Temporal Response Properties of Accessory Olfactory Bulb Neurons: Limitations and Opportunities for Decoding

The vomeronasal system (VNS) is a major vertebrate chemosensory system that functions in parallel to the main olfactory system (MOS). Despite many similarities, the two systems dramatically differ in the temporal domain. While MOS responses are governed by breathing and follow a subsecond temporal scale, VNS responses are uncoupled from breathing and evolve over seconds. This suggests that the contribution of response dynamics to stimulus information will differ between these systems. While temporal dynamics in the MOS are widely investigated, similar analyses in the accessory olfactory bulb (AOB) are lacking. Here, we have addressed this issue using controlled stimulus delivery to the vomeronasal organ of male and female mice. We first analyzed the temporal properties of AOB projection neurons and demonstrated that neurons display prolonged, variable, and neuron-specific characteristics. We then analyzed various decoding schemes using AOB population responses. We showed that compared with the simplest scheme (i.e., integration of spike counts over the entire response period), the division of this period into smaller temporal bins actually yields poorer decoding accuracy. However, optimal classification accuracy can be achieved well before the end of the response period by integrating spike counts within temporally defined windows. Since VNS stimulus uptake is variable, we analyzed decoding using limited information about stimulus uptake time, and showed that with enough neurons, such time-invariant decoding is feasible. Finally, we conducted simulations that demonstrated that, unlike the main olfactory bulb, the temporal features of AOB neurons disfavor decoding with high temporal accuracy, and, rather, support decoding without precise knowledge of stimulus uptake time

Temporal Response Properties of Accessory Olfactory Bulb Neurons: Limitations and Opportunities for Decoding

The vomeronasal system (VNS) is a major vertebrate chemosensory system that functions in parallel to the main olfactory system (MOS). Despite many similarities, the two systems dramatically differ in the temporal domain. While MOS responses are governed by breathing and follow a subsecond temporal scale, VNS responses are uncoupled from breathing and evolve over seconds. This suggests that the contribution of response dynamics to stimulus information will differ between these systems. While temporal dynamics in the MOS are widely investigated, similar analyses in the accessory olfactory bulb (AOB) are lacking. Here, we have addressed this issue using controlled stimulus delivery to the vomeronasal organ of male and female mice. We first analyzed the temporal properties of AOB projection neurons and demonstrated that neurons display prolonged, variable, and neuron-specific characteristics. We then analyzed various decoding schemes using AOB population responses. We showed that compared with the simplest scheme (i.e., integration of spike counts over the entire response period), the division of this period into smaller temporal bins actually yields poorer decoding accuracy. However, optimal classification accuracy can be achieved well before the end of the response period by integrating spike counts within temporally defined windows. Since VNS stimulus uptake is variable, we analyzed decoding using limited information about stimulus uptake time, and showed that with enough neurons, such time-invariant decoding is feasible. Finally, we conducted simulations that demonstrated that, unlike the main olfactory bulb, the temporal features of AOB neurons disfavor decoding with high temporal accuracy, and, rather, support decoding without precise knowledge of stimulus uptake time

Light-guided sectioning for precise in situ localization and tissue interface analysis for brain-implanted optical fibers and GRIN lenses

Optical implants to control and monitor neuronal activity in vivo have become foundational tools of neuroscience. Standard two-dimensional histology of the implant location, however, often suffers from distortion and loss during tissue processing. To address that, we developed a three-dimensional post hoc histology method called “light-guided sectioning” (LiGS), which preserves the tissue with its optical implant in place and allows staining and clearing of a volume up to 500 μm in depth. We demonstrate the use of LiGS to determine the precise location of an optical fiber relative to a deep brain target and to investigate the implant-tissue interface. We show accurate cell registration of ex vivo histology with single-cell, two-photon calcium imaging, obtained through gradient refractive index (GRIN) lenses, and identify subpopulations based on immunohistochemistry. LiGS provides spatial information in experimental paradigms that use optical fibers and GRIN lenses and could help increase reproducibility through identification of fiber-to-target localization and molecular profiling

RecV recombinase system for in vivo targeted optogenomic modifications of single cells or cell populations

Brain circuits comprise vast numbers of interconnected neurons with diverse molecular, anatomical and physiological properties. To allow targeting of individual neurons for structural and functional studies, we created light-inducible site-specific DNA recombinases based on Cre, Dre and Flp (RecVs). RecVs can induce genomic modifications by one-photon or two-photon light induction in vivo. They can produce targeted, sparse and strong labeling of individual neurons by modifying multiple loci within mouse and zebrafish genomes. In combination with other genetic strategies, they allow intersectional targeting of different neuronal classes. In the mouse cortex they enable sparse labeling and whole-brain morphological reconstructions of individual neurons. Furthermore, these enzymes allow single-cell two-photon targeted genetic modifications and can be used in combination with functional optical indicators with minimal interference. In summary, RecVs enable spatiotemporally precise optogenomic modifications that can facilitate detailed single-cell analysis of neural circuits by linking genetic identity, morphology, connectivity and function

RecV recombinase system for in vivo targeted optogenomic modifications of single cells or cell populations

Brain circuits comprise vast numbers of interconnected neurons with diverse molecular, anatomical and physiological properties. To allow targeting of individual neurons for structural and functional studies, we created light-inducible site-specific DNA recombinases based on Cre, Dre and Flp (RecVs). RecVs can induce genomic modifications by one-photon or two-photon light induction in vivo. They can produce targeted, sparse and strong labeling of individual neurons by modifying multiple loci within mouse and zebrafish genomes. In combination with other genetic strategies, they allow intersectional targeting of different neuronal classes. In the mouse cortex they enable sparse labeling and whole-brain morphological reconstructions of individual neurons. Furthermore, these enzymes allow single-cell two-photon targeted genetic modifications and can be used in combination with functional optical indicators with minimal interference. In summary, RecVs enable spatiotemporally precise optogenomic modifications that can facilitate detailed single-cell analysis of neural circuits by linking genetic identity, morphology, connectivity and function

Extracting Behaviorally Relevant Traits from Natural Stimuli: Benefits of Combinatorial Representations at the Accessory Olfactory Bulb.

