Deconstructing Odorant Identity via Primacy in Dual Networks

Kepple, Daniel; Giaffar, Hamza; Rinberg, Dmitry; Koulakov, Alexei A.

doi:10.1162/neco_a_01175

Cited by 17 publications

(37 citation statements)

References 36 publications

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“…Third, demixing models often interpret nonnegative neural activity in KCs (spiking neurons cannot have negative firing rates) as a manifestation of the physical nonnegativity of molecular concentrations (counts of molecules cannot be negative) [36]. However, such explanation does not generalize to most other neurons in the brain whose activity is nevertheless nonnegative.…”

Section: A Demixing Network Modelsmentioning

confidence: 99%

A clustering neural network model of insect olfaction

Pehlevan

Genkin

Chklovskii

2017

Preprint

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Abstract-A key step in insect olfaction is the transformation of a dense representation of odors in a small population of neurons -projection neurons (PNs) of the antennal lobe -into a sparse representation in a much larger population of neuronsKenyon cells (KCs) of the mushroom body. What computational purpose does this transformation serve? We propose that the PN-KC network implements an online clustering algorithm which we derive from the k-means cost function. The vector of PN-KC synaptic weights converging onto a given KC represents the corresponding cluster centroid. KC activities represent attribution indices, i.e. the degree to which a given odor presentation is attributed to each cluster. Remarkably, such clustering view of the PN-KC circuit naturally accounts for several of its salient features. First, attribution indices are nonnegative thus rationalizing rectification in KCs. Second, the constraint on the total sum of attribution indices for each presentation is enforced by a Lagrange multiplier identified with the activity of a single inhibitory interneuron reciprocally connected with KCs. Third, the soft-clustering version of our algorithm reproduces observed sparsity and overcompleteness of the KC representation which may optimize supervised classification downstream.

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Section: A Demixing Network Modelsmentioning

confidence: 99%

A clustering neural network model of insect olfaction

Pehlevan

Genkin

Chklovskii

2017

Preprint

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“…The predicted matching of the GC receptive fields for olfactory and for memory, which may indicate that a higher brain area has recognized parts of the odor 351 scene. This may allow something akin to the 'explaining away' of components of a 352 complex odor mixture that is theoretically predicted for the optimal processing of 353 stimuli [4][5][6]12]. Recent work has identified networks that demix familiar odors 354 employing approximate optimal Bayesian inference; the anatomical structure of these 355 networks is very close to that emerging naturally in our neurogenic model [6,7].…”

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confidence: 89%

“…To assess the discriminability of MC activity patterns we assumed that the firing 376 rates result from an irregular firing in which the variance of the spike number is 377 proportional, albeit not necessarily equal, to the mean spike number. Thus, we have not 378 taken the widely observed rhythmic aspect of the bulbar activity into account [53], 379 which suggests that animals may also be able to make use of spike-timing and synchrony 380 information in odor processing [5,54,55]. The modular network structure and the 381 associated top-down inputs that are predicted by our model are likely to have also 382 substantial impact on spike timing and synchrony.…”

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confidence: 95%

“…The top-down projections can provide the receiving brain area with 4 information that is not available in the feedforward stream [2] and can switch the 5 processing by the lower brain area between different modes [3]. From a theoretical 6 perspective, it has been posited that top-down signals can direct the lower brain area to 7 suppress response to expected inputs or to inputs that have already been recognized by 8 the higher brain area [4][5][6][7] and to transmit predominantly information about 9 unexpected or unexplained inputs and the error in the prediction of the inputs [8],…”

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confidence: 99%

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Top-down inputs drive neuronal network rewiring and context-enhanced sensory processing in olfaction

