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Windbuhler A, Okkesim S, Christ O, Mottaghi S, Rastogi S, Schmuker M, Baumann T, Hofmann UG. Machine Learning Approaches to Classify Anatomical Regions in Rodent Brain from High Density Recordings. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:3530-3533. [PMID: 36086280 DOI: 10.1109/embc48229.2022.9871702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Identifying different functional regions during a brain surgery is a challenging task usually performed by highly specialized neurophysiologists. Progress in this field may be used to improve in situ brain navigation and will serve as an important building block to minimize the number of animals in preclinical brain research required by properly positioning implants intraoperatively. The study at hand aims to correlate recorded extracellular signals with the volume of origin by deep learning methods. Our work establishes connections between the position in the brain and recorded high-density neural signals. This was achieved by evaluating the performance of BLSTM, BGRU, QRNN and CNN neural network architectures on multisite electrophysiological data sets. All networks were able to successfully distinguish cortical and thalamic brain regions according to their respective neural signals. The BGRU provides the best results with an accuracy of 88.6 % and demonstrates that this classification task might be solved in higher detail while minimizing complex preprocessing steps.
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2
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Burton SD, Brown A, Eiting TP, Youngstrom IA, Rust TC, Schmuker M, Wachowiak M. Mapping odorant sensitivities reveals a sparse but structured representation of olfactory chemical space by sensory input to the mouse olfactory bulb. eLife 2022; 11:80470. [PMID: 35861321 PMCID: PMC9352350 DOI: 10.7554/elife.80470] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 07/20/2022] [Indexed: 11/17/2022] Open
Abstract
In olfactory systems, convergence of sensory neurons onto glomeruli generates a map of odorant receptor identity. How glomerular maps relate to sensory space remains unclear. We sought to better characterize this relationship in the mouse olfactory system by defining glomeruli in terms of the odorants to which they are most sensitive. Using high-throughput odorant delivery and ultrasensitive imaging of sensory inputs, we imaged responses to 185 odorants presented at concentrations determined to activate only one or a few glomeruli across the dorsal olfactory bulb. The resulting datasets defined the tuning properties of glomeruli - and, by inference, their cognate odorant receptors - in a low-concentration regime, and yielded consensus maps of glomerular sensitivity across a wide range of chemical space. Glomeruli were extremely narrowly tuned, with ~25% responding to only one odorant, and extremely sensitive, responding to their effective odorants at sub-picomolar to nanomolar concentrations. Such narrow tuning in this concentration regime allowed for reliable functional identification of many glomeruli based on a single diagnostic odorant. At the same time, the response spectra of glomeruli responding to multiple odorants was best predicted by straightforward odorant structural features, and glomeruli sensitive to distinct odorants with common structural features were spatially clustered. These results define an underlying structure to the primary representation of sensory space by the mouse olfactory system.
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Affiliation(s)
- Shawn D Burton
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
| | - Audrey Brown
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
| | - Thomas P Eiting
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
| | - Isaac A Youngstrom
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
| | - Thomas C Rust
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
| | - Michael Schmuker
- Biocomputation Group, Centre of Data Innovation Research, Department of Computer Science, University of HertfordshireHertfordshireUnited Kingdom
| | - Matt Wachowiak
- Department of Neurobiology, University of Utah School of MedicineSalt Lake CityUnited States
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3
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Sinha A, Metzner C, Davey N, Adams R, Schmuker M, Steuber V. Growth rules for the repair of Asynchronous Irregular neuronal networks after peripheral lesions. PLoS Comput Biol 2021; 17:e1008996. [PMID: 34061830 PMCID: PMC8195387 DOI: 10.1371/journal.pcbi.1008996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 06/11/2021] [Accepted: 04/23/2021] [Indexed: 12/02/2022] Open
Abstract
Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model of peripheral lesioning and accurately reproduced the characteristics of network repair after deafferentation that are reported in experiments to study the activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we model deafferentation in a biologically realistic balanced network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex. Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed structural plasticity growth rules and the inhibitory synaptic plasticity mechanism that also balances our AI network both contribute to the restoration of the network to pre-deafferentation stable activity levels. An accumulating body of evidence suggests that our brain can compensate for peripheral lesions by adaptive rewiring of its neuronal circuitry. The underlying process, structural plasticity, can modify the connectivity of neuronal networks in the brain, thus affecting their function. To better understand the mechanisms of structural plasticity in the brain, we have developed a novel model of peripheral lesions and the resulting activity-dependent rewiring in a simplified balanced cortical network model that exhibits biologically realistic Asynchronous Irregular (AI) activity. In order to accurately reproduce the directionality and course of network rewiring after injury that is observed in peripheral lesion experiments, we derive activity dependent growth rules for different synaptic elements: dendritic and axonal contacts. Our simulation results suggest that excitatory and inhibitory synaptic elements have to react to changes in neuronal activity in opposite ways. We show that these rules result in a homeostatic stabilisation of activity in individual neurons. In our simulations, both synaptic and structural plasticity mechanisms contribute to network repair. Furthermore, our simulations indicate that while activity is restored in neurons deprived by the peripheral lesion, the temporal firing characteristics of the network may not be retained by the rewiring process.
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Affiliation(s)
- Ankur Sinha
- UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom
- * E-mail:
| | - Christoph Metzner
- UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
| | - Neil Davey
- UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom
| | - Roderick Adams
- UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom
| | - Michael Schmuker
- UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom
| | - Volker Steuber
- UH Biocomputation Research Group, Centre for Computer Science and Informatics Research, University of Hertfordshire, Hatfield United Kingdom
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4
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Tayarani-Najaran MH, Schmuker M. Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review. Front Neural Circuits 2021; 15:610446. [PMID: 34135736 PMCID: PMC8203204 DOI: 10.3389/fncir.2021.610446] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 04/27/2021] [Indexed: 11/13/2022] Open
Abstract
The nervous systems converts the physical quantities sensed by its primary receptors into trains of events that are then processed in the brain. The unmatched efficiency in information processing has long inspired engineers to seek brain-like approaches to sensing and signal processing. The key principle pursued in neuromorphic sensing is to shed the traditional approach of periodic sampling in favor of an event-driven scheme that mimicks sampling as it occurs in the nervous system, where events are preferably emitted upon the change of the sensed stimulus. In this paper we highlight the advantages and challenges of event-based sensing and signal processing in the visual, auditory and olfactory domains. We also provide a survey of the literature covering neuromorphic sensing and signal processing in all three modalities. Our aim is to facilitate research in event-based sensing and signal processing by providing a comprehensive overview of the research performed previously as well as highlighting conceptual advantages, current progress and future challenges in the field.
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Affiliation(s)
| | - Michael Schmuker
- School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom
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Schmuker M, Kupper R, Aertsen A, Wachtler T, Gewaltig MO. Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code. Biol Cybern 2021; 115:161-176. [PMID: 33787967 DOI: 10.1007/s00422-021-00866-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 01/31/2021] [Indexed: 06/12/2023]
Abstract
In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism's performance. We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neuronal networks where rapid responses and low spike counts are desired.
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Affiliation(s)
- Michael Schmuker
- Honda Research Institute Europe GmbH, Offenbach am Main, Germany.
- Department of Computer Science, Biocomputation Group, University of Hertfordshire, Hatfield, UK.
| | - Rüdiger Kupper
- Honda Research Institute Europe GmbH, Offenbach am Main, Germany
| | - Ad Aertsen
- Bernstein-Center Freiburg and Faculty of Biology, University of Freiburg, Freiburg im Breisgau, Germany
| | - Thomas Wachtler
- Department of Biology II, Ludwig-Maximilians-Universität München, München, Germany
| | - Marc-Oliver Gewaltig
- Honda Research Institute Europe GmbH, Offenbach am Main, Germany
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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6
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Abstract
Electronic olfaction can help detect and localize harmful gases and pollutants, but the turbulence of the natural environment presents a particular challenge: odor encounters are intermittent, and an effective electronic nose must therefore be able to resolve short odor pulses. The slow responses of the widely used metal oxide (MOX) gas sensors complicate the task. Here, we combine high-resolution data acquisition with a processing method based on Kalman filtering and absolute-deadband sampling to extract fast onset events. We find that our system can resolve the onset time of odor encounters with enough precision for source direction estimation with a pair of MOX sensors in a stereo-osmic configuration.
