Biology subjects

Tootoonian, S.

Publications and source records attributed to Tootoonian, S..

7 recordsLinked to original sources

bioRxiv2026

`Silent' mitral cells emerge from common feature subtraction

Whole-cell recordings from olfactory bulb mitral/tufted cells in awake mice revealed an inverse relationship between baseline activation and odour responses wherein cells that were spontaneously active tended to be inhibited by odours, while `silent' cells, quiet at baseline, tended to have vigorous odour responses. In this work we provide a normative explanation for these findings by extending previous sparse coding models of the olfactory system to include `common feature subtraction,' an odour-triggered variant of background subtraction in which the receptor contribution of features common to the odours being discriminated is subtracted. Using a circuit-level formulation of this computation consistent with the anatomy of the olfactory bulb we show how baseline activity cancels the effect of the common olfactory features on receptor activations, promoting inference of the target odours. We provide simulation results showing qualitative agreement with experimental findings. The model also predicts in closed form, without fitting, the slopes of the relationships between a cell's baseline and its common input and between its baseline and its odour response, and these predictions agree with simulations. Our model makes the experimentally testable prediction that a cell's baseline activation is not an intrinsic property, but reflects the negative of its recent activity.

neuroscience↗

bioRxiv2026

Decorrelation by gain control in the mouse olfactory bulb

Classifying sensory stimuli requires neural circuits to transform overlapping inputs into representations that are more distinct and decodable. Odour representations in the olfactory bulb (OB) have been reported to decorrelate from sensory input to output, a transformation thought to enhance odour separability. What has been missing is a systematic quantification of this transformation across odours, and a mechanistic account of how it can be implemented by OB circuitry. Using in vivo two-photon calcium imaging, we recorded glomerular input and output responses to a chemically diverse panel of 47 odours in mice. We observed a robust decorrelation of odour-evoked activity patterns from input to output: correlations between odour representations were consistently reduced, while overall response variance was preserved. This transformation increased the dimensionality of the population code and improved the separability of odour representations. Consistent with this, classification analyses showed that odour identity could be decoded with significantly higher accuracy at the output. To understand the mechanisms underlying this transformation, we developed a linear model of the OB with different levels of connectivity. Models in which each channel underwent only self-gain modulation reproduced the observed decorrelation nearly as well as models with fully unconstrained lateral connectivity. Analysis revealed that channels contributing unique information were selectively amplified, while redundant channels were attenuated. Together, our results show that the OB implements a measurable input-output transformation that enhances odour separability. By combining systematic recordings with computational modelling, we found that feedforward gain modulation serves as a simple and scalable mechanism capable of explaining the observed decorrelation, reframing how we understand the functional architecture of early olfactory processing.

neuroscience↗

bioRxiv2025

Structure-Function Mapping of Olfactory Bulb Circuits with Synchrotron X-ray Nanotomography

Information is routed between brain areas via parallel streams. Neurons may share common inputs yet convey distinct information to different downstream targets. Here, we leverage the anatomical organisation of the mouse olfactory bulb (OB), where dozens of projection neurons (mitral and tufted cells, M/TCs) affiliate with a single input unit, a glomerulus1-3. To link functional properties of M/TCs to their anatomical glomerular association at scale, we combine in vivo two-photon (2P) imaging with synchrotron {micro}CT4-6 anatomical analysis and targeted X- ray nano-holotomography (XNH)7,8. Improving XNH resolution for mm3 volumes enables us to reliably identify subcellular features, automatically segment >80,000 cell nuclei in individual experiments, and delineate several hundred functionally imaged projection neurons and their detailed morphology, including up to 20 M/TCs per individual glomerulus ("sister" cells). In over 2400 sister cell pairs, we consistently find that odour response amplitudes to a panel of 47 monomolecular odours are conserved between sister cells, with, however, distinct responses to individual odours. Responses correlated with anatomical features such as cell body position and lateral dendritic arborisation. Thus, sister cells neither simply relay glomerular inputs nor are they dominated by network activity. Instead, they show a "balanced diversity" in their responses, enabling efficient encoding of odour stimuli whilst retaining the overall structure of odour space. Thus, synchrotron X-ray tomography can reliably link subcellular anatomy to function in a non-destructive way across the mm3 scale. With recent advances in X-ray optics9 and the emergence of 4th generation synchrotrons10,11, it becomes conceivable to extend this highly accessible approach to entire brain regions with increasing resolution.

