For many animals, the neural activity in early olfactory circuits during a single sniff cycle contains sufficient information for fine odor discrimination. Whilst much is known about the transformations of neural representations in early olfactory circuits, exactly how odorant evoked activity in the main olfactory bulb shapes the perception of odors remains largely unknown. In olfaction, odorant identity is generally conserved over a wide range of conditions, including concentration. We present a theory of identity assignment in the olfactory system that accounts for this invariance with respect to stimulus intensity. We suggest that the identities of relatively few high affinity olfactory receptor types determine an odorant's perceived identity. This set of high-affinity receptors is defined as the primary set and the coding model based on their responses is called the primacy theory. In this study, we explore the impact that primacy coding may have on the evolution of the ensemble of olfactory receptors. A primacy coding mechanism predicts the arrangement of different receptor types in a low-dimensional structure that we call a primacy hull. We present several statistical analyses that can detect the presence of this structure, allowing the predictions of the primacy model to be tested experimentally.
The structure of neuronal connectivity often provides insights into the relevant stimulus features, such as spatial location, orientation, sound frequency, etc1-6. The olfactory system, however, appears to lack structured connectivity as suggested by reports of broad and distributed connections both from the olfactory bulb to the piriform cortex7-22 and within the cortex23-25. These studies have inspired computational models of circuit function that rely on random connectivity26-33. It remains, nonetheless, unclear whether the olfactory connectivity contains spatial structure. Here, we use high throughput anatomical methods (MAPseq and BARseq)34-38 to analyze the projections of 5,309 bulb and 30,433 piriform cortex output neurons in the mouse at single-cell resolution. We identify previously unrecognized spatial organization in connectivity along the anterior-posterior axis (A-P) of the piriform cortex. We find that both the bulb projections to the cortex and the cortical outputs are not random, but rather form gradients along the A-P axis. Strikingly, these gradients are matched: bulb neurons targeting a given location within the piriform cortex co-innervate extra-piriform regions that receive strong inputs from neurons within that piriform locus. We also identify signatures of local connectivity in the piriform cortex. Our findings suggest an organizing principle of matched direct and indirect olfactory pathways that innervate extra-piriform targets in a coordinated manner, thus supporting models of information processing that rely on structured connectivity within the olfactory system.
Comparison of brain samples representing different developmental stages often necessitates registering the samples to common coordinates. Although the available software tools are successful in registering 3D images of adult brains, registration of perinatal brains remains challenging due to rapid growth-dependent morphological changes and variations in developmental pace between animals. To address these challenges, we propose a multi-step algorithm for the registration of perinatal brains. First, we optimized image preprocessing to increase the algorithm's sensitivity to mismatches in registered images. Second, we developed an attention-gated simulated annealing (Monte Carlo) procedure capable of focusing on the differences between perinatal brains. Third, we applied classical multidimensional scaling (CMDS) to align (synchronize) brain samples in time, accounting for individual development paces. We tested this multi-step algorithm on 28 samples of whole-mounted perinatal mouse brains (P0 - P9) and observed accurate registration results. Our computational pipeline offers a runtime of several minutes per brain on a personal computer and automates brain registration tasks including mapping brain data to atlases, comparison of averaged experimental groups, and monitoring brain development dynamics.
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