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Affiliation(s)
- Damien Drix
- Biocomputation group, Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, United Kingdom
| | - Michael Schmuker
- Biocomputation group, Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, United Kingdom
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7
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Parma V, Ohla K, Veldhuizen MG, Niv MY, Kelly CE, Bakke AJ, Cooper KW, Bouysset C, Pirastu N, Dibattista M, Kaur R, Liuzza MT, Pepino MY, Schöpf V, Pereda-Loth V, Olsson SB, Gerkin RC, Rohlfs Domínguez P, Albayay J, Farruggia MC, Bhutani S, Fjaeldstad AW, Kumar R, Menini A, Bensafi M, Sandell M, Konstantinidis I, Di Pizio A, Genovese F, Öztürk L, Thomas-Danguin T, Frasnelli J, Boesveldt S, Saatci Ö, Saraiva LR, Lin C, Golebiowski J, Hwang LD, Ozdener MH, Guàrdia MD, Laudamiel C, Ritchie M, Havlícek J, Pierron D, Roura E, Navarro M, Nolden AA, Lim J, Whitcroft KL, Colquitt LR, Ferdenzi C, Brindha EV, Altundag A, Macchi A, Nunez-Parra A, Patel ZM, Fiorucci S, Philpott CM, Smith BC, Lundström JN, Mucignat C, Parker JK, van den Brink M, Schmuker M, Fischmeister FPS, Heinbockel T, Shields VDC, Faraji F, Santamaría E, Fredborg WEA, Morini G, Olofsson JK, Jalessi M, Karni N, D'Errico A, Alizadeh R, Pellegrino R, Meyer P, Huart C, Chen B, Soler GM, Alwashahi MK, Welge-Lüssen A, Freiherr J, de Groot JHB, Klein H, Okamoto M, Singh PB, Hsieh JW, Reed DR, Hummel T, Munger SD, Hayes JE. Corrigendum to: More Than Smell-COVID-19 Is Associated With Severe Impairment of Smell, Taste, and Chemesthesis. Chem Senses 2021; 46:6457126. [PMID: 34879393 PMCID: PMC8689756 DOI: 10.1093/chemse/bjab050] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- Valentina Parma
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Kathrin Ohla
- Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, Germany
| | - Maria G Veldhuizen
- Department of Anatomy, Faculty of Medicine, Mersin University, Çiftlikköy Campus, Yenişehir, Mersin, Turkey
| | - Masha Y Niv
- Institute of Biochemistry, Food Science and Nutrition, The Hebrew University of Jerusalem, Rehovot, Israel
| | | | - Alyssa J Bakke
- Department of Food Science, The Pennsylvania State University, Erickson Food Science Building, University Park, PA, USA
| | - Keiland W Cooper
- Center for the Neurobiology of Learning and Memory, University of California and Qureshey Research Laboratory, Irvine, CA, USA
| | - Cédric Bouysset
- Institut de Chimie de Nice, UMR CNRS 7272, Université Côte d'Azur, Avenue Valrose, Nice, France
| | - Nicola Pirastu
- Centre for Global Health Research, Usher Institute, The University of Edinburgh, Old Medical School, Teviot Place, Edinburgh, UK
| | - Michele Dibattista
- Department of Basic Medical Sciences, Neuroscience and Sensory Organs, Università degli Studi di Bari A. Moro, P.zza G. Cesare, Bari, Italy
| | - Rishemjit Kaur
- CSIR-Central Scientific Instruments Organisation, Chandigarh, India
| | - Marco Tullio Liuzza
- Department of Medical and Surgical Sciences, "Magna Graecia" University of Catanzaro, Viale Europa (Loc. Germaneto), Catanzaro, Italy
| | - Marta Y Pepino
- Department of Food Science and Human Nutrition and Division of Nutritional Sciences, University of Illinois at Urbana Champaign, Urbana, IL, USA
| | - Veronika Schöpf
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel, Vienna, Austria
| | - Veronica Pereda-Loth
- Laboratoire d'Anthropologie Moléculaire et Imagerie de Synthese, UMR 5288 CNRS, Universitéde Toulouse, Toulouse, France
| | - Shannon B Olsson
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bengaluru, India
| | - Richard C Gerkin
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Paloma Rohlfs Domínguez
- Department of Psychology and Anthropology, University of Extremadura, Avenida de la Universidad, s/n, Cáceres, Spain
| | - Javier Albayay
- Department of General Psychology, University of Padova, Via Venezia, Padova, Italy
| | - Michael C Farruggia
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Surabhi Bhutani
- School of Exercise and Nutritional Sciences, 5500 Campanile Drive, San Diego State University, San Diego, CA, USA
| | - Alexander W Fjaeldstad
- Flavour Clinic, Department of Otorhinolaryngology, Regional Hospital West Jutland, Central Denmark Region, Laegaardvej, Holstebro, Denmark
| | - Ritesh Kumar
- Biocomputation Group, Department of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Anna Menini
- Neuroscience Area, International School for Advanced Studies, SISSA, Via Bonomea, Trieste, Italy
| | - Moustafa Bensafi
- Neuropop Team, Lyon Neuroscience Research Center, CNRS UMR5292-INSERM U1028-University Claude Bernard Lyon 1, 95 bd Pinel, Bron, France
| | - Mari Sandell
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland.,Functional Foods Forum, University of Turku, Turku, Finland
| | | | - Antonella Di Pizio
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Lise-Meitner-Str., Freising, Germany
| | | | - Lina Öztürk
- Department of Anatomy, Faculty of Medicine, Mersin University, Çiftlikköy Campus, Yenişehir, Mersin, Turkey
| | - Thierry Thomas-Danguin
- CSGA-Centre for Taste and Feeding Behavior, INRAE, CNRS, AgroSup Dijon, Université Bourgogne Franche-Comté, 17 rue Sully, Dijon, France
| | - Johannes Frasnelli
- Department of Anatomy, Université du Québec à Trois-Rivières, boul. des Forges, Trois-Rivières, QC, Canada
| | - Sanne Boesveldt
- Division of Human Nutrition and Health, Wageningen University, Stippeneng, WE Wageningen, the Netherlands
| | - Özlem Saatci
- Department of Otorhinolaryngology, Medical Science University, Emek, Sancaktepe-İstanbul, Turkey
| | - Luis R Saraiva
- Monell Chemical Senses Center, Philadelphia, PA, USA.,Sidra Medicine, Out Patient Clinic, Doha, Qatar
| | - Cailu Lin
- Monell Chemical Senses Center, Philadelphia, PA, USA
| | - Jérôme Golebiowski
- Institut de Chimie de Nice, UMR CNRS 7272, Université Côte d'Azur, Avenue Valrose, Nice, France
| | - Liang-Dar Hwang
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia
| | | | - Maria Dolors Guàrdia
- IRTA-Food Technology Programme, IRTA, Finca Camps i Armet, Monells, Girona, Spain
| | | | - Marina Ritchie
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Jan Havlícek
- Department of Zoology, Charles University, Viničná, Nové Město, Czechia
| | - Denis Pierron
- Équipe de Médecine Evolutive, UMR5288 CNRS/Université Toulouse III, faculté de chirurgie dentaire, 3 Chemin des Maraîchers, Toulouse, France
| | - Eugeni Roura
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Marta Navarro
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Alissa A Nolden
- Department of Food Science, University of Massachusetts, Holdsworth Way, Amherst, MA, USA
| | - Juyun Lim
- Department of Food Science and Technology, Oregon State University, Corvallis, OR, USA
| | | | | | - Camille Ferdenzi
- Neuropop Team, Lyon Neuroscience Research Center, CNRS UMR5292-INSERM U1028-University Claude Bernard Lyon 1, 95 bd Pinel, Bron, France
| | - Evelyn V Brindha
- Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, Tamilnadu, India
| | - Aytug Altundag
- Otorhinolaryngology Department, Biruni University, Protokol Yolu, Topkapı, Zeytinburnu, Istanbul, Turkey
| | - Alberto Macchi
- Italian Academy of Rhinology Asst Settelaghi-University of Insubriae, via Guicciardini, Varese, Italy
| | - Alexia Nunez-Parra
- Department of Biology, Universidad de Chile, Las Palmeras, Santiago, Chile
| | - Zara M Patel
- Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Sébastien Fiorucci
- Institut de Chimie de Nice, UMR CNRS 7272, Université Côte d'Azur, Avenue Valrose, Nice, France
| | - Carl M Philpott
- The Norfolk Smell and Taste Clinic, University of East Anglia, Norwich Research Park, Norwich, UK
| | - Barry C Smith
- Centre for the Study of the Senses, Institute of Philosophy, School of Advanced Study, University of London, London, UK
| | - Johan N Lundström
- Monell Chemical Senses Center, Philadelphia, PA, USA.,Department of Clinical Neuroscience, Karolinska Institutet, Nobels väg, Stockholm, Sweden
| | - Carla Mucignat
- Department of Molecular Medicine, University of Padova, via Marzolo, Padova, Italy
| | - Jane K Parker
- Department of Food and Nutritional Sciences, University of Reading, Whiteknights, Reading, UK
| | - Mirjam van den Brink
- Laboratory of Behavioural Gastronomy, Maastricht University Campus Venlo, Nassaustraat, BV Venlo, the Netherlands
| | - Michael Schmuker
- Biocomputation Group, Department of Computer Science, University of Hertfordshire, Hatfield, UK
| | | | - Thomas Heinbockel
- Department of Anatomy, College of Medicine, Howard University, N.W., Washington, DC, USA
| | - Vonnie D C Shields
- Biological Sciences Department, Fisher College of Science and Mathematics, Towson University, Towson, MD USA
| | - Farhoud Faraji
- Division of Otolaryngology, Head & Neck Surgery, University of California San Diego Health, MC La Jolla, CA, USA
| | - Enrique Santamaría
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IDISNA), Proteored-ISCIII, Pamplona, Spain