neuroscience↗

bioRxiv2024

Robust encoding of sub-sniff temporal information in the mouse olfactory bulb

SummaryThe sensory world is highly dynamic, and the temporal structure of stimuli contains rich information about the environment. Odour plumes are shaped by complex airflow that imprint information about the nature and spatial organisation of the olfactory environment onto their temporal dynamics. Whilst insects and mammals alike can discern high-frequency information, how temporal properties of the olfactory environment are represented in the brain remains largely unknown. Here, we presented temporally rich and systematically varying odour stimuli whilst electrically recording from the output neurons of the mouse olfactory bulb, mitral and tufted cells (MTC). We found that temporal aspects of odour stimuli could readily be read out from MTC responses, with a temporal resolution of up to 20 ms. Remarkably, temporal representation was virtually identical across three different odours. To understand which temporal features are encoded, we developed a single-cell model accurately describing both single-cell and population responses. Temporal receptive fields of MTCs translated between different odours, indicating that MTC tuning to odour quality and dynamics are partially separable. Together, this suggests a stereotypical representation of odour dynamics across projection neurons and can serve as an entry point into dissecting mechanisms underlying how information about the environment is extracted from temporally fluctuating odour plumes.

neuroscience↗

bioRxiv2024

Quantifying spectral information about source separation in multisource odour plumes

Odours released by objects in natural environments can contain information about their spatial locations. In particular, the correlation of odour concentration fields produced by two spatially separated sources contains information about the distance between the sources. Mice are able to distinguish correlated and anti-correlated odour fluctuations at frequencies up to 40 Hz. Can this high-frequency acuity support odour source localization? Here we answer this question by quantifying the spatial information about source separation contained in the spectral constituents of correlations. We used computational fluid dynamics simulations of multisource plumes in two-dimensional chaotic flow environments to generate temporally complex, covarying odour concentration fields. By relating the correlation of these fields to the spectral decompositions of the associated odour concentration timeseries, and making simplifying assumptions about the statistics of these decompositions, we derived analytic expressions for the Fisher information contained in the spectral components of the correlations about source separation. We computed the Fisher information for a broad range of frequencies and source separations and found that high frequencies were more informative than low frequencies when sources were close relative to the sizes of the large eddies in the flow. We observed a qualitatively similar effect in an independent set of simulations with different geometry, but not for surrogate data with a similar power spectrum to our simulations but in which all frequencies were a priori equally informative. Our work suggests that the high-frequency acuity of the murine olfactory system may support high-resolution spatial localization of odour sources. We also provide a model of the distribution of the spectral components of correlations that is accurate over a broad range of frequencies and source separations. More broadly, our work establishes an approach for the quantification of the spatial information in odour concentration timeseries.

biophysics↗

bioRxiv2021

Sparse connectivity for MAP inference in linear models using sister mitral cells

Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in studies of sensory processing is to understand how the brain extracts those variables. Here we revisit a common encoding model in which variables are encoded linearly. Although there are typically more variables than neurons, this problem is still solvable because only a small number of variables appear at any one time (sparse prior). However, previous solutions require all-to-all connectivity, inconsistent with the sparse connectivity seen in the brain. Here we propose an algorithm that provably reaches the MAP (maximum a posteriori) inference solution, but does so using sparse connectivity. Our algorithm is inspired by the circuit of the mouse olfactory bulb, but our approach is general enough to apply to other modalities. In addition, it should be possible to extend it to nonlinear encoding models. SummarySensory systems must infer latent variables from noisy and ambiguous input. MAP inference - choosing the most likely latent variable given the sensory input - is one of the simplest methods for doing that, but its neural implementation often requires all-to-all connectivity between the neurons involved. In common sensory contexts this can require a single neuron to connect to hundreds of thousands of others, which is biologically implausible. In this work we take inspiration from the sister mitral cells of the olfactory system - groups of neurons associated with the same input channel - to derive a method for performing MAP inference using sparse connectivity. We do so by assigning sister cells to random subsets of the latent variables and using additional cells to ensure that sisters correctly share information. We then derive the circuitry and dynamics required for the sister cells to compute the original MAP inference solution. Our work yields a biologically plausible circuit that provably solves the MAP inference problem and provides experimentally testable predictions. While inspired by the olfactory system, our method is quite general, and is likely to apply to other sensory modalities.

neuroscience↗

bioRxiv2018

Functional and Anatomical Specificity in a Higher Olfactory Centre

Most sensory systems are organized into parallel neuronal pathways that process distinct aspects of incoming stimuli. For example, in insects, second order olfactory projection neurons target both the mushroom body, which is required for learning, and the lateral horn (LH), which has been proposed to mediate innate olfactory behavior. Mushroom body neurons encode odors in a sparse population code, which does not appear stereotyped across animals. In contrast the functional principles of odor coding in the LH remain poorly understood. We have carried out a comprehensive anatomical analysis of the Drosophila LH, counting ~1400 neurons; combining genetic driver lines, anatomical and functional criteria, we identify 165 LHN cell types. We then show that genetically labeled LHNs have stereotyped odor responses across animals for 33 of these cell types. LHN tuning can be ultra-sparse (1/40 odors tested), but on average single LHNs respond to three times more odors than single projection neurons. This difference can be rationalized by our observation that LHNs are better odor categorizers, likely due to pooling of input projection neurons responding to different odors of the same category. Our results reveal some of the principles by which a higher sensory processing area can extract innate behavioral significance from sensory stimuli.

neuroscience↗