The Functional Logic of Odor Information Processing in the <i>Drosophila</i> Antennal Lobe

Lazar, Aurel A.; Liu, Tingkai; Yeh, Chung-Heng

doi:10.1101/2021.12.27.474306

Cited by 4 publications

(8 citation statements)

References 44 publications

(77 reference statements)

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“…This sparsity has been attributed to the feedback inhibition from APL to KC [21,29,30,26,31,32]. While the exact APL to KC inhibition mechanism in the Mushroom Body Calyx is unclear, we applied the differential Divisive Normalization Processor model proposed in [2] to describe the PN-KC-APL interaction in the KC Dendritic Tree. Therefore, the PN-KC-APL circuit (shown in Fig.…”

Section: Modeling the Expansion/normalization Architecture Of The Pn-...mentioning

confidence: 99%

“…We define the dictionary of pure odorant responses as where N is the appropriate dimension of the population responses, e.g., N = 50 for PNs and N = 2000 for KCs. While PN response is significantly less concentration dependent than the OSN response [2], the dictionary R is parameterized by concentration u to account for imperfect concentration-invariance of PN response.…”

Section: Algorithms For Odorant Demixingmentioning

confidence: 99%

“…It has previously been proposed that the Antenna encodes odorant identity and concentration waveform via multiplicative coupling [1], and the Antennal Lobe separates the odorant identity and concentration waveform via an ON-OFF odorant objective identity recovery processor implemented by the Local Neuron circuits [2]. However, the discussion of odorant identity recovery in the Antennal Lobe only pertains to mono-molecular odorants, and it remains unclear how odorant mixtures are represented in the early olfactory pathway.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Odorant Mixture Separation in Drosophila Early Olfactory System

Lazar

Liu

Yeh

et al. 2022

Preprint

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Natural odorant scenes are complex landscapes comprising mixtures of volatile compounds. It was previously proposed that the Antennal Lobe circuit recovers the odorant identity in a concentration-invariant manner via divisive normalization of Local Neurons. It remains unclear, however, how identities of odorant components in a mixture is represented or recovered in the fruit fly early olfactory pathway. In the current work, we take a different approach from the traditional steady-state analyses that classify odorant mixture encoding into configural vs. elemental schemes. Instead, we focus on the spatio-temporal responses of the early olfactory pathway at the levels of the Antennal Lobe and the Mushroom Body, and formulate the odorant demixing problem as a blind source separation problem - where the identities of each individual odorant component and their corresponding concentration waveforms are recovered from the spatio-temporal PSTH of Olfactory Sensory Neurons (OSNs), Projection Neurons (PNs), and Kenyon Cells (KCs) respectively. Building upon previous models of the Antenna and the Antennal Lobe, we advanced a feedback divisive normalization architecture of the Mushroom Body Calyx circuit comprised of PN, KC and the giant Anterior Paired Lateral (APL) neuron. We demonstrate that the PN KC-APL circuit produces a high dimensional representation of odorant mixture with robust sparsity, and results in greater odorant demixing performance than the PN responses.

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Section: Modeling the Expansion/normalization Architecture Of The Pn-...mentioning

confidence: 99%

Section: Algorithms For Odorant Demixingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Odorant Mixture Separation in Drosophila Early Olfactory System

Lazar

Liu

Yeh

et al. 2022

Preprint

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“…The recovery of odorant object identity at the Antennal Lobe level calls for decoupling the semantic information from the syntactic information carried by the amplitude of the concentration waveform. The question arises here whether the AL circuit can generate concentrationinvariant multi-channel PN responses both between random encounters with a given odorant at different concentration levels, as well as at temporally fluctuating concentration levels [11][12][13]. However, to the best of our knowledge, no unified theoretical framework that accounts for concentration invariance across receptor channels, across time and across odorant object encounters have been proposed [14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

The functional logic of odor information processing in the Drosophila antennal lobe

2023

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Recent advances in molecular transduction of odorants in the Olfactory Sensory Neurons (OSNs) of the Drosophila Antenna have shown that the odorant object identity is multiplicatively coupled with the odorant concentration waveform. The resulting combinatorial neural code is a confounding representation of odorant semantic information (identity) and syntactic information (concentration). To distill the functional logic of odor information processing in the Antennal Lobe (AL) a number of challenges need to be addressed including 1) how is the odorant semantic information decoupled from the syntactic information at the level of the AL, 2) how are these two information streams processed by the diverse AL Local Neurons (LNs) and 3) what is the end-to-end functional logic of the AL? By analyzing single-channel physiology recordings at the output of the AL, we found that the Projection Neuron responses can be decomposed into a concentration-invariant component, and two transient components boosting the positive/negative concentration contrast that indicate onset/offset timing information of the odorant object. We hypothesized that the concentration-invariant component, in the multi-channel context, is the recovered odorant identity vector presented between onset/offset timing events. We developed a model of LN pathways in the Antennal Lobe termed the differential Divisive Normalization Processors (DNPs), which robustly extract the semantics (the identity of the odorant object) and the ON/OFF semantic timing events indicating the presence/absence of an odorant object. For real-time processing with spiking PN models, we showed that the phase-space of the biological spike generator of the PN offers an intuit perspective for the representation of recovered odorant semantics and examined the dynamics induced by the odorant semantic timing events. Finally, we provided theoretical and computational evidence for the functional logic of the AL as a robust ON-OFF odorant object identity recovery processor across odorant identities, concentration amplitudes and waveform profiles.

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“…The object structure of the space of natural sensory stimuli that the fruit flies constantly sample has not been discussed in the formal ontology of the fly brain anatomy. Although natural stimuli have been widely used in sensory neuroscience (Egelhaaf et al, 2002 ; Tootoonian et al, 2012 ; Jeanne et al, 2018 ), the modeling of the object structure of the environment (Lazar et al, 2022 ) has often been neglected in the neuroscience literature at large. The aforementioned object structure is, however, essential in defining, characterizing and evaluating the functional logic of brain circuits.…”

Section: Introductionmentioning

confidence: 99%

A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain

Lazar

Türkcan

Zhou

2022

Front. Neuroinform.

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The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice and humans. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain circuits at this scale poses significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for modeling circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of a massive number of local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe.

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The Functional Logic of Odor Information Processing in the Drosophila Antennal Lobe

Cited by 4 publications

References 44 publications

Odorant Mixture Separation in Drosophila Early Olfactory System

Odorant Mixture Separation in Drosophila Early Olfactory System

The functional logic of odor information processing in the Drosophila antennal lobe

A Programmable Ontology Encompassing the Functional Logic of the Drosophila Brain

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