| | - William E A Fredborg
- Department of Psychology, Stockholm University, Frescativägen, Stockholm, Sweden
| | - Gabriella Morini
- University of Gastronomic Sciences, Piazza Vittorio Emanuele II 9, Bra, Pollenzo, CN, Italy
| | - Jonas K Olofsson
- Department of Psychology, Stockholm University, Frescativägen, Stockholm, Sweden
| | - Maryam Jalessi
- Skull Base Research Center, The Five Senses Institute, Iran University of Medical Sciences, Rasoul Akram Hospital, Sattarkhan Ave., Tehran, Iran
| | - Noam Karni
- Internal Medicine Department, Hadassah Medical Center, Kiryat Hadassah, Jerusalem, Israel
| | - Anna D'Errico
- Department of Molecular and Cellular Neurobiology, Goethe Universität Frankfurt, Goethe Universität Frankfurt, Max von Laue Strasse, Frankfurt am Main, Germany
| | - Rafieh Alizadeh
- ENT and Head and Neck Research Center and Department, Hazrat Rasoul Hospital, The Five Senses Institute, Iran University of Medical Sciences, Iran University of Medical Sciences, Shahid Hemmat Highway, Tehran, Iran
| | - Robert Pellegrino
- Food Science Department, University of Tennessee, Knoxville, TN, USA
| | - Pablo Meyer
- Health Care and Life Sciences, IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
| | - Caroline Huart
- Department of Otorhinolaryngology, Cliniques Universitaires Saint-Luc, Avenue Hippocrate, Brussels, Belgium
| | - Ben Chen
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Liwan District, Guangzhou City, China
| | - Graciela M Soler
- Department of Otorhinolaringology, Buenos Aires University and GEOG (Grupo de Estudio de Olfato y Gusto), Calle Paraguay, Piso 3. CABA (Ciudad Autónoma de Buenos Aires), Argentina
| | - Mohammed K Alwashahi
- Surgery Department, ENT Division, Sultan Qaboos University Hospital, Al Khoud, Muscat, Oman
| | - Antje Welge-Lüssen
- Department of Otorhinolaryngology, University Hospital Basel, Petersgraben, Basel, Switzerland
| | - Jessica Freiherr
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage, Erlangen, Germany
| | - Jasper H B de Groot
- Department of Psychology, Utrecht University, Heidelberglaan 1, CS Utrecht, The Netherlands
| | - Hadar Klein
- Institute of Biochemistry, Food Science and Nutrition, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Masako Okamoto
- Department of Applied Biological Chemistry, The University of Tokyo, Yayoi, Bunkyo-ku, Tokyo, Japan
| | - Preet Bano Singh
- Department of Oral Surgery and Oral Medicine, Faculty of Dentistry, University of Oslo, Blindern, Oslo, Norway
| | - Julien W Hsieh
- Rhinology-Olfactology Unit, ENT Department, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil, Geneva, Switzerland
| | | | | | - Thomas Hummel
- Department of Otorhinolaryngology, TU Dresden, Helmholtzstr., Dresden, Germany
| | - Steven D Munger
- Center for Smell and Taste, University of Florida, , Rm LG-101D, Gainesville, FL, USA.,Department of Pharmacology and Therapeutics, University of Florida, Gainesville, FL, USA
| | - John E Hayes
- Department of Food Science, The Pennsylvania State University, Erickson Food Science Building, University Park, PA, USA
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Parma V, Ohla K, Veldhuizen MG, Niv MY, Kelly CE, Bakke AJ, Cooper KW, Bouysset C, Pirastu N, Dibattista M, Kaur R, Liuzza MT, Pepino MY, Schöpf V, Pereda-Loth V, Olsson SB, Gerkin RC, Rohlfs Domínguez P, Albayay J, Farruggia MC, Bhutani S, Fjaeldstad AW, Kumar R, Menini A, Bensafi M, Sandell M, Konstantinidis I, Di Pizio A, Genovese F, Öztürk L, Thomas-Danguin T, Frasnelli J, Boesveldt S, Saatci Ö, Saraiva LR, Lin C, Golebiowski J, Hwang LD, Ozdener MH, Guàrdia MD, Laudamiel C, Ritchie M, Havlícek J, Pierron D, Roura E, Navarro M, Nolden AA, Lim J, Whitcroft KL, Colquitt LR, Ferdenzi C, Brindha EV, Altundag A, Macchi A, Nunez-Parra A, Patel ZM, Fiorucci S, Philpott CM, Smith BC, Lundström JN, Mucignat C, Parker JK, van den Brink M, Schmuker M, Fischmeister FPS, Heinbockel T, Shields VDC, Faraji F, Santamaría E, Fredborg WEA, Morini G, Olofsson JK, Jalessi M, Karni N, D'Errico A, Alizadeh R, Pellegrino R, Meyer P, Huart C, Chen B, Soler GM, Alwashahi MK, Welge-Lüssen A, Freiherr J, de Groot JHB, Klein H, Okamoto M, Singh PB, Hsieh JW, Reed DR, Hummel T, Munger SD, Hayes JE. More Than Smell-COVID-19 Is Associated With Severe Impairment of Smell, Taste, and Chemesthesis. Chem Senses 2020; 45:609-622. [PMID: 32564071 PMCID: PMC7337664 DOI: 10.1093/chemse/bjaa041] [Citation(s) in RCA: 300] [Impact Index Per Article: 75.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, and generally lacked quantitative measurements. Here, we report the development, implementation and initial results of a multi-lingual, international questionnaire to assess self-reported quantity and quality of perception in three distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, 8 other, ages 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change ±100) revealed a mean reduction of smell (-79.7 ± 28.7, mean ± SD), taste (-69.0 ± 32.6), and chemesthetic (-37.3 ± 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell, but also affects taste and chemesthesis. The multimodal impact of COVID-19 and lack of perceived nasal obstruction suggest that SARS-CoV-2 infection may disrupt sensory-neural mechanisms.
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Affiliation(s)
- Valentina Parma
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Kathrin Ohla
- Institute of Neuroscience and Medicine (INM-3), Research Center Jülich, Wilhelm-Johnen-Straße, Jülich, Germany
| | - Maria G Veldhuizen
- Department of Anatomy, Faculty of Medicine, Mersin University, Çiftlikköy Campus, Yenişehir, Mersin, Turkey
| | - Masha Y Niv
- Institute of Biochemistry, Food Science and Nutrition, The Hebrew University of Jerusalem, Rehovot, Israel
| | | | - Alyssa J Bakke
- Department of Food Science, The Pennsylvania State University, Erickson Food Science Building, University Park, PA, USA
| | - Keiland W Cooper
- Center for the Neurobiology of Learning and Memory, University of California and Qureshey Research Laboratory, Irvine, CA, USA
| | - Cédric Bouysset
- Institut de Chimie de Nice, UMR CNRS 7272, Université Côte d'Azur, Avenue Valrose, Nice, France
| | - Nicola Pirastu
- Centre for Global Health Research, Usher Institute, The University of Edinburgh, Old Medical School, Teviot Place, Edinburgh, UK
| | - Michele Dibattista
- Department of Basic Medical Sciences, Neuroscience and Sensory Organs, Università degli Studi di Bari A. Moro, P.zza G. Cesare, Bari, Italy
| | - Rishemjit Kaur
- CSIR-Central Scientific Instruments Organisation, Chandigarh, India
| | - Marco Tullio Liuzza
- Department of Medical and Surgical Sciences, "Magna Graecia" University of Catanzaro, Viale Europa (Loc. Germaneto), Catanzaro, Italy
| | - Marta Y Pepino
- Department of Food Science and Human Nutrition and Division of Nutritional Sciences, University of Illinois at Urbana Champaign, Urbana, IL, USA
| | - Veronika Schöpf
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Währinger Gürtel, Vienna, Austria
| | - Veronica Pereda-Loth
- Laboratoire d'Anthropologie Moléculaire et Imagerie de Synthese, UMR 5288 CNRS, Universitéde Toulouse, Toulouse, France
| | - Shannon B Olsson
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK Campus, Bengaluru, India
| | - Richard C Gerkin
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
| | - Paloma Rohlfs Domínguez
- Department of Psychology and Anthropology, University of Extremadura, Avenida de la Universidad, s/n, Cáceres, Spain
| | - Javier Albayay
- Department of General Psychology, University of Padova, Via Venezia, Padova, Italy
| | - Michael C Farruggia
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Surabhi Bhutani
- School of Exercise and Nutritional Sciences, 5500 Campanile Drive, San Diego State University, San Diego, CA, USA
| | - Alexander W Fjaeldstad
- Flavour Clinic, Department of Otorhinolaryngology, Regional Hospital West Jutland, Central Denmark Region, Laegaardvej, Holstebro, Denmark
| | - Ritesh Kumar
- Biocomputation Group, Department of Computer Science, University of Hertfordshire, Hatfield, UK
| | - Anna Menini
- Neuroscience Area, International School for Advanced Studies, SISSA, Via Bonomea, Trieste, Italy
| | - Moustafa Bensafi
- Neuropop Team, Lyon Neuroscience Research Center, CNRS UMR5292-INSERM U1028-University Claude Bernard Lyon 1, 95 bd Pinel, Bron, France
| | - Mari Sandell
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland.,Functional Foods Forum, University of Turku, Turku, Finland
| | | | - Antonella Di Pizio
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Lise-Meitner-Str., Freising, Germany
| | | | - Lina Öztürk
- Department of Anatomy, Faculty of Medicine, Mersin University, Çiftlikköy Campus, Yenişehir, Mersin, Turkey
| | - Thierry Thomas-Danguin
- CSGA-Centre for Taste and Feeding Behavior, INRAE, CNRS, AgroSup Dijon, Université Bourgogne Franche-Comté, 17 rue Sully, Dijon, France
| | - Johannes Frasnelli
- Department of Anatomy, Université du Québec à Trois-Rivières, boul. des Forges, Trois-Rivières, QC, Canada
