Paradoxical relationship between speed and accuracy in olfactory figure-background segregation

Adaptive olfactory circuitry restores function despite severe olfactory bulb degeneration

The olfactory bulb (OB) is a critical component of mammalian olfactory neuroanatomy. Beyond being the first and sole relay station for olfactory information to the rest of the brain, it also contains elaborate stereotypical circuitry that is considered essential for olfaction. Indeed, substantial lesions of the OB in rodents lead to anosmia. Here, we examined the circuitry that underlies olfaction in a mouse model with severe developmental degeneration of the OB. These mice could perform odor-guided tasks and even responded normally to innate olfactory cues. Despite the near total loss of the OB, piriform cortices in these mice responded to odors, and its neural activity sufficed to decode odor identity. We found that sensory neurons express the full repertoire of olfactory receptors, and their axons project primarily to the rudiments of the OB but also, ectopically, to olfactory cortical regions. Within the OB, the number of principal neurons was greatly reduced, and the morphology of their dendrites was abnormal, extending over large regions within the OB. Glomerular organization was totally lost in the severe cases of OB degeneration and altered in the more conserved OBs. This study shows that olfactory functionality can be preserved despite reduced and aberrant circuitry that is missing many of the elements believed to be essential for olfaction, and it may explain reported retention of olfaction in humans with degenerated OBs.

Haploinsufficiency of Shank3 in Mice Selectively Impairs Target Odor Recognition in Novel Background Odors

Individuals with mutations in a single copy of the SHANK3 gene present with social interaction deficits. Although social behavior in mice depends on olfaction, mice with mutations in a single copy of the Shank3 gene do not have olfactory deficits in simple odor identification tasks (Drapeau et al., 2018). Here, we tested olfaction in mice with mutations in a single copy of the Shank3 gene (Peça et al., 2011) using a complex odor task and imaging in awake mice. Average glomerular responses in the olfactory bulb of Shank3B +/- were correlated with WT mice. However, there was increased trial-to-trial variability in the odor responses for Shank3B +/- mice. Simulations demonstrated that this increased variability could affect odor detection in novel environments. To test whether performance was affected by the increased variability, we tested target odor recognition in the presence of novel background odors using a recently developed task (Li et al., 2023). Head-fixed mice were trained to detect target odors in the presence of known background odors. Performance was tested using catch trials where the known background odors were replaced by novel background odors. We compared the performance of eight Shank3B +/- mice (five males, three females) on this task with six WT mice (three males, three females). Performance for known background odors and learning rates were similar between Shank3B +/- and WT mice. However, when tested with novel background odors, the performance of Shank3B +/- mice dropped to almost chance levels. Thus, haploinsufficiency of the Shank3 gene causes a specific deficit in odor detection in novel environments. Our results are discussed in the context of other Shank3 mouse models and have implications for understanding olfactory function in neurodevelopmental disorders.SIGNIFICANCE STATEMENT People and mice with mutations in a single copy in the synaptic gene Shank3 show features seen in autism spectrum disorders, including social interaction deficits. Although mice social behavior uses olfaction, mice with mutations in a single copy of Shank3 have so far not shown olfactory deficits when tested using simple tasks. Here, we used a recently developed task to show that these mice could identify odors in the presence of known background odors as well as wild-type mice. However, their performance fell below that of wild-type mice when challenged with novel background odors. This deficit was also previously reported in the Cntnap2 mouse model of autism, suggesting that odor detection in novel backgrounds is a general deficit across mouse models of autism.

Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb

Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb. This model maps onto the neuron classes of the olfactory bulb, and recapitulates salient features of their connectivity and physiology. For circuit sizes comparable to the human olfactory bulb, we show that this model can accurately detect tens of odors within the timescale of a single sniff. We also show that this model can perform Bayesian posterior sampling for accurate uncertainty estimation. Fast inference is possible only if the geometry of the neural code is chosen to match receptor properties, yielding a distributed neural code that is not axis-aligned to individual odor identities. Our results illustrate how normative modeling can help us map function onto specific neural circuits to generate new hypotheses.

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.

Robust odor identification in novel olfactory environments in mice

Relevant odors signaling food, mates, or predators can be masked by unpredictable mixtures of less relevant background odors. Here, we developed a mouse behavioral paradigm to test the role played by the novelty of the background odors. During the task, mice identified target odors in previously learned background odors and were challenged by catch trials with novel background odors, a task similar to visual CAPTCHA. Female wild-type (WT) mice could accurately identify known targets in novel background odors. WT mice performance was higher than linear classifiers and the nearest neighbor classifier trained using olfactory bulb glomerular activation patterns. Performance was more consistent with an odor deconvolution method. We also used our task to investigate the performance of female Cntnap2^-/- mice, which show some autism-like behaviors. Cntnap2^-/- mice had glomerular activation patterns similar to WT mice and matched WT mice target detection for known background odors. However, Cntnap2^-/- mice performance fell almost to chance levels in the presence of novel backgrounds. Our findings suggest that mice use a robust algorithm for detecting odors in novel environments and this computation is impaired in Cntnap2^-/- mice.

Response Time Distributions and the Accumulation of Visual Evidence in Freely Moving Mice

Response time (RT) distributions are histograms of the observed RTs for discriminative choices, comprising a rich source of empirical information to study perceptual processes. The drift-diffusion model (DDM), a mathematical formulation predicting decision tasks, reproduces the RT distributions, contributing to our understanding of these processes from a theoretical perspective. Notably, although the mouse is a popular model system for studying brain function and behavior, little is known about mouse perceptual RT distributions, and their description from an information-accumulation perspective. We combined an automated visual discrimination task with pharmacological micro-infusions of targeted brain regions to acquire thousands of responses from freely-moving adult mice. Both choices and escape latencies showed a strong dependency on stimulus discriminability. By applying a DDM fit to our experimental data, we found that the rate of incoming evidence (drift rate) increased with stimulus contrast but was reversibly impaired when inactivating the primary visual cortex (V1). Other brain regions involved in the decision-making process, the posterior parietal cortex (PPC) and the frontal orienting fields (FOF), also influenced relevant parameters from the DDM. The large number of empirical observations that we collected for this study allowed us to achieve accurate convergence for the model fit. Therefore, changes in the experimental conditions were mirrored by changes in model parameters, suggesting the participation of relevant brain areas in the decision-making process. This approach could help interpret future studies involving attention, discrimination, and learning in adult mice.