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Moore JP, Kamino K, Kottou R, Shimizu TS, Emonet T. Signal integration and adaptive sensory diversity tuning in Escherichia coli chemotaxis. Cell Syst 2024; 15:628-638.e8. [PMID: 38981486 PMCID: PMC11307269 DOI: 10.1016/j.cels.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 04/01/2024] [Accepted: 06/17/2024] [Indexed: 07/11/2024]
Abstract
In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E. coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal's ambient concentration increases. However, among competitive ligands, the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E. coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.
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Affiliation(s)
- Jeremy Philippe Moore
- Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Quantitative Biology Institute, Yale University, New Haven, CT 06511, USA
| | - Keita Kamino
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan
| | - Rafaela Kottou
- Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Quantitative Biology Institute, Yale University, New Haven, CT 06511, USA
| | | | - Thierry Emonet
- Molecular Cellular and Developmental Biology, Yale University, New Haven, CT 06511, USA; Quantitative Biology Institute, Yale University, New Haven, CT 06511, USA; Department of Physics, Yale University, New Haven, CT 06511, USA.
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2
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Choi K, Rosenbluth W, Graf IR, Kadakia N, Emonet T. Bifurcation enhances temporal information encoding in the olfactory periphery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.27.596086. [PMID: 38853849 PMCID: PMC11160621 DOI: 10.1101/2024.05.27.596086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.
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3
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Choi K, Rosenbluth W, Graf IR, Kadakia N, Emonet T. Bifurcation enhances temporal information encoding in the olfactory periphery. ARXIV 2024:arXiv:2405.20135v2. [PMID: 38855541 PMCID: PMC11160886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.
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Affiliation(s)
- Kiri Choi
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06511, USA
- Quantitative Biology Institute, Yale University, New Haven, Connecticut 06511, USA
- Swartz Foundation for Theoretical Neuroscience, Yale University, New Haven, Connecticut 06511, USA
| | - Will Rosenbluth
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06511, USA
| | - Isabella R. Graf
- Quantitative Biology Institute, Yale University, New Haven, Connecticut 06511, USA
- Department of Physics, Yale University, New Haven, Connecticut 06511, USA
| | - Nirag Kadakia
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06511, USA
- Quantitative Biology Institute, Yale University, New Haven, Connecticut 06511, USA
- Swartz Foundation for Theoretical Neuroscience, Yale University, New Haven, Connecticut 06511, USA
| | - Thierry Emonet
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06511, USA
- Quantitative Biology Institute, Yale University, New Haven, Connecticut 06511, USA
- Department of Physics, Yale University, New Haven, Connecticut 06511, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut 06511, USA
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4
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Moore JP, Kamino K, Kottou R, Shimizu TS, Emonet T. Signal Integration and Adaptive Sensory Diversity Tuning in Escherichia coli Chemotaxis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.08.527720. [PMID: 36798398 PMCID: PMC9934624 DOI: 10.1101/2023.02.08.527720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
In uncertain environments, phenotypic diversity can be advantageous for survival. However, as the environmental uncertainty decreases, the relative advantage of having diverse phenotypes decreases. Here, we show how populations of E. coli integrate multiple chemical signals to adjust sensory diversity in response to changes in the prevalence of each ligand in the environment. Measuring kinase activity in single cells, we quantified the sensitivity distribution to various chemoattractants in different mixtures of background stimuli. We found that when ligands bind uncompetitively, the population tunes sensory diversity to each signal independently, decreasing diversity when the signal ambient concentration increases. However, amongst competitive ligands the population can only decrease sensory diversity one ligand at a time. Mathematical modeling suggests that sensory diversity tuning benefits E. coli populations by modulating how many cells are committed to tracking each signal proportionally as their prevalence changes.
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Zavatone-Veth JA, Masset P, Tong WL, Zak JD, Murthy VN, Pehlevan C. Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.21.545947. [PMID: 37961548 PMCID: PMC10634677 DOI: 10.1101/2023.06.21.545947] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb. This model maps onto the neuron classes of the olfactory bulb, and recapitulates salient features of their connectivity and physiology. For circuit sizes comparable to the human olfactory bulb, we show that this model can accurately detect tens of odors within the timescale of a single sniff. We also show that this model can perform Bayesian posterior sampling for accurate uncertainty estimation. Fast inference is possible only if the geometry of the neural code is chosen to match receptor properties, yielding a distributed neural code that is not axis-aligned to individual odor identities. Our results illustrate how normative modeling can help us map function onto specific neural circuits to generate new hypotheses.