| | - Sanne Boesveldt
- Division of Human Nutrition and Health, Wageningen University, Stippeneng, WE Wageningen, the Netherlands
| | - Özlem Saatci
- Department of Otorhinolaryngology, Medical Science University, Emek, Sancaktepe-İstanbul, Turkey
| | - Luis R Saraiva
- Monell Chemical Senses Center, Philadelphia, PA, USA.,Sidra Medicine, Out Patient Clinic, Doha, Qatar
| | - Cailu Lin
- Monell Chemical Senses Center, Philadelphia, PA, USA
| | - Jérôme Golebiowski
- Institut de Chimie de Nice, UMR CNRS 7272, Université Côte d'Azur, Avenue Valrose, Nice, France
| | - Liang-Dar Hwang
- The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia
| | | | - Maria Dolors Guàrdia
- IRTA-Food Technology Programme, IRTA, Finca Camps i Armet, Monells, Girona, Spain
| | | | - Marina Ritchie
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Jan Havlícek
- Department of Zoology, Charles University, Viničná, Nové Město, Czechia
| | - Denis Pierron
- Équipe de Médecine Evolutive, UMR5288 CNRS/Université Toulouse III, faculté de chirurgie dentaire, 3 Chemin des Maraîchers, Toulouse, France
| | - Eugeni Roura
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Marta Navarro
- Centre for Nutrition and Food Sciences, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD, Australia
| | - Alissa A Nolden
- Department of Food Science, University of Massachusetts, Holdsworth Way, Amherst, MA, USA
| | - Juyun Lim
- Department of Food Science and Technology, Oregon State University, Corvallis, OR, USA
| | | | | | - Camille Ferdenzi
- Neuropop Team, Lyon Neuroscience Research Center, CNRS UMR5292-INSERM U1028-University Claude Bernard Lyon 1, 95 bd Pinel, Bron, France
| | - Evelyn V Brindha
- Department of Electrical and Electronics Engineering, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore, Tamilnadu, India
| | - Aytug Altundag
- Otorhinolaryngology Department, Biruni University, Protokol Yolu, Topkapı, Zeytinburnu, Istanbul, Turkey
| | - Alberto Macchi
- Italian Academy of Rhinology Asst Settelaghi-University of Insubriae, via Guicciardini, Varese, Italy
| | - Alexia Nunez-Parra
- Department of Biology, Universidad de Chile, Las Palmeras, Santiago, Chile
| | - Zara M Patel
- Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Sébastien Fiorucci
- Institut de Chimie de Nice, UMR CNRS 7272, Université Côte d'Azur, Avenue Valrose, Nice, France
| | - Carl M Philpott
- The Norfolk Smell and Taste Clinic, University of East Anglia, Norwich Research Park, Norwich, UK
| | - Barry C Smith
- Centre for the Study of the Senses, Institute of Philosophy, School of Advanced Study, University of London, London, UK
| | - Johan N Lundström
- Monell Chemical Senses Center, Philadelphia, PA, USA.,Department of Clinical Neuroscience, Karolinska Institutet, Nobels väg, Stockholm, Sweden
| | - Carla Mucignat
- Department of Molecular Medicine, University of Padova, via Marzolo, Padova, Italy
| | - Jane K Parker
- Department of Food and Nutritional Sciences, University of Reading, Whiteknights, Reading, UK
| | - Mirjam van den Brink
- Laboratory of Behavioural Gastronomy, Maastricht University Campus Venlo, Nassaustraat, BV Venlo, the Netherlands
| | - Michael Schmuker
- Biocomputation Group, Department of Computer Science, University of Hertfordshire, Hatfield, UK
| | | | - Thomas Heinbockel
- Department of Anatomy, College of Medicine, Howard University, N.W., Washington, DC, USA
| | - Vonnie D C Shields
- Biological Sciences Department, Fisher College of Science and Mathematics, Towson University, Towson, MD USA
| | - Farhoud Faraji
- Division of Otolaryngology, Head & Neck Surgery, University of California San Diego Health, MC La Jolla, CA, USA
| | - Enrique Santamaría
- Clinical Neuroproteomics Unit, Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad pública de Navarra (UPNA), Instituto de Investigación Sanitaria de Navarra (IDISNA), Proteored-ISCIII, Pamplona, Spain
| | - William E A Fredborg
- Department of Psychology, Stockholm University, Frescativägen, Stockholm, Sweden
| | - Gabriella Morini
- University of Gastronomic Sciences, Piazza Vittorio Emanuele II 9, Bra, Pollenzo, CN, Italy
| | - Jonas K Olofsson
- Department of Psychology, Stockholm University, Frescativägen, Stockholm, Sweden
| | - Maryam Jalessi
- Skull Base Research Center, The Five Senses Institute, Iran University of Medical Sciences, Rasoul Akram Hospital, Sattarkhan Ave., Tehran, Iran
| | - Noam Karni
- Internal Medicine Department, Hadassah Medical Center, Kiryat Hadassah, Jerusalem, Israel
| | - Anna D'Errico
- Department of Molecular and Cellular Neurobiology, Goethe Universität Frankfurt, Goethe Universität Frankfurt, Max von Laue Strasse, Frankfurt am Main, Germany
| | - Rafieh Alizadeh
- ENT and Head and Neck Research Center and Department, Hazrat Rasoul Hospital, The Five Senses Institute, Iran University of Medical Sciences, Iran University of Medical Sciences, Shahid Hemmat Highway, Tehran, Iran
| | - Robert Pellegrino
- Food Science Department, University of Tennessee, Knoxville, TN, USA
| | - Pablo Meyer
- Health Care and Life Sciences, IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
| | - Caroline Huart
- Department of Otorhinolaryngology, Cliniques Universitaires Saint-Luc, Avenue Hippocrate, Brussels, Belgium
| | - Ben Chen
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Liwan District, Guangzhou City, China
| | - Graciela M Soler
- Department of Otorhinolaringology, Buenos Aires University and GEOG (Grupo de Estudio de Olfato y Gusto), Calle Paraguay, Piso 3. CABA (Ciudad Autónoma de Buenos Aires), Argentina
| | - Mohammed K Alwashahi
- Surgery Department, ENT Division, Sultan Qaboos University Hospital, Al Khoud, Muscat, Oman
| | - Antje Welge-Lüssen
- Department of Otorhinolaryngology, University Hospital Basel, Petersgraben, Basel, Switzerland
| | - Jessica Freiherr
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage, Erlangen, Germany
| | - Jasper H B de Groot
- Department of Psychology, Utrecht University, Heidelberglaan 1, CS Utrecht, The Netherlands
| | - Hadar Klein
- Institute of Biochemistry, Food Science and Nutrition, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Masako Okamoto
- Department of Applied Biological Chemistry, The University of Tokyo, Yayoi, Bunkyo-ku, Tokyo, Japan
| | - Preet Bano Singh
- Department of Oral Surgery and Oral Medicine, Faculty of Dentistry, University of Oslo, Blindern, Oslo, Norway
| | - Julien W Hsieh
- Rhinology-Olfactology Unit, ENT Department, Geneva University Hospitals, Rue Gabrielle-Perret-Gentil, Geneva, Switzerland
| | | | | | - Thomas Hummel
- Department of Otorhinolaryngology, TU Dresden, Helmholtzstr., Dresden, Germany
| | - Steven D Munger
- Center for Smell and Taste, University of Florida, , Rm LG-101D, Gainesville, FL, USA.,Department of Pharmacology and Therapeutics, University of Florida, Gainesville, FL, USA
| | - John E Hayes
- Department of Food Science, The Pennsylvania State University, Erickson Food Science Building, University Park, PA, USA
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Drix D, Hafner VV, Schmuker M. Sparse coding with a somato-dendritic rule. Neural Netw 2020; 131:37-49. [PMID: 32750603 DOI: 10.1016/j.neunet.2020.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 04/30/2020] [Accepted: 06/04/2020] [Indexed: 10/24/2022]
Abstract
Cortical neurons are silent most of the time: sparse activity enables low-energy computation in the brain, and promises to do the same in neuromorphic hardware. Beyond power efficiency, sparse codes have favourable properties for associative learning, as they can store more information than local codes but are easier to read out than dense codes. Auto-encoders with a sparse constraint can learn sparse codes, and so can single-layer networks that combine recurrent inhibition with unsupervised Hebbian learning. But the latter usually require fast homeostatic plasticity, which could lead to catastrophic forgetting in embodied agents that learn continuously. Here we set out to explore whether plasticity at recurrent inhibitory synapses could take up that role instead, regulating both the population sparseness and the firing rates of individual neurons. We put the idea to the test in a network that employs compartmentalised inputs to solve the task: rate-based dendritic compartments integrate the feedforward input, while spiking integrate-and-fire somas compete through recurrent inhibition. A somato-dendritic learning rule allows somatic inhibition to modulate nonlinear Hebbian learning in the dendrites. Trained on MNIST digits and natural images, the network discovers independent components that form a sparse encoding of the input and support linear decoding. These findings confirm that intrinsic homeostatic plasticity is not strictly required for regulating sparseness: inhibitory synaptic plasticity can have the same effect. Our work illustrates the usefulness of compartmentalised inputs, and makes the case for moving beyond point neuron models in artificial spiking neural networks.
