles for the two antennae. In one of the behavioral modules (trail following), ants used both antennae to detect trail edges and direct subsequent turns, suggesting a specialized form of tropotaxis. Lastly, removal of an antenna resulted not only in less accurate tracking but also in changes in the sampling pattern of the remaining antenna. Our quantitative characterization of odor trail tracking lays a foundation to build better models of olfactory sensory processing and sensorimotor behavior in terrestrial insects.

All-optical electrophysiology in mammalian neurons using engineered microbial rhodopsins.

All-optical electrophysiology-spatially resolved simultaneous optical perturbation and measurement of membrane voltage-would open new vistas in neuroscience research. We evolved two archaerhodopsin-based voltage indicators, QuasAr1 and QuasAr2, which show improved brightness and voltage sensitivity, have microsecond response times and produce no photocurrent. We engineered a channelrhodopsin actuator, CheRiff, which shows high light sensitivity and rapid kinetics and is spectrally orthogonal to the QuasArs. A coexpression vector, Optopatch, enabled cross-talk-free genetically targeted all-optical electrophysiology. In cultured rat neurons, we combined Optopatch with patterned optical excitation to probe back-propagating action potentials (APs) in dendritic spines, synaptic transmission, subcellular microsecond-timescale details of AP propagation, and simultaneous firing of many neurons in a network. Optopatch measurements revealed homeostatic tuning of intrinsic excitability in human stem cell-derived neurons. In rat brain slices, Optopatch induced and reported APs and subthreshold events with high signal-to-noise ratios. The Optopatch platform enables high-throughput, spatially resolved electrophysiology without the use of conventional electrodes.

Molecular organization of vomeronasal chemoreception.

The vomeronasal organ (VNO) has a key role in mediating the social and defensive responses of many terrestrial vertebrates to species- and sex-specific chemosignals. More than 250 putative pheromone receptors have been identified in the mouse VNO, but the nature of the signals detected by individual VNO receptors has not yet been elucidated. To gain insight into the molecular logic of VNO detection leading to mating, aggression or defensive responses, we sought to uncover the response profiles of individual vomeronasal receptors to a wide range of animal cues. Here we describe the repertoire of behaviourally and physiologically relevant stimuli detected by a large number of individual vomeronasal receptors in mice, and define a global map of vomeronasal signal detection. We demonstrate that the two classes (V1R and V2R) of vomeronasal receptors use fundamentally different strategies to encode chemosensory information, and that distinct receptor subfamilies have evolved towards the specific recognition of certain animal groups or chemical structures. The association of large subsets of vomeronasal receptors with cognate, ethologically and physiologically relevant stimuli establishes the molecular foundation of vomeronasal information coding, and opens new avenues for further investigating the neural mechanisms underlying behaviour specificity.

Circuit formation and function in the olfactory bulb of mice with reduced spontaneous afferent activity.

The type of neuronal activity required for circuit development is a matter of significant debate. We addressed this issue by analyzing the topographic organization of the olfactory bulb in transgenic mice engineered to have very little afferent spontaneous activity due to the overexpression of the inwardly rectifying potassium channel Kir2.1 in the olfactory sensory neurons (Kir2.1 mice). In these conditions, the topography of the olfactory bulb was unrefined. Odor-evoked responses were readily recorded in glomeruli with reduced spontaneous afferent activity, although the functional maps were coarser than in controls and contributed to altered olfactory discrimination behavior. In addition, overexpression of Kir2.1 in adults induced a regression of the already refined connectivity to an immature (i.e., coarser) status. Our data suggest that spontaneous activity plays a critical role not only in the development but also in the maintenance of the topography of the olfactory bulb and in sensory information processing.

Olfactory navigation in fluctuating environments.

We humans move around in the world, guided largely by light and sound. Many cohabitants of our planet, however, predominantly use their chemical senses to navigate a rich landscape. Light and sound propagate with predictable geometric precision, and animals in particular have evolved ways to exploit these physical principles. Odors, on the other hand, are at the mercy of the carrier medium, air or water, for long distance transport, which can quickly become turbulent and unpredictable. Nevertheless, animals have found ways to navigate these fickle features to chase mates, find food or return home. Understanding the physics of odor transport can help rationalize the strategies animals use for navigation and guide studies of how the corresponding algorithms are implemented by their brains and bodies.

Distinct information conveyed to the olfactory bulb by feedforward input from the nose and feedback from the cortex.

