The Functional Logic of the Odor Information Processing Circuits


In our working model of the early olfactory system, OSNs expressing the same receptor type form a single channel. Antenna channels operate independently in parallel and represent the receptor map parallel processing model. As shown in the figure below, a more general parallel processing model operates in the antennal lobe.

https://raw.githubusercontent.com/bionet/bionet.github.io/img/img/rmppm.jpg

Odorant Transduction and Combinatorial Encoding in the Antenna

A key functionality of olfactory sensory neurons (OSNs) in the Drosophila antennae is to jointly encode both odorant identity and odorant concentration. The identity of an odorant is combinatorially encoded by the set of responding OSN groups expressing the same receptor type, and the size of OSN set varies as the concentration changes. The temporal response of an OSN simultaneously represents the information of odorant concentration and concentration gradient. We advanced a comprehensive model of fruit fly OSNs as a cascade consisting of an odorant transduction process and a biophysical spike generator. Open source code and documentation is available as part of the FlyBrainLab Odorant Transduction Library.

  1. Aurel A. Lazar and Chung-Heng Yeh, A Parallel Processing Model of Drosophila Olfactory Sensory Neurons and Its Biological Validation, bioRxiv, December 2017.
  2. Aurel A. Lazar and Chung-Heng Yeh, A Molecular Odorant Transduction Model and the Complexity of Spatio-Temporal Encoding in the Drosophila Antenna, PLOS Computational Biology, Volume 16, Number 4, April 2020.

Predictive Coding in the Antennal Lobe

By modeling the odorant identity and concentration as an odorant-receptor affinity tensor modulated by the odorant concentration profile, we have shown that the OSN spike train input to the predictive coding circuit is concentration dependent, and the affinity value characterizing different OSN types results in shifts of the OSN odorant concentration versus PSTH response curve. Second, we have demonstrated that the LNs by inhibiting the axonal terminals drive the OSN axonal terminals to encode the odorant-receptor affinity value independently of the odorant concentration amplitude. Furthermore, the temporal response curves are contrast invariant across orders of magnitude of concentration amplitude values.

  1. Aurel A. Lazar and Chung-Heng Yeh, Predictive Coding in the Drosophila Antennal Lobe, BMC Neuroscience 2019, 20 (Suppl 1): P346 , 28th Annual Computational Neuroscience Meeting, July 13-17, 2019, Barcelona, Spain.
  2. Aurel A. Lazar, Tingkai Liu, and Chung-Heng Yeh, An Odorant Encoding Machine for Sampling, Reconstruction and Robust Representation of Odorant Identity, ICASSP 2020, pp. 1743-1747, May 4-8, 2020, Barcelona, Spain.

Comparing Adult Antenna/Antennal Lobe Circuit Models based upon the FlyCircuit and Hemibrain Datasets

We depict below a comparison between two models of the antenna and antennal lobe circuit of the adult fly based on the FlyCircuit (left) dataset (Chiang et al., 2011) and an exploratory model based on the Hemibrain (right) dataset (Scheffer et al., 2020). For more details, see

  1. Aurel A. Lazar, Tingkai Liu, Mehmet K. Turkcan, and Yiyin Zhou, FlyBrainLab: Accelerating the Discovery of the Functional Logic of the Fruit Fly Brain in the Connectomic and Synaptomic Era, eLife 2021;10:e62362, February 2021.
https://raw.githubusercontent.com/bionet/bionet.github.io/img/img/fbl_compare_datasets.jpg

(a) Morphology of olfactory sensory neurons, local neurons, and projection neurons in the antennal lobe for the two datasets. The axons of the projection neurons and their projections to the mushroom body and lateral horn are also visible. (b) Circuit diagrams depicting the antenna and antennal lobe circuit motifs derived from the two datasets. (c) Response of the antenna/antennal lobe circuit to a constant ammonium hydroxide step input applied between 1 s and 3 s of a 5 s simulation; (left) the interaction between the odorant and 23 olfactory receptors is captured as the vector of affinity values; (middle and right) a heatmap of the uniglomerular PN PSTH values (spikes/second) grouped by glomerulus for the two circuit models. (d) The PN response transients of the two circuit models for uniform noise input with a minimum of 0ppm and a maximum of 100 ppm preprocessed with a 30 Hz low-pass filter (Kim et al., 2011) and delivered between 1 s and 3 s.