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Affiliation(s)
- Damien Drix
- Biocomputation group, Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Adaptive Systems laboratory, Institut für Informatik, Humboldt-Universität zu Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Verena V Hafner
- Adaptive Systems laboratory, Institut für Informatik, Humboldt-Universität zu Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Michael Schmuker
- Biocomputation group, Department of Computer Science, University of Hertfordshire, Hatfield, United Kingdom; Bernstein Center for Computational Neuroscience, Berlin, Germany
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10
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Soelter J, Schumacher J, Spors H, Schmuker M. Computational exploration of molecular receptive fields in the olfactory bulb reveals a glomerulus-centric chemical map. Sci Rep 2020; 10:77. [PMID: 31919393 PMCID: PMC6952415 DOI: 10.1038/s41598-019-56863-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Accepted: 09/24/2019] [Indexed: 01/13/2023] Open
Abstract
Progress in olfactory research is currently hampered by incomplete knowledge about chemical receptive ranges of primary receptors. Moreover, the chemical logic underlying the arrangement of computational units in the olfactory bulb has still not been resolved. We undertook a large-scale approach at characterising molecular receptive ranges (MRRs) of glomeruli in the dorsal olfactory bulb (dOB) innervated by the MOR18-2 olfactory receptor, also known as Olfr78, with human ortholog OR51E2. Guided by an iterative approach that combined biological screening and machine learning, we selected 214 odorants to characterise the response of MOR18-2 and its neighbouring glomeruli. We found that a combination of conventional physico-chemical and vibrational molecular descriptors performed best in predicting glomerular responses using nonlinear Support-Vector Regression. We also discovered several previously unknown odorants activating MOR18-2 glomeruli, and obtained detailed MRRs of MOR18-2 glomeruli and their neighbours. Our results confirm earlier findings that demonstrated tunotopy, that is, glomeruli with similar tuning curves tend to be located in spatial proximity in the dOB. In addition, our results indicate chemotopy, that is, a preference for glomeruli with similar physico-chemical MRR descriptions being located in spatial proximity. Together, these findings suggest the existence of a partial chemical map underlying glomerular arrangement in the dOB. Our methodology that combines machine learning and physiological measurements lights the way towards future high-throughput studies to deorphanise and characterise structure-activity relationships in olfaction.
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Affiliation(s)
- Jan Soelter
- Neuroinformatics & Theoretical Neuroscience, Freie Universität Berlin, Königin-Luise-Str. 1-3, 14195, Berlin, Germany
| | - Jan Schumacher
- Max-Planck-Institute for Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt/Main, Germany
| | - Hartwig Spors
- Max-Planck-Institute for Biophysics, Max-von-Laue-Str. 3, 60438, Frankfurt/Main, Germany
- Department of Neuropediatrics, Max-Liebig-University, Giessen, Germany
| | - Michael Schmuker
- Neuroinformatics & Theoretical Neuroscience, Freie Universität Berlin, Königin-Luise-Str. 1-3, 14195, Berlin, Germany.
- Biocomputation Group, University of Hertfordshire, Hatfield, AL10 9AB, United Kingdom.
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Abstract
Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, highly parallel operation of brains. However, to use these systems in applications, we need "neuromorphic algorithms" that can run on them. Here we develop a spiking neural network model for neuromorphic hardware that uses spike timing-dependent plasticity and lateral inhibition to perform unsupervised clustering. With this model, time-invariant, rate-coded datasets can be mapped into a feature space with a specified resolution, i.e., number of clusters, using exclusively neuromorphic hardware. We developed and tested implementations on the SpiNNaker neuromorphic system and on GPUs using the GeNN framework. We show that our neuromorphic clustering algorithm achieves results comparable to those of conventional clustering algorithms such as self-organizing maps, neural gas or k-means clustering. We then combine it with a previously reported supervised neuromorphic classifier network to demonstrate its practical use as a neuromorphic preprocessing module.
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Affiliation(s)
- Alan Diamond
- School of Engineering and Informatics, University of Sussex, Falmer, Brighton, BN1 9QJ UK
| | - Michael Schmuker
- Department of Computer Science, University of Hertfordshire Hatfield, Hertfordshire, AL10 9AB UK
| | - Thomas Nowotny
- School of Engineering and Informatics, University of Sussex, Falmer, Brighton, BN1 9QJ UK
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Newton AJH, Seidenstein AH, McDougal RA, Pérez-Cervera A, Huguet G, M-Seara T, Haimerl C, Angulo-Garcia D, Torcini A, Cossart R, Malvache A, Skiker K, Maouene M, Ragognetti G, Lorusso L, Viggiano A, Marcelli A, Senatore R, Parziale A, Stramaglia S, Pellicoro M, Angelini L, Amico E, Aerts H, Cortés J, Laureys S, Marinazzo D, Stramaglia S, Bassez I, Faes L, Almgren H, Razi A, Van de Steen F, Krebs R, Aerts H, Kanari L, Dlotko P, Scolamiero M, Levi R, Shillcock J, de Kock CP, Hess K, Markram H, Ly C, Marsat G, Gillespie T, Sandström M, Abrams M, Grethe JS, Martone M, De Gernier R, Solinas S, Rössert C, Haelterman M, Massar S, Pasquale V, Pastore VP, Martinoia S, Massobrio P, Capone C, Tort-Colet N, Sanchez-Vives MV, Mattia M, Almasi A, Cloherty SL, Grayden DB, Wong YT, Ibbotson MR, Meffin H, Prince LY, Tsaneva-Atanasova K, Mellor JR, Mazzoni A, Rosa M, Carpaneto J, Romito LM, Priori A, Micera S, Migliore R, Lupascu CA, Franchina F, Bologna LL, Romani A, Saray S, Van Geit W, Káli S, Thomson A, Mercer A, Lange S, Falck J, Muller E, Schürmann F, Todorov D, Capps R, Barnett W, Molkov Y, Devalle F, Pazó D, Montbrió E, Mochol G, Azab H, Hayden BY, Moreno-Bote R, Balasubramani PP, Chakravarthy SV, Muddapu VR, Gheorghiu MD, Mimica B, Withlock J, Mureșan RC, Zick JL, Schultz K, Blackman RK, Chafee MV, Netoff TI, Roberts N, Nagaraj V, Lamperski A, Netoff TI, Grado LL, Johnson MD, Darrow DP, Lonardoni D, Amin H, Di Marco S, Maccione A, Berdondini L, Nieus T, Stimberg M, Goodman DFM, Nowotny T, Koren V, Dragoi V, Obermayer K, Castro S, Fernandez M, El-Deredy W, Xu K, Maidana JP, Orio P, Chen W, Hepburn I, Casalegno F, Devresse A, Ovcharenko A, Pereira F, Delalondre F, De Schutter E, Bratby P, Gallimore AR, Klingbeil G, Zamora C, Zang Y, Crotty P, Palmerduca E, Antonietti A, Casellato C, Erö C, D’Angelo E, Gewaltig MO, Pedrocchi A, Bytschok I, Dold D, Schemmel J, Meier K, Petrovici MA, Shen HA, Surace SC, Pfister JP, Lefebvre B, Marre O, Yger P, Papoutsi A, Park J, Ash R, Smirnakis S, Poirazi P, Felix RA, Dimitrov AG, Portfors C, Daun S, Toth TI, Jędrzejewska-Szmek J, Kabbani N, Blackwel KT, Moezzi B, Schaworonkow N, Plogmacher L, Goldsworthy MR, Hordacre B, McDonnell MD, Iannella N, Ridding MC, Triesch J, Maex R, Safaryan K, Steuber V, Tang R, Tang YY, Verveyko DV, Brazhe AR, Verisokin AY, Postnov DE, Günay C, Panuccio G, Giugliano M, Prinz AA, Varona P, Rabinovich MI, Denham J, Ranner T, Cohen N, Reva M, Rebola N, Kirizs T, Nusser Z, DiGregorio D, Mavritsaki E, Rentzelas P, Ukani NH, Tomkins A, Yeh CH, Bruning W, Fenichel AL, Zhou Y, Huang YC, Florescu D, Ortiz CL, Richmond P, Lo CC, Coca D, Chiang AS, Lazar AA, Moezzi B, Creaser JL, Lin C, Ashwin P, Brown JT, Ridler T, Levenstein D, Watson BO, Buzsáki G, Rinzel J, Curtu R, Nguyen A, Assadzadeh S, Robinson PA, Sanz-Leon P, Forlim CG, de Almeida LOB, Pinto RD, Rodríguez FB, Lareo Á, Forlim CG, Rodríguez FB, Montero A, Mosqueiro T, Huerta R, Rodriguez FB, Changoluisa V, Rodriguez FB, Cordeiro