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Affiliation(s)
- Jacob A Zavatone-Veth
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Physics, Harvard University Cambridge, MA 02138
| | - Paul Masset
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Molecular and Cellular Biology, Harvard University Cambridge, MA 02138
| | - William L Tong
- Center for Brain Science, Harvard University Cambridge, MA 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University Cambridge, MA 02138
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University Cambridge, MA 02138
| | - Joseph D Zak
- Department of Biological Sciences, University of Illinois at Chicago Chicago, IL 60607
| | - Venkatesh N Murthy
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Molecular and Cellular Biology, Harvard University Cambridge, MA 02138
| | - Cengiz Pehlevan
- Center for Brain Science, Harvard University Cambridge, MA 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University Cambridge, MA 02138
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University Cambridge, MA 02138
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Feng J, Xu J, Deng Y, Gao J. A Fechner multiscale local descriptor for face recognition. THE JOURNAL OF SUPERCOMPUTING 2023:1-28. [PMID: 37359343 PMCID: PMC10234800 DOI: 10.1007/s11227-023-05421-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/21/2023] [Indexed: 06/28/2023]
Abstract
Inspired by Fechner's law, we propose a Fechner multiscale local descriptor (FMLD) for feature extraction and face recognition. Fechner's law is a well-known law in psychology, which states that a human perception is proportional to the logarithm of the intensity of the corresponding significant differences physical quantity. FMLD uses the significant difference between pixels to simulate the pattern perception of human beings to the changes of surroundings. The first round of feature extraction is performed in two local domains of different sizes to capture the structural features of the facial images, resulting in four facial feature images. In the second round of feature extraction, two binary patterns are used to extract local features on the obtained magnitude and direction feature images, and four corresponding feature maps are output. Finally, all feature maps are fused to form an overall histogram feature. Different from the existing descriptors, the FMLD's magnitude and direction features are not isolated. They are derived from the "perceived intensity", thus there is a close relationship between them, which further facilitates the feature representation. In the experiments, we evaluated the performance of FMLD in multiple face databases and compared it with the leading edge approaches. The results show that the proposed FMLD performs well in recognizing images with illumination, pose, expression and occlusion changes. The results also indicate that the feature images produced by FMLD significantly improve the performance of convolutional neural network (CNN), and the combination of FMLD and CNN exhibits better performance than other advanced descriptors.
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Affiliation(s)
- Jinxiang Feng
- Guangdong University of Technology, Guangzhou, China
| | - Jie Xu
- Guangdong University of Technology, Guangzhou, China
- Guangzhou Maritime University, Guangzhou, China
| | - Yizhi Deng
- Guangdong University of Technology, Guangzhou, China
| | - Jun Gao
- Guangdong University of Technology, Guangzhou, China
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Jayaram V, Sehdev A, Kadakia N, Brown EA, Emonet T. Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments. PLoS Comput Biol 2023; 19:e1010606. [PMID: 37167321 DOI: 10.1371/journal.pcbi.1010606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 05/23/2023] [Accepted: 04/03/2023] [Indexed: 05/13/2023] Open
Abstract
To survive, insects must effectively navigate odors plumes to their source. In natural plumes, turbulent winds break up smooth odor regions into disconnected patches, so navigators encounter brief bursts of odor interrupted by bouts of clean air. The timing of these encounters plays a critical role in navigation, determining the direction, rate, and magnitude of insects' orientation and speed dynamics. Disambiguating the specific role of odor timing from other cues, such as spatial structure, is challenging due to natural correlations between plumes' temporal and spatial features. Here, we use optogenetics to isolate temporal features of odor signals, examining how the frequency and duration of odor encounters shape the navigational decisions of freely-walking Drosophila. We find that fly angular velocity depends on signal frequency and intermittency-the fraction of time signal can be detected-but not directly on durations. Rather than switching strategies when signal statistics change, flies smoothly transition between signal regimes, by combining an odor offset response with a frequency-dependent novelty-like response. In the latter, flies are more likely to turn in response to each odor hit only when the hits are sparse. Finally, the upwind bias of individual turns relies on a filtering scheme with two distinct timescales, allowing rapid and sustained responses in a variety of signal statistics. A quantitative model incorporating these ingredients recapitulates fly orientation dynamics across a wide range of environments and shows that temporal novelty detection, when combined with odor motion detection, enhances odor plume navigation.