Comparing Early Olfactory Circuit Models of the Larva and the Adult Fruit Flies

We depict below a comparison of two Drosophila Early Olfactory System (EOS) models describing adult (left, developed based on Hemibrain dataset) and larval (right, developed based on LarvaEM dataset) circuits. For more details, see

  1. Aurel A. Lazar, Tingkai Liu, Mehmet K. Turkcan, and Yiyin Zhou, FlyBrainLab: Accelerating the Discovery of the Functional Logic of the Fruit Fly Brain in the Connectomic and Synaptomic Era, eLife 2021;10:e62362, February 2021.

https://raw.githubusercontent.com/bionet/bionet.github.io/img/img/fbl_compare_development.jpg

(a) Morphology of olfactory sensory neurons in the antenna, local neurons and projection neurons (PNs) in the antennal lobe. (b) Circuit diagrams depicting the antenna and antennal lobe circuit motifs. (c) (left) Interaction between 13 odorants and 37 odorant receptors (ORs) characterized by affinity values. The ORs expressed only in the adult fruit flies are grouped in the top panel; the ones that are expressed in both the adult and the larva are grouped in the middle panel; and those expressed only in the larva are shown in the bottom panel. Steady-state outputs of the EOS models to a step concentration waveform of 100 ppm are used to characterize combinatorial codes of odorant identities at the OSN level (middle) and the PN level (right).

Functional Role of the Mushroom Body

The mushroom body (MB) as a memory processor involved in associative learning that enables flies to navigate in complex environments. To guide navigation, the MB integrates internal states and a diverse palette of sensory data including olfactory, visual, mechanosensory, and motion control information and forwards the processed information streams to, among others, motor control.

  1. Aurel A. Lazar, Tingkai Liu, and Chung-Heng Yeh, Odorant Encoding Machine for Sampling, Reconstruction and Robust Representation of Odorant Identity, ICASSP 2020, pp. 1743-1747, May 4-8, 2020, Barcelona, Spain.
  2. Aurel A. Lazar, Tingkai Liu, and Chung-Heng Yeh, The Geometry of Spatio-Temporal Odorant Mixture Encoding in the Drosophila Mushroom Body, BMC Neuroscience 2020, 21(Suppl 1):P218, CNS*2020, July 2020.
  3. Aurel A. Lazar, Mehmet Kerem Turkcan, and Yiyin Zhou, Generating Executable Mushroom Body and Lateral Horn Circuits from the Hemibrain Dataset with FlyBrainLab, BMC Neuroscience 2020, 21(Suppl 1):P105, CNS*2020, July 2020.

In Vivo Recordings in the Early Olfactory System

We believe that the lack of a deeper understanding of how Olfactory Sensory Neurons (OSNs) encode odorants has fundamentally hindered progress in understanding olfactory signal processing in higher brain centers. Moreover, the lack of precise stimulus delivery and measurement systems has fundamentally limited the progress of functional identification in olfaction.

To address this limitation, we developed a novel in vivo experimental setup with precise and reproducible delivery of airborne stimuli. This experimental setup enabled us to apply system identification methods to OSNs in Drosophila. We applied time-varying odorant stimuli and recorded in vivo the response of Projection Neurons (PNs) postsynaptic to OSNs. These novel type of recordings have shown that individual OSNs and PNs encode the gradient and acceleration of odorant concentration waveforms, respectively. This research was performed in collaboration with Dr. Richard Axel in the Axel Laboratory.


  1. Anmo J. Kim, Aurel A. Lazar and Yevgeniy B. Slutskiy, System Identification of Drosophila Olfactory Sensory Neurons , Journal of Computational Neuroscience, Vol. 30, No.1, February 2011, pp. 143-161, Special Issue on Methods of Information Theory.
  2. A.J. Kim, A.A. Lazar, and Y.B. Slutskiy, Projection Neurons in Drosophila Antennal Lobes Signal the Acceleration of Odor Concentrations, eLife 2015;10.7554/eLife.06651, June 2015.
  3. A.A. Lazar and C.-H. Yeh, Functional Identification of an Antennal Lobe DM4 Projection Neuron of the Fruit Fly, Computational Neuroscience Meeting, Volume 15, July 2014, Québec City, Canada.



The Bionet Group is supported by grants from



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