VL, Ceballos CC, Kamiji NL, Roque AC, Lytton WW, Knox A, Rosenthal JJC, Daun S, Popovych S, Liu L, Wang BA, Tóth TI, Grefkes C, Fink GR, Rosjat N, Perez-Trujillo A, Espinal A, Sotelo-Figueroa MA, Cruz-Aceves I, Rostro-Gonzalez H, Zapotocky M, Hoskovcová M, Kopecká J, Ulmanová O, Růžička E, Gärtner M, Duvarci S, Roeper J, Schneider G, Albert S, Schmack K, Remme M, Schreiber S, Migliore M, Lupascu CA, Bologna LL, Antonel SM, Courcol JD, Schürmann F, Çelikok SU, Navarro-López EM, Şengör NS, Elibol R, Sengor NS, Özdemir MY, Li T, Arleo A, Sheynikhovich D, Nakamura A, Shimono M, Song Y, Park S, Choi I, Jeong J, Shin HS, Sadeh S, Gleeson P, Angus Silver R, Chatzikalymniou AP, Skinner FK, Sanchez-Rodriguez LM, Sotero RC, Hertäg L, Mackwood O, Sprekeler H, Puhlmann S, Weber SN, Higgins D, Naumann LB, Weber SN, Iyer R, Mihalas S, Ticcinelli V, Stankovski T, McClintock PVE, Stefanovska A, Janjić P, Solev D, Seifert G, Kocarev L, Steinhäuser C, Salmasi M, Glasauer S, Stemmler M, Zhang D, Zhang C, Stepanyants A, Goncharenko J, Kros L, Davey N, de Zeeuw C, Hoebeek F, Sinha A, Adams R, Schmuker M, Psarrou M, Schilstra M, Torben-Nielsen B, Metzner C, Schweikard A, Mäki-Marttunen T, Zurowski B, Marinazzo D, Faes L, Stramaglia S, Jordan HOC, Stringer SM, Gajewska-Dendek E, Suffczyński P, Tam N, Zouridakis G, Pollonini L, Tang YY, Asl MM, Valizadeh A, Tass PA, Nold A, Fan W, Konrad S, Endle H, Vogt J, Tchumatchenko T, Herpich J, Tetzlaff C, Luboeinski J, Nachstedt T, Ciba M, Bahmer A, Thielemann C, Kuebler ES, Tauskela JS, Thivierge JP, Bakker R, García-Amado M, Evangelio M, Clascá F, Tiesinga P, Buckley CL, Toyoizumi T, Dubreuil AM, Monasson R, Treves A, Spalla D, Rosay S, Kleberg FI, Wong W, de Oliveira Floriano B, Matsuo T, Uchida T, Dibenedetto D, Uludağ K, Goodarzinick A, Schmidt M, Hilgetag CC, Diesmann M, van Albada SJ, Fauth M, van Rossum M, Reyes-Sánchez M, Amaducci R, Muñiz C, Varona P, Elices I, Arroyo D, Levi R, Cohen B, Chow C, Vattikuti S, Bertolotti E, Burioni R, di Volo M, Vezzani A, Menzat B, Vogels TP, Wagatsuma N, Saha S, Kapoor R, Kerr R, Wagner J, del Molino LCG, Yang GR, Mejias JF, Wang XJ, Song H, Goodliffe J, Luebke J, Weaver CM, Thomas J, Sinha N, Shaju N, Maszczyk T, Jin J, Cash SS, Dauwels J, Brandon Westover M, Karimian M, Moerel M, De Weerd P, Burwick T, Westra RL, Abeysuriya R, Hadida J, Sotiropoulos S, Jbabdi S, Woolrich M, Bensmail C, Wrobel B, Zhou X, Ji Z, Liu X, Xia Y, Wu S, Wang X, Zhang M, Wu S, Ofer N, Shefi O, Yaari G, Carnevale T, Majumdar A, Sivagnanam S, Yoshimoto K, Smirnova EY, Amakhin DV, Malkin SL, Zaitsev AV, Chizhov AV, Zaleshina M, Zaleshin A, Barranca VJ, Zhu G, Skilling QM, Maruyama D, Ognjanovski N, Aton SJ, Zochowski M, Wu J, Aton S, Rich S, Booth V, Budak M, Dura-Bernal S, Neymotin SA, Suter BA, Shepherd GMG, Felton MA, Yu AB, Boothe DL, Oie KS, Franaszczuk PJ, Shuvaev SA, Başerdem B, Zador A, Koulakov AA, López-Madrona VJ, Pereda E, Mirasso CR, Canals S, Masoli S, Rongala UB, Mazzoni A, Spanne A, Jorntell H, Oddo CM, Vartanov AV, Neklyudova AK, Kozlovskiy SA, Kiselnikov AA, Marakshina JA, Teleńczuk M, Teleńczuk B, Destexhe A, Kuokkanen PT, Kraemer A, McColgan T, Carr CE, Kempter R. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3. BMC Neurosci 2017. [PMCID: PMC5592441 DOI: 10.1186/s12868-017-0372-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Diamond A, Schmuker M, Berna AZ, Trowell S, Nowotny T. Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system. Bioinspir Biomim 2016; 11:026002. [PMID: 26891474 DOI: 10.1088/1748-3190/11/2/026002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In many application domains, conventional e-noses are frequently outperformed in both speed and accuracy by their biological counterparts. Exploring potential bio-inspired improvements, we note a number of neuronal network models have demonstrated some success in classifying static datasets by abstracting the insect olfactory system. However, these designs remain largely unproven in practical settings, where sensor data is real-time, continuous, potentially noisy, lacks a precise onset signal and accurate classification requires the inclusion of temporal aspects into the feature set. This investigation therefore seeks to inform and develop the potential and suitability of biomimetic classifiers for use with typical real-world sensor data. Taking a generic classifier design inspired by the inhibition and competition in the insect antennal lobe, we apply it to identifying 20 individual chemical odours from the timeseries of responses of metal oxide sensors. We show that four out of twelve available sensors and the first 30 s (10%) of the sensors' continuous response are sufficient to deliver 92% accurate classification without access to an odour onset signal. In contrast to previous approaches, once training is complete, sensor signals can be fed continuously into the classifier without requiring discretization. We conclude that for continuous data there may be a conceptual advantage in using spiking networks, in particular where time is an essential component of computation. Classification was achieved in real time using a GPU-accelerated spiking neural network simulator developed in our group.
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Affiliation(s)
- A Diamond
- School Of Engineering and Informatics, University of Sussex, UK
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Diamond A, Nowotny T, Schmuker M. Comparing Neuromorphic Solutions in Action: Implementing a Bio-Inspired Solution to a Benchmark Classification Task on Three Parallel-Computing Platforms. Front Neurosci 2016; 9:491. [PMID: 26778950 PMCID: PMC4705229 DOI: 10.3389/fnins.2015.00491] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Accepted: 12/10/2015] [Indexed: 01/24/2023] Open
Abstract
Neuromorphic computing employs models of neuronal circuits to solve computing problems. Neuromorphic hardware systems are now becoming more widely available and "neuromorphic algorithms" are being developed. As they are maturing toward deployment in general research environments, it becomes important to assess and compare them in the context of the applications they are meant to solve. This should encompass not just task performance, but also ease of implementation, speed of processing, scalability, and power efficiency. Here, we report our practical experience of implementing a bio-inspired, spiking network for multivariate classification on three different platforms: the hybrid digital/analog Spikey system, the digital spike-based SpiNNaker system, and GeNN, a meta-compiler for parallel GPU hardware. We assess performance using a standard hand-written digit classification task. We found that whilst a different implementation approach was required for each platform, classification performances remained in line. This suggests that all three implementations were able to exercise the model's ability to solve the task rather than exposing inherent platform limits, although differences emerged when capacity was approached. With respect to execution speed and power consumption, we found that for each platform a large fraction of the computing time was spent outside of the neuromorphic device, on the host machine. Time was spent in a range of combinations of preparing the model, encoding suitable input spiking data, shifting data, and decoding spike-encoded results. This is also where a large proportion of the total power was consumed, most markedly for the SpiNNaker and Spikey systems. We conclude that the simulation efficiency advantage of the assessed specialized hardware systems is easily lost in excessive host-device communication, or non-neuronal parts of the computation. These results emphasize the need to optimize the host-device communication architecture for scalability, maximum throughput, and minimum latency. Moreover, our results indicate that special attention should be paid to minimize host-device communication when designing and implementing networks for efficient neuromorphic computing.