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Affiliation(s)
- Viraaj Jayaram
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, United States of America
- Quantitative Biology Institute, Yale University, New Haven, Connecticut, United States of America
- Department of Physics, Yale University, New Haven, Connecticut, United States of America
| | - Aarti Sehdev
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, United States of America
- Quantitative Biology Institute, Yale University, New Haven, Connecticut, United States of America
| | - Nirag Kadakia
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, United States of America
- Quantitative Biology Institute, Yale University, New Haven, Connecticut, United States of America
- Swartz Foundation for Theoretical Neuroscience, Yale University, New Haven, Connecticut, United States of America
| | - Ethan A Brown
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, United States of America
- Quantitative Biology Institute, Yale University, New Haven, Connecticut, United States of America
- Yale College, Yale University, New Haven, Connecticut, United States of America
| | - Thierry Emonet
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, United States of America
- Quantitative Biology Institute, Yale University, New Haven, Connecticut, United States of America
- Department of Physics, Yale University, New Haven, Connecticut, United States of America
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, United States of America
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8
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Jayaram V, Kadakia N, Emonet T. Sensing complementary temporal features of odor signals enhances navigation of diverse turbulent plumes. eLife 2022; 11:e72415. [PMID: 35072625 PMCID: PMC8871351 DOI: 10.7554/elife.72415] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 01/20/2022] [Indexed: 11/13/2022] Open
Abstract
We and others have shown that during odor plume navigation, walking Drosophila melanogaster bias their motion upwind in response to both the frequency of their encounters with the odor (Demir et al., 2020) and the intermittency of the odor signal, which we define to be the fraction of time the signal is above a detection threshold (Alvarez-Salvado et al., 2018). Here, we combine and simplify previous mathematical models that recapitulated these data to investigate the benefits of sensing both of these temporal features and how these benefits depend on the spatiotemporal statistics of the odor plume. Through agent-based simulations, we find that navigators that only use frequency or intermittency perform well in some environments - achieving maximal performance when gains are near those inferred from experiment - but fail in others. Robust performance across diverse environments requires both temporal modalities. However, we also find a steep trade-off when using both sensors simultaneously, suggesting a strong benefit to modulating how much each sensor is weighted, rather than using both in a fixed combination across plumes. Finally, we show that the circuitry of the Drosophila olfactory periphery naturally enables simultaneous intermittency and frequency sensing, enhancing robust navigation through a diversity of odor environments. Together, our results suggest that the first stage of olfactory processing selects and encodes temporal features of odor signals critical to real-world navigation tasks.
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Affiliation(s)
- Viraaj Jayaram
- Department of Physics, Yale UniversityNew HavenUnited States
- Department of Molecular, Cellular and Developmental Biology, Yale UniversityNew HavenUnited States
- Quantitative Biology Institute, Yale UniversityNew HavenUnited States
| | - Nirag Kadakia
- Department of Molecular, Cellular and Developmental Biology, Yale UniversityNew HavenUnited States
- Quantitative Biology Institute, Yale UniversityNew HavenUnited States
| | - Thierry Emonet
- Department of Physics, Yale UniversityNew HavenUnited States
- Department of Molecular, Cellular and Developmental Biology, Yale UniversityNew HavenUnited States
- Quantitative Biology Institute, Yale UniversityNew HavenUnited States
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Singh V, Tchernookov M, Balasubramanian V. What the odor is not: Estimation by elimination. Phys Rev E 2021; 104:024415. [PMID: 34525542 PMCID: PMC8892575 DOI: 10.1103/physreve.104.024415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 08/02/2021] [Indexed: 11/07/2022]
Abstract
Olfactory systems use a small number of broadly sensitive receptors to combinatorially encode a vast number of odors. We propose a method of decoding such distributed representations by exploiting a statistical fact: Receptors that do not respond to an odor carry more information than receptors that do because they signal the absence of all odorants that bind to them. Thus, it is easier to identify what the odor is not rather than what the odor is. For realistic numbers of receptors, response functions, and odor complexity, this method of elimination turns an underconstrained decoding problem into a solvable one, allowing accurate determination of odorants in a mixture and their concentrations. We construct a neural network realization of our algorithm based on the structure of the olfactory pathway.
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Affiliation(s)
- Vijay Singh
- Department of Physics, North Carolina A&T State University, Greensboro, NC, 27410, USA
- Department of Physics, & Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Martin Tchernookov
- Department of Physics, University of Wisconsin, Whitewater, WI, 53190, USA
| | - Vijay Balasubramanian
- Department of Physics, & Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, PA 19104, USA
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10
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Abstract
The olfactory system translates chemical signals into neuronal signals that inform behavioral decisions of the animal. Odors are cues for source identity, but if monitored long enough, they can also be used to localize the source. Odor representations should therefore be robust to changing conditions and flexible in order to drive an appropriate behavior. In this review, we aim at discussing the main computations that allow robust and flexible encoding of odor information in the olfactory neural pathway.