Sensory systems are organized hierarchically, but feedback projections frequently disrupt this order. In the olfactory bulb (OB), cortical feedback projections numerically match sensory inputs. To unravel information carried by these two streams, we imaged the activity of olfactory sensory neurons (OSNs) and cortical axons in the mouse OB using calcium indicators, multiphoton microscopy, and diverse olfactory stimuli. Here, we show that odorant mixtures of increasing complexity evoke progressively denser OSN activity, yet cortical feedback activity is of similar sparsity for all stimuli. Also, representations of complex mixtures are similar in OSNs but are decorrelated in cortical axons. While OSN responses to increasing odorant concentrations exhibit a sigmoidal relationship, cortical axonal responses are complex and nonmonotonic, which can be explained by a model with activity-dependent feedback inhibition in the cortex. Our study indicates that early-stage olfactory circuits have access to local feedforward signals and global, efficiently formatted information about odor scenes through cortical feedback.

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.

The role of prospective contingency in the control of behavior and dopamine signals during associative learning.

Associative learning depends on contingency, the degree to which a stimulus predicts an outcome. Despite its importance, the neural mechanisms linking contingency to behavior remain elusive. Here we examined the dopamine activity in the ventral striatum - a signal implicated in associative learning - in a Pavlovian contingency degradation task in mice. We show that both anticipatory licking and dopamine responses to a conditioned stimulus decreased when additional rewards were delivered uncued, but remained unchanged if additional rewards were cued. These results conflict with contingency-based accounts using a traditional definition of contingency or a novel causal learning model (ANCCR), but can be explained by temporal difference (TD) learning models equipped with an appropriate inter-trial-interval (ITI) state representation. Recurrent neural networks trained within a TD framework develop state representations like our best 'handcrafted' model. Our findings suggest that the TD error can be a measure that describes both contingency and dopaminergic activity.

Self-supervised learning for interventional image analytics: toward robust device trackers.

Purpose: The accurate detection and tracking of devices, such as guiding catheters in live X-ray image acquisitions, are essential prerequisites for endovascular cardiac interventions. This information is leveraged for procedural guidance, e.g., directing stent placements. To ensure procedural safety and efficacy, there is a need for high robustness/no failures during tracking. To achieve this, one needs to efficiently tackle challenges, such as device obscuration by the contrast agent or other external devices or wires and changes in the field-of-view or acquisition angle, as well as the continuous movement due to cardiac and respiratory motion. Approach: To overcome the aforementioned challenges, we propose an approach to learn spatio-temporal features from a very large data cohort of over 16 million interventional X-ray frames using self-supervision for image sequence data. Our approach is based on a masked image modeling technique that leverages frame interpolation-based reconstruction to learn fine inter-frame temporal correspondences. The features encoded in the resulting model are fine-tuned downstream in a light-weight model. Results: Our approach achieves state-of-the-art performance, in particular for robustness, compared to ultra optimized reference solutions (that use multi-stage feature fusion or multi-task and flow regularization). The experiments show that our method achieves a 66.31% reduction in the maximum tracking error against the reference solutions (23.20% when flow regularization is used), achieving a success score of 97.95% at a 3× faster inference speed of 42 frames-per-second (on GPU). In addition, we achieve a 20% reduction in the standard deviation of errors, which indicates a much more stable tracking performance. Conclusions: The proposed data-driven approach achieves superior performance, particularly in robustness and speed compared with the frequently used multi-modular approaches for device tracking. The results encourage the use of our approach in various other tasks within interventional image analytics that require effective understanding of spatio-temporal semantics.

Activity-dependent regulation of inhibition via GAD67.

Persistent alterations in network activity trigger compensatory changes in excitation and inhibition that restore neuronal firing rate to an optimal range. One example of such synaptic homeostasis is the downregulation of inhibitory transmission by chronic inactivity, in part through the reduction of vesicular transmitter content. The enzyme glutamic acid decarboxylase 67 (GAD67) is critical for GABA synthesis, but its involvement in homeostatic plasticity is unclear. We explored the role of GAD67 in activity-dependent synaptic plasticity using a mouse line (Gad1(-/-)) in which GAD67 expression is disrupted by genomic insertion of the green fluorescent protein (GFP). Homozygous deletion of Gad1 significantly reduced miniature inhibitory postsynaptic current (mIPSC) amplitudes and GABA levels in cultured hippocampal neurons. The fractional block of mIPSC amplitude by a low affinity, competitive GABA(A) receptor antagonist was higher in GAD67-lacking neurons, suggesting that GABA concentration in the synaptic cleft is lower in knockout animals. Chronic suppression of activity by the application of tetrodotoxin (TTX) reduced mIPSC amplitudes and the levels of GAD67 and GABA. Moreover, TTX reduced GFP levels in interneurons, suggesting that GAD67 gene expression is a key regulatory target of activity. These in vitro experiments were corroborated by in vivo studies in which olfactory deprivation reduced mIPSC amplitudes and GFP levels in glomerular neurons in the olfactory bulb. Importantly, TTX-induced downregulation of mIPSC was attenuated in Gad1(-/-) neurons. Altogether, these findings indicate that activity-driven expression of GAD67 critically controls GABA synthesis and, thus, vesicular filling of the transmitter.