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Affiliation(s)
- Alan Diamond
- School of Engineering and Informatics, University of SussexBrighton, UK
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Strutz A, Soelter J, Baschwitz A, Farhan A, Grabe V, Rybak J, Knaden M, Schmuker M, Hansson BS, Sachse S. Decoding odor quality and intensity in the Drosophila brain. eLife 2014; 3:e04147. [PMID: 25512254 PMCID: PMC4270039 DOI: 10.7554/elife.04147] [Citation(s) in RCA: 109] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2014] [Accepted: 11/09/2014] [Indexed: 12/12/2022] Open
Abstract
To internally reflect the sensory environment, animals create neural maps encoding the external stimulus space. From that primary neural code relevant information has to be extracted for accurate navigation. We analyzed how different odor features such as hedonic valence and intensity are functionally integrated in the lateral horn (LH) of the vinegar fly, Drosophila melanogaster. We characterized an olfactory-processing pathway, comprised of inhibitory projection neurons (iPNs) that target the LH exclusively, at morphological, functional and behavioral levels. We demonstrate that iPNs are subdivided into two morphological groups encoding positive hedonic valence or intensity information and conveying these features into separate domains in the LH. Silencing iPNs severely diminished flies' attraction behavior. Moreover, functional imaging disclosed a LH region tuned to repulsive odors comprised exclusively of third-order neurons. We provide evidence for a feature-based map in the LH, and elucidate its role as the center for integrating behaviorally relevant olfactory information. DOI:http://dx.doi.org/10.7554/eLife.04147.001 Organisms need to sense and adapt to their environment in order to survive. Senses such as vision and smell allow an organism to absorb information about the external environment and translate it into a meaningful internal image. This internal image helps the organism to remember incidents and act accordingly when they encounter similar situations again. A typical example is when organisms are repeatedly attracted to odors that are essential for survival, such as food and pheromones, and are repulsed by odors that threaten survival. Strutz et al. addressed how attractiveness or repulsiveness of a smell, and also the strength of a smell, are processed by a part of the olfactory system called the lateral horn in fruit flies. This involved mapping the neuronal patterns that were generated in the lateral horn when a fly was exposed to particular odors. Strutz et al. found that a subset of neurons called inhibitory projection neurons processes information about whether the odor is attractive or repulsive, and that a second subset of these neurons process information about the intensity of the odor. Other insects, such as honey bees and hawk moths, have olfactory systems with a similar architecture and might also employ a similar spatial approach to encode information regarding the intensity and identity of odors. Locusts, on the other hand, employ a temporal approach to encoding information about odors. The work of Strutz et al. shows that certain qualities of odors are contained in a spatial map in a specific brain region of the fly. This opens up the question of how the information in this spatial map influences decisions made by the fly. DOI:http://dx.doi.org/10.7554/eLife.04147.002
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Affiliation(s)
- Antonia Strutz
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Jan Soelter
- Department for Biology, Pharmacy and Chemistry, Free University Berlin, Neuroinformatics and Theoretical Neuroscience, Berlin, Germany
| | - Amelie Baschwitz
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Abu Farhan
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Veit Grabe
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Jürgen Rybak
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Markus Knaden
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Michael Schmuker
- Department for Biology, Pharmacy and Chemistry, Free University Berlin, Neuroinformatics and Theoretical Neuroscience, Berlin, Germany
| | - Bill S Hansson
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Jena, Germany
| | - Silke Sachse
- Department of Evolutionary Neuroethology, Max Planck Institute for Chemical Ecology, Jena, Germany
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Diamond A, Schmuker M, Berna AZ, Trowell S, Nowotny T. Classifying chemical sensor data using GPU-accelerated bio-mimetic neuronal networks based on the insect olfactory system. BMC Neurosci 2014. [PMCID: PMC4126557 DOI: 10.1186/1471-2202-15-s1-p77] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Gabler S, Soelter J, Hussain T, Sachse S, Schmuker M. Physicochemical vs. Vibrational Descriptors for Prediction of Odor Receptor Responses. Mol Inform 2013; 32:855-65. [PMID: 27480237 DOI: 10.1002/minf.201300037] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2013] [Accepted: 07/25/2013] [Indexed: 01/20/2023]
Abstract
Responses of olfactory receptors (ORs) can be predicted by applying machine learning methods on a multivariate encoding of an odorant's chemical structure. Physicochemical descriptors that encode features of the molecular graph are a popular choice for such an encoding. Here, we explore the EVA descriptor set, which encodes features derived from the vibrational spectrum of a molecule. We assessed the performance of Support Vector Regression (SVR) and Random Forest Regression (RFR) to predict the gradual response of Drosophila ORs. We compared a 27-dimensional variant of the EVA descriptor against a set of 1467 descriptors provided by the eDragon software package, and against a 32-dimensional subset thereof that has been proposed as the basis for an odor metric consisting of 32 descriptors (HADDAD). The best prediction performance was reproducibly achieved using SVR on the highest-dimensional feature set. The low-dimensional EVA and HADDAD feature sets predicted odor-OR interactions with similar accuracy. Adding charge and polarizability information to the EVA descriptor did not improve the results but rather decreased predictive power. Post-hoc in vivo measurements confirmed these results. Our findings indicate that EVA provides a meaningful low-dimensional representation of odor space, although EVA hardly outperformed "classical" descriptor sets.
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Affiliation(s)
- Stephan Gabler
- Theoretical Neuroscience, Institute of Biology, Dept. of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 1-3, D-14195 Berlin, Germany.,Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, Haus 6, D-10115 Berlin, Germany
| | - Jan Soelter
- Theoretical Neuroscience, Institute of Biology, Dept. of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 1-3, D-14195 Berlin, Germany
| | - Taufia Hussain
- Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, D-07745 Jena, Germany
| | - Silke Sachse
- Max Planck Institute for Chemical Ecology, Hans-Knöll-Straße 8, D-07745 Jena, Germany
| | - Michael Schmuker
- Theoretical Neuroscience, Institute of Biology, Dept. of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Königin-Luise-Str. 1-3, D-14195 Berlin, Germany. .,Bernstein Center for Computational Neuroscience Berlin, Philippstr. 13, Haus 6, D-10115 Berlin, Germany.
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Pfeil T, Grübl A, Jeltsch S, Müller E, Müller P, Petrovici MA, Schmuker M, Brüderle D, Schemmel J, Meier K. Six networks on a universal neuromorphic computing substrate. Front Neurosci 2013; 7:11. [PMID: 23423583 PMCID: PMC3575075 DOI: 10.3389/fnins.2013.00011] [Citation(s) in RCA: 115] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2012] [Accepted: 01/18/2013] [Indexed: 11/28/2022] Open
Abstract
In this study, we present a highly configurable neuromorphic computing substrate and use it for emulating several types of neural networks. At the heart of this system lies a mixed-signal chip, with analog implementations of neurons and synapses and digital transmission of action potentials. Major advantages of this emulation device, which has been explicitly designed as a universal neural network emulator, are its inherent parallelism and high acceleration factor compared to conventional computers. Its configurability allows the realization of almost arbitrary network topologies and the use of widely varied neuronal and synaptic parameters. Fixed-pattern noise inherent to analog circuitry is reduced by calibration routines. An integrated development environment allows neuroscientists to operate the device without any prior knowledge of neuromorphic circuit design. As a showcase for the capabilities of the system, we describe the successful emulation of six different neural networks which cover a broad spectrum of both structure and functionality.
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Affiliation(s)
- Thomas Pfeil
- Kirchhoff-Institute for Physics, Universität Heidelberg Heidelberg, Germany
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Schmuker M, Schneider G. Brain-Like Processing and Classification of Chemical Data. Mach Learn 2012. [DOI: 10.4018/978-1-60960-818-7.ch802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The purpose of the olfactory system is to encode and classify odorants. Hence, its circuits have likely evolved to cope with this task in an efficient, quasi-optimal manner. In this chapter the authors present a three-step approach that emulate neurocomputational principles of the olfactory system to encode, transform and classify chemical data. In the first step, the original chemical stimulus space is encoded by virtual receptors. In the second step, the signals from these receptors are decorrelated by correlation-dependent lateral inhibition. The third step mimics olfactory scent perception by a machine learning classifier. The authors observed that the accuracy of scent prediction is significantly improved by decorrelation in the second stage. Moreover, they found that although the data transformation they propose is suited for dimensionality reduction, it is more robust against overdetermined data than principal component scores. The authors successfully used our method to predict bioactivity of drug-like compounds, demonstrating that it can provide an effective means to connect chemical space with biological activity.
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Schmuker M, Yamagata N, Nawrot MP, Menzel R. Parallel representation of stimulus identity and intensity in a dual pathway model inspired by the olfactory system of the honeybee. Front Neuroeng 2011; 4:17. [PMID: 22232601 PMCID: PMC3246696 DOI: 10.3389/fneng.2011.00017] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2011] [Accepted: 12/01/2011] [Indexed: 11/13/2022]
Abstract
The honeybee Apis mellifera has a remarkable ability to detect and locate food sources during foraging, and to associate odor cues with food rewards. In the honeybee's olfactory system, sensory input is first processed in the antennal lobe (AL) network. Uniglomerular projection neurons (PNs) convey the sensory code from the AL to higher brain regions via two parallel but anatomically distinct pathways, the lateral and the medial antenno-cerebral tract (l- and m-ACT). Neurons innervating either tract show characteristic differences in odor selectivity, concentration dependence, and representation of mixtures. It is still unknown how this differential stimulus representation is achieved within the AL network. In this contribution, we use a computational network model to demonstrate that the experimentally observed features of odor coding in PNs can be reproduced by varying lateral inhibition and gain control in an otherwise unchanged AL network. We show that odor coding in the l-ACT supports detection and accurate identification of weak odor traces at the expense of concentration sensitivity, while odor coding in the m-ACT provides the basis for the computation and following of concentration gradients but provides weaker discrimination power. Both coding strategies are mutually exclusive, which creates a tradeoff between detection accuracy and sensitivity. The development of two parallel systems may thus reflect an evolutionary solution to this problem that enables honeybees to achieve both tasks during bee foraging in their natural environment, and which could inspire the development of artificial chemosensory devices for odor-guided navigation in robots.