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11
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Jafari S, Alenius M. Odor response adaptation in Drosophila-a continuous individualization process. Cell Tissue Res 2021; 383:143-148. [PMID: 33492517 PMCID: PMC7873105 DOI: 10.1007/s00441-020-03384-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/06/2020] [Indexed: 01/26/2023]
Abstract
Olfactory perception is very individualized in humans and also in Drosophila. The process that individualize olfaction is adaptation that across multiple time scales and mechanisms shape perception and olfactory-guided behaviors. Olfactory adaptation occurs both in the central nervous system and in the periphery. Central adaptation occurs at the level of the circuits that process olfactory inputs from the periphery where it can integrate inputs from other senses, metabolic states, and stress. We will here focus on the periphery and how the fast, slow, and persistent (lifelong) adaptation mechanisms in the olfactory sensory neurons individualize the Drosophila olfactory system.
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Affiliation(s)
- Shadi Jafari
- Department of Biology, New York University, New York, NY, USA
| | - Mattias Alenius
- Department of Molecular Biology, Umeå University, 901 87, Umeå, SE, Sweden.
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12
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Reply to Semelidou and Skoulakis: "Short-term" habituation has multiple distinct mechanisms. Proc Natl Acad Sci U S A 2020; 117:20373-20374. [PMID: 32843562 DOI: 10.1073/pnas.2013768117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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13
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Abstract
Habituation is a form of simple memory that suppresses neural activity in response to repeated, neutral stimuli. This process is critical in helping organisms guide attention toward the most salient and novel features in the environment. Here, we follow known circuit mechanisms in the fruit fly olfactory system to derive a simple algorithm for habituation. We show, both empirically and analytically, that this algorithm is able to filter out redundant information, enhance discrimination between odors that share a similar background, and improve detection of novel components in odor mixtures. Overall, we propose an algorithmic perspective on the biological mechanism of habituation and use this perspective to understand how sensory physiology can affect odor perception. Our framework may also help toward understanding the effects of habituation in other more sophisticated neural systems.
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14
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Qin S, Li Q, Tang C, Tu Y. Optimal compressed sensing strategies for an array of nonlinear olfactory receptor neurons with and without spontaneous activity. Proc Natl Acad Sci U S A 2019; 116:20286-20295. [PMID: 31548382 PMCID: PMC6789560 DOI: 10.1073/pnas.1906571116] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
There are numerous different odorant molecules in nature but only a relatively small number of olfactory receptor neurons (ORNs) in brains. This "compressed sensing" challenge is compounded by the constraint that ORNs are nonlinear sensors with a finite dynamic range. Here, we investigate possible optimal olfactory coding strategies by maximizing mutual information between odor mixtures and ORNs' responses with respect to the bipartite odor-receptor interaction network (ORIN) characterized by sensitivities between all odorant-ORN pairs. For ORNs without spontaneous (basal) activity, we find that the optimal ORIN is sparse-a finite fraction of sensitives are zero, and the nonzero sensitivities follow a broad distribution that depends on the odor statistics. We show analytically that sparsity in the optimal ORIN originates from a trade-off between the broad tuning of ORNs and possible interference. Furthermore, we show that the optimal ORIN enhances performances of downstream learning tasks (reconstruction and classification). For ORNs with a finite basal activity, we find that having inhibitory odor-receptor interactions increases the coding capacity and the fraction of inhibitory interactions increases with the ORN basal activity. We argue that basal activities in sensory receptors in different organisms are due to the trade-off between the increase in coding capacity and the cost of maintaining the spontaneous basal activity. Our theoretical findings are consistent with existing experiments and predictions are made to further test our theory. The optimal coding model provides a unifying framework to understand the peripheral olfactory systems across different organisms.
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Affiliation(s)
- Shanshan Qin
- Center for Quantitative Biology, Peking University, Beijing 100871, China
| | - Qianyi Li
- Integrated Science Program, Yuanpei College, Peking University, Beijing 100871, China
| | - Chao Tang
- Center for Quantitative Biology, Peking University, Beijing 100871, China;
- School of Physics, Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Yuhai Tu
- Physical Sciences Department, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598
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