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Joshi S, Haney S, Wang Z, Locatelli F, Cao Y, Smith B, Bazhenov M. Plasticity in inhibitory networks improves pattern separation in early olfactory processing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.24.576675. [PMID: 38328149 PMCID: PMC10849730 DOI: 10.1101/2024.01.24.576675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and changing ratios in complex mixtures. Changes in nectar production throughout the day and potentially many times within a forager's lifetime add to the complexity. The honeybee olfactory system, containing less than 1000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. Previous studies identified plasticity between AL neurons but its role in odor learning remains poorly understood. We used a computational network model and live imaging of the honeybee's AL to explore the neural mechanisms and functions of plasticity in the early olfactory system. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise neural code. Our Calcium imaging data support these predictions. Analysis of a Graph Convolutional Network in machine learning performing an odor categorization task revealed a similar mechanism of contrast enhancement. Our model provides insights into how inhibitory plasticity in the early olfactory network reshapes coding for efficient learning of complex odors. Significance Statement By combining computational modeling, machine learning, and analysis of calcium imaging data, we demonstrate that associative and non-associative plasticity in the honeybee antennal lobe (AL) - first relay of the insect olfactory system - work together to enhance the contrast between rewarded and unrewarded odors. Training the AL's inhibitory network within specific odor environments enables the suppression of neural responses to common odor components, while amplifying responses to distinctive ones. This study sheds light on the olfactory system's ability to adapt and efficiently learn new odor-reward associations across varying environments, and it proposes innovative, energy-efficient principles applicable to artificial intelligence.
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Kim B, Haney S, Milan AP, Joshi S, Aldworth Z, Rulkov N, Kim AT, Bazhenov M, Stopfer MA. Olfactory receptor neurons generate multiple response motifs, increasing coding space dimensionality. eLife 2023; 12:79152. [PMID: 36719272 PMCID: PMC9925048 DOI: 10.7554/elife.79152] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 01/31/2023] [Indexed: 02/01/2023] Open
Abstract
Odorants binding to olfactory receptor neurons (ORNs) trigger bursts of action potentials, providing the brain with its only experience of the olfactory environment. Our recordings made in vivo from locust ORNs showed that odor-elicited firing patterns comprise four distinct response motifs, each defined by a reliable temporal profile. Different odorants could elicit different response motifs from a given ORN, a property we term motif switching. Further, each motif undergoes its own form of sensory adaptation when activated by repeated plume-like odor pulses. A computational model constrained by our recordings revealed that organizing responses into multiple motifs provides substantial benefits for classifying odors and processing complex odor plumes: each motif contributes uniquely to encode the plume's composition and structure. Multiple motifs and motif switching further improve odor classification by expanding coding dimensionality. Our model demonstrated that these response features could provide benefits for olfactory navigation, including determining the distance to an odor source.
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Affiliation(s)
- Brian Kim
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)BethesdaUnited States
- Brown University - National Institutes of Health Graduate Partnership ProgramProvidenceUnited States
| | - Seth Haney
- Department of Medicine, University of California, San DiegoSan DiegoUnited States
| | - Ana P Milan
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Shruti Joshi
- Department of Medicine, University of California, San DiegoSan DiegoUnited States
| | - Zane Aldworth
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)BethesdaUnited States
| | - Nikolai Rulkov
- Biocircuits Institute, University of California, San DiegoLa JollaUnited States
| | - Alexander T Kim
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)BethesdaUnited States
| | - Maxim Bazhenov
- Department of Medicine, University of California, San DiegoSan DiegoUnited States
| | - Mark A Stopfer
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH)BethesdaUnited States
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Tavoni G, Kersen DEC, Balasubramanian V. Cortical feedback and gating in odor discrimination and generalization. PLoS Comput Biol 2021; 17:e1009479. [PMID: 34634035 PMCID: PMC8530364 DOI: 10.1371/journal.pcbi.1009479] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Revised: 10/21/2021] [Accepted: 09/24/2021] [Indexed: 11/30/2022] Open
Abstract
A central question in neuroscience is how context changes perception. In the olfactory system, for example, experiments show that task demands can drive divergence and convergence of cortical odor responses, likely underpinning olfactory discrimination and generalization. Here, we propose a simple statistical mechanism for this effect based on unstructured feedback from the central brain to the olfactory bulb, which represents the context associated with an odor, and sufficiently selective cortical gating of sensory inputs. Strikingly, the model predicts that both convergence and divergence of cortical odor patterns should increase when odors are initially more similar, an effect reported in recent experiments. The theory in turn predicts reversals of these trends following experimental manipulations and in neurological conditions that increase cortical excitability.
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Affiliation(s)
- Gaia Tavoni
- Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Neuroscience, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - David E. Chen Kersen
- Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Vijay Balasubramanian
- Computational Neuroscience Initiative, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Neuroscience, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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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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Rapp H, Nawrot MP. A spiking neural program for sensorimotor control during foraging in flying insects. Proc Natl Acad Sci U S A 2020; 117:28412-28421. [PMID: 33122439 PMCID: PMC7668073 DOI: 10.1073/pnas.2009821117] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles, we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning, and motor control. We focus on cast and surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule, the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast-and-surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.
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Affiliation(s)
- Hannes Rapp
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne 50674, Germany
| | - Martin Paul Nawrot
- Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne 50674, Germany
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Hu B, Wang JJ, Jin C. In vivo odorant input induces spike timing-dependent plasticity of glutamatergic synapses in developing zebrafish olfactory bulb. Biochem Biophys Res Commun 2020; 526:532-538. [PMID: 32245615 DOI: 10.1016/j.bbrc.2020.03.126] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2020] [Accepted: 03/21/2020] [Indexed: 01/24/2023]
Abstract
Early odorant experience and neural activity are essential for refining developing neural connections. Although neural activity-induced synaptic plasticity is one of the most important cellular mechanisms underlying the refinement of neural circuits, whether and how natural odorant experience induces long-term plasticity in the olfactory bulb remains unknown. In vivo perforated whole-cell recording from mitral cells (MCs) in larval zebrafish showed that odorant experience induced persistent modification of developing olfactory bulb circuits via spike timing-dependent plasticity (STDP). Repetitive odorant stimuli paired with postsynaptic spiking in a critical time window (pre-post, positive timing) resulted in persistent enhancement of glutamatergic inputs from olfactory sensory neurons, but long-term depression within the opposite time window (post-pre, negative timing). Furthermore, spike-timing-dependent potentiation (tLTP) in STDP induced by repetitive odorant stimulation had similar cellular processes to those of electrical stimulation-induced tLTP. Finally, odorant input induced STDP required the activation of postsynaptic N-methyl-d-aspartate receptors (NMDARs). Thus, the NMDAR is likely to be a postsynaptic coincidence detector responsible for the sensory experience-dependent refinement of developing connections.
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Affiliation(s)
- Bin Hu
- Research Center for Biochemistry and Molecular Biology, Jiangsu Key Laboratory of Brain Disease Bioinformation, Xuzhou Medical University, Jiangsu, 221004, China; Department of Neurobiology, School of Basic Medical Sciences, Nanjing Medical University, Jiangsu, 211166, China.
| | - Jing-Jing Wang
- Department of Neurobiology, School of Basic Medical Sciences, Nanjing Medical University, Jiangsu, 211166, China
| | - Chen Jin
- Research Center for Biochemistry and Molecular Biology, Jiangsu Key Laboratory of Brain Disease Bioinformation, Xuzhou Medical University, Jiangsu, 221004, China
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