Adams

Graham

Han

et al. 2018

Preprint

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Much of the computational power of the mammalian brain arises from its extensive top-down projections. To enable neuron-specific information processing these projections have to be precisely targeted. How such a specific connectivity emerges and what functions it supports is still poorly understood. We addressed these questions in silico in the context of the profound structural plasticity of the olfactory system. At the core of this plasticity are the granule cells of the olfactory bulb, which integrate bottom-up sensory inputs and top-down inputs delivered by vast top-down projections from cortical and other brain areas. We developed a biophysically supported computational model for the rewiring of the top-down projections and the intra-bulbar network via adult neurogenesis. The model captures various previous physiological and behavioral observations and makes specific predictions for the cortico-bulbar network connectivity that is learned by odor exposure and environmental contexts. Specifically, it predicts that after learning the granule-cell receptive fields with respect to sensory and with respect to cortical inputs are highly correlated. This enables cortical cells that respond to a learned odor to enact disynaptic inhibitory control specifically of bulbar principal cells that respond to that odor. Functionally, the model predicts context-enhanced stimulus discrimination in cluttered environments ('olfactory cocktail parties') and the ability of the system to adapt to its tasks by rapidly switching between different odor-processing modes. These predictions are experimentally testable. At the same time they provide guidance for future experiments aimed at unraveling the cortico-bulbar connectivity. Author summaryIn mammalian sensory processing, extensive top-down feedback from higher brain areas reshapes the feedforward, bottom-up information processing. The structure of the top-down connectivity, the mechanisms leading to its specificity, and the functions it supports are still poorly understood. Using computational modeling, we investigated these issues in the olfactory system. There, the granule cells of the olfactory bulb, which is the first brain area to receive sensory input from the nose, are the key players of extensive structural changes to the network through the addition and also the removal of granule cells as well as through the formation and removal of their connections. This structural plasticity allows the system to learn and to adapt its sensory processing to its PLOS 1/21 odor environment. Crucially, the granule cells combine bottom-up sensory input from the nose with top-down input from higher brain areas, including cortex. Our biophysically supported computational model predicts that, after learning, the granule cells enable cortical neurons that respond to a learned odor to gain inhibitory control of principal neurons of the olfactory bulb, specifically of those that respond to the learned odor. Functionally, this allows top-down input to enhance odor discrimination in cluttered envir...

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“…Indeed, experiments 23 suggests that odors are encoded robustly by the receptor types that respond within a 24 given time window after sniff onset [23,24]. In particular, the odor identity could be 25 robustly encoded by a fixed number of the receptors that respond first, which is known 26 as primacy coding [23,25]. So far, it is unclear whether this simple coding scheme is 27 sufficient to explain the remarkable discriminatory capability of the olfactory system.…”

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confidence: 99%

Primacy coding facilitates effective odor discrimination when receptor sensitivities are tuned

Zwicker

2018

Preprint

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The olfactory system faces the difficult task of identifying an enormous variety of odors independent of their intensity. Primacy coding, where the odor identity is encoded by the receptor types that respond earliest, is one possible representation that can facilitate this task. So far, it is unclear whether primacy coding facilitates typical olfactory tasks and what constraints it implies for the olfactory system. In this paper, we develop a simple model of primacy coding, which we simulate numerically and analyze using a statistical description. We show that the encoded information depends strongly on the number of receptor types included in the primacy representation, but only weakly on the size of the receptor repertoire. The representation is independent of the odor intensity and the transmitted information is useful to perform typical olfactory tasks, like detecting a target odor or discriminating similar mixtures, with close to experimentally measured performance. Interestingly, we find situations in which a smaller receptor repertoire is advantageous for identifying a target odor. The model also suggests that overly sensitive receptor types could dominate the entire response and make the whole array useless, which allows us to predict how receptor arrays need to adapt to stay useful during environmental changes. By quantifying the information transmitted using primacy coding, we can thus connect microscopic characteristics of the olfactory system to its overall performance. Author summaryHumans can identify odors independent of their intensity. Experimental data suggest that this is accomplished by representing the odor identity by the earliest responding receptor types. Using theoretical modeling, we here show that such a primacy code allows discriminating odors with close to experimentally measured performance. This performance depends strongly on the number of receptors considered in the primacy code, but the receptor repertoire size is less important. The model also suggests a strong evolutionary pressure on the receptor sensitivities, which could explain observed receptor copy number adaptations. Taken together, the model connects detailed molecular measurements to large-scale psycho-physical measurements, which will contribute to our understanding of the olfactory system. July 9, 2018 1/22 1 The olfactory system identifies and discriminates odors for solving vital tasks like 2 navigating the environment, identifying food, and engaging in social interactions. These 3 tasks are complicated by the enormous variety of odors, which vary in composition and 4 in the concentrations of their individual molecules. In particular, the olfactory system 5 needs to separately recognize the odor identity (what is there?) and the odor intensity 6 (how much is there?). For instance, the identity is required to decide whether to 7 approach or avoid an odor source, whereas the intensity information is important for 8 localizing it. It is unclear how these two odor properties are separated. 9Odors are sensed by olfactor...

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Deconstructing Odorant Identity via Primacy in Dual Networks

Cited by 17 publications

References 36 publications

A clustering neural network model of insect olfaction

A clustering neural network model of insect olfaction

Top-down inputs drive neuronal network rewiring and context-enhanced sensory processing in olfaction

Primacy coding facilitates effective odor discrimination when receptor sensitivities are tuned

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