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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.

Odor landscapes contain complex blends of molecules that each activate unique, overlapping populations of olfactory sensory neurons (OSNs). Despite the presence of hundreds of OSN subtypes in many animals, the overlapping nature of odor inputs may lead to saturation of neural responses at the early stages of stimulus encoding. Information loss due to saturation could be mitigated by normalizing mechanisms such as antagonism at the level of receptor-ligand interactions, whose existence and prevalence remains uncertain. By imaging OSN axon terminals in olfactory bulb glomeruli as well as OSN cell bodies within the olfactory epithelium in freely breathing mice, we find widespread antagonistic interactions in binary odor mixtures. In complex mixtures of up to 12 odorants, antagonistic interactions are stronger and more prevalent with increasing mixture complexity. Therefore, antagonism is a common feature of odor mixture encoding in OSNs and helps in normalizing activity to reduce saturation and increase information transfer. Odor blends contain molecules that activate unique, overlapping populations of sensory neurons (OSNs). Here, by imaging OSN axon terminals, as well as their cell bodies within the olfactory epithelium, the authors find widespread antagonistic interactions in binary and complex odor mixtures.

Dogs and laboratory mice are commonly trained to perform complex tasks by guiding them through a curriculum of simpler tasks (‘shaping’). What are the principles behind effective shaping strategies? Here, we propose a teacher-student framework for shaping behavior, where an autonomous teacher agent decides its student’s task based on the student’s transcript of successes and failures on previously assigned tasks. Using algorithms for Monte Carlo planning under uncertainty, we show that near-optimal shaping algorithms achieve a careful balance between reinforcement and extinction. Near-optimal algorithms track learning rate to adaptively alternate between simpler and harder tasks. Based on this intuition, we derive an adaptive shaping heuristic with minimal parameters, which we show is near-optimal on a sequence learning task and robustly trains deep reinforcement learning agents on navigation tasks that involve sparse, delayed rewards. Extensions to continuous curricula are explored. Our work provides a starting point towards a general computational framework for shaping behavior that applies to both animals and artificial agents.

Being able to perceive an individual odor in a continually changing and rich olfactory environment is a challenging task. Here, Rokni and colleagues find that this olfactory cocktail party problem can be studied in mice and reveal that the ability to identify a specific odor in a complex odorous scene is constrained by the amount of overlap in the representations of target and background odors at the level of olfactory receptors. In odorant-rich environments, animals must be able to detect specific odorants of interest against variable backgrounds. However, studies have found that both humans and rodents are poor at analyzing the components of odorant mixtures, suggesting that olfaction is a synthetic sense in which mixtures are perceived holistically. We found that mice could be easily trained to detect target odorants embedded in unpredictable and variable mixtures. To relate the behavioral performance to neural representation, we imaged the responses of olfactory bulb glomeruli to individual odors in mice expressing the Ca 2+ indicator GCaMP3 in olfactory receptor neurons. The difficulty of segregating the target from the background depended strongly on the extent of overlap between the glomerular responses to target and background odors. Our study indicates that the olfactory system has powerful analytic abilities that are constrained by the limits of combinatorial neural representation of odorants at the level of the olfactory receptors.

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. How the feedforward information from the nose and feedback from the cortex interact in the olfactory bulb is not fully understood. Here, by imaging olfactory sensory neurons and cortical projections to the olfactory bulb, the authors show that sensory transformations contained within both streams.

The serotonergic raphe nuclei modulate neuronal function typically over minutes or hours. The authors report that raphe nuclei affect odor responses in output neurons of the olfactory bulb at sub-second time scales. These effects are mediated through multiple neurotransmitters and are distinct depending on the type of output neuron. The serotonergic raphe nuclei are involved in regulating brain states over timescales of minutes and hours. We examined more rapid effects of raphe activation on two classes of principal neurons in the mouse olfactory bulb, mitral and tufted cells, which send olfactory information to distinct targets. Brief stimulation of the raphe nuclei led to excitation of tufted cells at rest and potentiation of their odor responses. While mitral cells at rest were also excited by raphe activation, their odor responses were bidirectionally modulated, leading to improved pattern separation of odors. In vitro whole-cell recordings revealed that specific optogenetic activation of raphe axons affected bulbar neurons through dual release of serotonin and glutamate. Therefore, the raphe nuclei, in addition to their role in neuromodulation of brain states, are also involved in fast, sub-second top-down modulation similar to cortical feedback. This modulation can selectively and differentially sensitize or decorrelate distinct output channels.