For many animals, chemosensation is essential for guiding social behavior. However, because multiple factors can modulate levels of individual chemical cues, deriving information about other individuals via natural chemical stimuli involves considerable challenges. How social information is extracted despite these sources of variability is poorly understood. The vomeronasal system provides an excellent opportunity to study this topic due to its role in detecting socially relevant traits. Here, we focus on two such traits: a female mouse's strain and reproductive state. In particular, we measure stimulus-induced neuronal activity in the accessory olfactory bulb (AOB) in response to various dilutions of urine, vaginal secretions, and saliva, from estrus and non-estrus female mice from two different strains. We first show that all tested secretions provide information about a female's receptivity and genotype. Next, we investigate how these traits can be decoded from neuronal activity despite multiple sources of variability. We show that individual neurons are limited in their capacity to allow trait classification across multiple sources of variability. However, simple linear classifiers sampling neuronal activity from small neuronal ensembles can provide a substantial improvement over that attained with individual units. Furthermore, we show that some traits are more efficiently detected than others, and that particular secretions may be optimized for conveying information about specific traits. Across all tested stimulus sources, discrimination between strains is more accurate than discrimination of receptivity, and detection of receptivity is more accurate with vaginal secretions than with urine. Our findings highlight the challenges of chemosensory processing of natural stimuli, and suggest that downstream readout stages decode multiple behaviorally relevant traits by sampling information from distinct but overlapping populations of AOB neurons

Experimental approach.

<p><b>A</b>. The experimental preparation. Stimuli are presented to the VNO through the nostril, and then the sympathetic nerve trunk is stimulated with a cuff electrode to induce VNO suction. Extracellular multi-site probes (32 channels) were targeted to the external cell layer of the AOB. Inset shows a 20μm sagittal section including the main and accessory olfactory bulbs. One tract made by a probe dipped in DiI (red) prior to insertion can be seen. Blue: DAPI nuclear stain. <b>B</b>. Illustration of stimulus sources (secretions) used. <b>C.</b> Datasets used in this study. L,M,H represent stimuli of low, medium and high concentrations (for vaginal secretions: L = 9x dilution, M = 3x, H = 1x, for urine: L = 300x, M = 100x, H = 33x). In stimulus set 3, stimuli were not diluted following collection. <b>D.</b> Outline of the classification approach. Single-trial responses (see rasters for five trials, one of which is highlighted in red) for each unit were defined as the mean firing rate change following stimulus presentation (indicated by bar). Response vectors of all units are then used to train and test specific classifiers. The cartoon networks on the right show classifiers with hypothetical downstream units (blue) receiving inputs from AOB units (gray). The cartoons illustrate that different classifiers may assign different weights to each of the units.</p

Comparison of classifiers and effects of unit removal.

<p>Comparison of the performance of the best individual unit, best perceptron classifier, and best SVM classifier (left in each panel), and an analysis of the effects of unit removal on perceptron performance (right panel). Each panel shows results of 10 repeated unit removal cycles. In each stage of each cycle, the one unit with the highest absolute weight was removed. The dotted red line indicates the performance of the best individual unit. The vertical black line indicates the 10 cycle average of the number of units removed before perceptron performance drops below that obtained with the best individual unit. Values are given in the text. <b>A.</b> Reproductive state classifications with vaginal secretions. <b>B.</b> Strain classifications with vaginal secretions. <b>C.</b> Reproductive state classifications with urine. <b>D.</b> Strain classifications with urine. <b>E.</b> Reproductive state classifications across secretions. <b>F.</b> Strain classifications across secretions.</p

Comparison of state and strain classifications with vaginal secretions and urine.

<p><b>A.</b> Classification of strain using vaginal secretions (stimulus set 1). <b>B.</b> Classification of state using vaginal secretions. <b>C.</b> Classification of strain using urine (stimulus set 2). <b>D.</b> Classification of state using urine. Each bar represents the performance of one type of classification with 50 units (the best value over the 10 repeated training cycles is shown). When multiple classifications of a given type exist (i.e. six pairwise and two dilution invariant classifications), the mean values are shown.</p

Analysis of classifier weights.

<p><b>A.</b> Same data as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004798#pcbi.1004798.g003" target="_blank">Fig 3A</a>, sorted according to the general state classifier weights, which are indicated on the right by blue bars. <b>B.</b> Like A, but unit ordering and weights are for the general strain classifier. <b>C</b> and <b>D</b>, bootstrap analysis of the reproductive-state and strain dimensions in the vaginal secretions dataset. The index corresponding to each dimension is indicated by the black line, while the distribution of this value in shuffled data is shown in blue. Only the strain dimension is represented more prominently than expected by chance. <b>E.</b> Same data with units arranged according to the weights assigned by the state classifier for the BC strain. The colored bars below indicate the columns that are used for training and testing the classifier (other columns are ignored during training of this classifier). <b>F</b>. Like E, but units are sorted according to the weights assigned by the state classifier for the C57 strain. <b>G.</b> Correlation between (normalized) weights for the general vaginal secretion state and strain classifiers (weights shown in panels 6A and B). Single units are indicated in blue, multi-units are shown in red.</p