Natural environments feature mixtures of odorants of diverse quantities, qualities and complexities. Olfactory receptor neurons (ORNs) are the first layer in the sensory pathway and transmit the olfactory signal to higher regions of the brain. Yet, the response of ORNs to mixtures is strongly non-additive, and exhibits antagonistic interactions among odorants. Here, we model the processing of mixtures by mammalian ORNs, focusing on the role of inhibitory mechanisms. We show how antagonism leads to an effective ‘normalization’ of the ensemble ORN response, that is, the distribution of responses of the ORN population induced by any mixture is largely independent of the number of components in the mixture. This property arises from a novel mechanism involving the distinct statistical properties of receptor binding and activation, without any recurrent neuronal circuitry. Normalization allows our encoding model to outperform non-interacting models in odor discrimination tasks, leads to experimentally testable predictions and explains several psychophysical experiments in humans. When ordering in a coffee shop, you probably recognize and enjoy the aroma of freshly roasted coffee beans. But as well as coffee, you can also smell the croissants behind the counter and maybe even the perfume or cologne of the person next to you. Each of these scents consists of a collection of chemicals, or odorants. To distinguish between the aroma of coffee and that of croissants, your brain must group the odorants appropriately and then keep the groups separate from each other. This is not a trivial task. Odorants bind to proteins called odorant receptors found on the surface of cells in the nose called olfactory receptor neurons. But each odorant does not have its own dedicated receptor. Instead, a single odorant will bind to multiple types of odorant receptors, and thus, each olfactory receptor neuron may respond to multiple odorants. So how does the brain encode mixtures of odorants in a way that allows us to distinguish one aroma from another? Reddy, Zak et al. have developed a computational model to explain how this process works. The model assumes that an odorant triggers a response in an olfactory receptor neuron via two steps. First, the odorant binds to an odorant receptor. Second, the bound odorant activates the receptor. But the odorant that binds most strongly to a receptor will not necessarily be the odorant that is best at activating that receptor. This allows a phenomenon called competitive antagonism to occur. This is when one odorant in a mixture binds more strongly to a receptor than the other odorants, but only weakly activates that receptor. In so doing, the strongly bound odorant prevents the other odorants from binding to and activating the receptor. This helps tame the dominating influence of background odors, which might otherwise saturate the responses of individual olfactory receptor neurons. Reddy, Zak et al. show that processes such as competitive antagonism enable olfactory receptor neurons to encode all of the odors within a mixture. The model can explain various phenomena observed in experiments and it adds to our understanding of how the brain generates our sense of smell. The model may also be relevant to other biological systems that must filter weak signals from a dominant background. These include the immune system, which must distinguish a small set of foreign proteins from the much larger number of proteins that make up our bodies.

A combination of a sensitive blue light–gated channelrhodopsin actuator and red-shifted Arch-based voltage sensors allows all-optical electrophysiology without cross-talk in cultured neurons or brain slices. 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.

Making sense of vomeronasal signals Humans have retained only one functional chemosensing organ, the nose, but many terrestrial vertebrates also rely on a secondary system, the vomeronasal organ. Its primary role is to mediate the social and defensive responses to chemical signals emitted from either conspecifics (pheromones) or animals from other species (kairomones). Catherine Dulac and colleagues have now identified a series of chemoreceptor proteins that respond specifically to subsets of chemical cues produced by potential sexual partners or aggressors, and possible prey or predators. The 'de-orphaning' of close to a hundred vomeronasal receptors paves the way for further dissection of the neural circuits that control innate behaviours in response to socially relevant chemical signals. 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 1 . More than 250 putative pheromone receptors have been identified in the mouse VNO 2 , 3 , 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.

Odors evoke complex spatiotemporal patterns of activity in the olfactory bulb. The authors show that the spike rates of downstream piriform cortex neurons (PCNs) reflect the relative timing of activation. Posterior PCNs are more sensitive to input timing than anterior PCNs. Odor stimulation evokes complex spatiotemporal activity in the olfactory bulb, suggesting that both the identity of activated neurons and the timing of their activity convey information about odors. However, whether and how downstream neurons decipher these temporal patterns remains unknown. We addressed this question by measuring the spiking activity of downstream neurons while optogenetically stimulating two foci in the olfactory bulb with varying relative timing in mice. We found that the overall spike rates of piriform cortex neurons (PCNs) were sensitive to the relative timing of activation. Posterior PCNs showed higher sensitivity to relative input times than neurons in the anterior piriform cortex. In contrast, olfactory bulb neurons rarely showed such sensitivity. Thus, the brain can transform a relative time code in the periphery into a firing rate–based representation in central brain areas, providing evidence for the relevance of a relative time–based code in the olfactory bulb.