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Affiliation(s)
- Michael Schmuker
- Neuroinformatics and Theoretical Neuroscience, Institute of Biology, Freie Universität Berlin Berlin, Germany
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Mamlouk AM, Schmuker M. Self-organization of virtual odorant receptors inspired by insect olfaction. BMC Neurosci 2011. [PMCID: PMC3240396 DOI: 10.1186/1471-2202-12-s1-p286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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Schmuker M, Häusler C, Brüderle D, Nawrot MP. Benchmarking the impact of information processing in the insect olfactory system with a spiking neuromorphic classifier. BMC Neurosci 2011. [PMCID: PMC3240338 DOI: 10.1186/1471-2202-12-s1-p233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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Abstract
How are odor mixtures perceived? We take a behavioral approach toward this question, using associative odor-recognition experiments in Drosophila. We test how strongly flies avoid a binary mixture after punishment training with one of its constituent elements and how much, in turn, flies avoid an odor element if it had been a component of a previously punished binary mixture. A distinguishing feature of our approach is that we first adjust odors for task-relevant behavioral potency, that is, for equal learnability. Doing so, we find that 1) generalization between mixture and elements is symmetrical and partial, 2) elements are equally similar to all mixtures containing it and that 3) mixtures are equally similar to both their constituent elements. As boundary conditions for the applicability of these rules, we note that first, although variations in learnability are small and remain below statistical cut-off, these variations nevertheless correlate with the elements' perceptual "weight" in the mixture; thus, even small differences in learnability between the elements have the potential to feign mixture asymmetries. Second, the more distant the elements of a mixture are to each other in terms of their physicochemical properties, the more distant the flies regard the elements from the mixture. Thus, titrating for task-relevant behavioral potency and taking into account physicochemical relatedness of odors reveals rules of mixture perception that, maybe surprisingly, appear to be fairly simple.
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Affiliation(s)
- Claire Eschbach
- Universität Leipzig, Institut für Biologie, Genetik, Talstrasse 33, Leipzig, Germany.
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Abstract
To provide a behavior-based estimate of odor similarity in larval Drosophila, we use 4 recognition-type experiments: 1) We train larvae to associate an odor with food and then test whether they would regard another odor as the same as the trained one. 2) We train larvae to associate an odor with food and test whether they prefer the trained odor against a novel nontrained one. 3) We train larvae differentially to associate one odor with food, but not the other one, and test whether they prefer the rewarded against the nonrewarded odor. 4) In an experiment like (3), we test the larvae after a 30-min break. This yields a combined task-independent estimate of perceived difference between odor pairs. Comparing these perceived differences to published measures of physicochemical difference reveals a weak correlation. A notable exception are 3-octanol and benzaldehyde, which are distinct in published accounts of chemical similarity and in terms of their published sensory representation but nevertheless are consistently regarded as the most similar of the 10 odor pairs employed. It thus appears as if at least some aspects of olfactory perception are “computed” in postreceptor circuits on the basis of sensory signals rather than being immediately given by them.
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Affiliation(s)
- Yi-chun Chen
- Department of Neurobiology and Genetics, Universität Würzburg, Biozentrum Am Hubland, 97074 Würzburg, Germany
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Yamagata N, Schmuker M, Szyszka P, Mizunami M, Menzel R. Differential odor processing in two olfactory pathways in the honeybee. Front Syst Neurosci 2009; 3:16. [PMID: 20198105 PMCID: PMC2802323 DOI: 10.3389/neuro.06.016.2009] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2009] [Accepted: 11/17/2009] [Indexed: 11/13/2022] Open
Abstract
An important component in understanding central olfactory processing and coding in the insect brain relates to the characterization of the functional divisions between morphologically distinct types of projection neurons (PN). Using calcium imaging, we investigated how the identity, concentration and mixtures of odors are represented in axon terminals (boutons) of two types of PNs – lPN and mPN. In lPN boutons we found less concentration dependence, narrow tuning profiles at a high concentration, which may be optimized for fine, concentration-invariant odor discrimination. In mPN boutons, however, we found clear rising concentration dependence, broader tuning profiles at a high concentration, which may be optimized for concentration coding. In addition, we found more mixture suppression in lPNs than in mPNs, indicating lPNs better adaptation for synthetic mixture processing. These results suggest a functional division of odor processing in both PN types.
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Affiliation(s)
- Nobuhiro Yamagata
- Institut für Neurobiologie, Freie Universität Berlin Berlin, Germany
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Muller E, Davison A, Brizzi T, Bruederle D, Eppler M, Kremkow J, Pecevski D, Perrinet L, Schmuker M, Yger P. NeuralEnsemble.Org: Unifying neural simulators in Python to
ease the model complexity bottleneck. Front Neuroinform 2009. [DOI: 10.3389/conf.neuro.11.2009.08.104] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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Renner S, Hechenberger M, Noeske T, Böcker A, Jatzke C, Schmuker M, Parsons C, Weil T, Schneider G. Suche nach Wirkstoff-Grundgerüsten mit 3D-Pharmakophorhypothesen und Ensembles neuronaler Netze. Angew Chem Int Ed Engl 2007. [DOI: 10.1002/ange.200604125] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Renner S, Hechenberger M, Noeske T, Böcker A, Jatzke C, Schmuker M, Parsons CG, Weil T, Schneider G. Searching for Drug Scaffolds with 3D Pharmacophores and Neural Network Ensembles. Angew Chem Int Ed Engl 2007; 46:5336-9. [PMID: 17604383 DOI: 10.1002/anie.200604125] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Steffen Renner
- Chemical R&D, Medicinal Chemistry/Cheminformatics, Merz Pharmaceuticals GmbH, Eckenheimer Landstrasse 100, 60318 Frankfurt am Main, Germany
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Schmuker M, de Bruyne M, Hähnel M, Schneider G. Predicting olfactory receptor neuron responses from odorant structure. Chem Cent J 2007; 1:11. [PMID: 17880742 PMCID: PMC1994056 DOI: 10.1186/1752-153x-1-11] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2007] [Accepted: 05/04/2007] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Olfactory receptors work at the interface between the chemical world of volatile molecules and the perception of scent in the brain. Their main purpose is to translate chemical space into information that can be processed by neural circuits. Assuming that these receptors have evolved to cope with this task, the analysis of their coding strategy promises to yield valuable insight in how to encode chemical information in an efficient way. RESULTS We mimicked olfactory coding by modeling responses of primary olfactory neurons to small molecules using a large set of physicochemical molecular descriptors and artificial neural networks. We then tested these models by recording in vivo receptor neuron responses to a new set of odorants and successfully predicted the responses of five out of seven receptor neurons. Correlation coefficients ranged from 0.66 to 0.85, demonstrating the applicability of our approach for the analysis of olfactory receptor activation data. The molecular descriptors that are best-suited for response prediction vary for different receptor neurons, implying that each receptor neuron detects a different aspect of chemical space. Finally, we demonstrate that receptor responses themselves can be used as descriptors in a predictive model of neuron activation. CONCLUSION The chemical meaning of molecular descriptors helps understand structure-response relationships for olfactory receptors and their "receptive fields". Moreover, it is possible to predict receptor neuron activation from chemical structure using machine-learning techniques, although this is still complicated by a lack of training data.
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Affiliation(s)
- Michael Schmuker
- Johann Wolfgang Goethe Universität, Beilstein Endowed Chair for Cheminformatics, Institute of Organic Chemistry and Chemical Biology, Siesmayerstr. 70, 60323 Frankfurt am Main, Germany
- (present address) Freie Universität Berlin, Institute of Biology-Neurobiology, Königin-Luise-Str. 28–30, 14195 Berlin, Germany
| | - Marien de Bruyne
- Freie Universität Berlin, Institute of Biology-Neurobiology, Königin-Luise-Str. 28–30, 14195 Berlin, Germany
- Monash University, School of Biological Sciences, Wellington Road, Clayton VIC 3800, Australia
| | - Melanie Hähnel
- Freie Universität Berlin, Institute of Biology-Neurobiology, Königin-Luise-Str. 28–30, 14195 Berlin, Germany
| | - Gisbert Schneider
- Johann Wolfgang Goethe Universität, Beilstein Endowed Chair for Cheminformatics, Institute of Organic Chemistry and Chemical Biology, Siesmayerstr. 70, 60323 Frankfurt am Main, Germany
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Schmuker M, Schwarte F, Brück A, Proschak E, Tanrikulu Y, Givehchi A, Scheiffele K, Schneider G. SOMMER: self-organising maps for education and research. J Mol Model 2006; 13:225-8. [PMID: 17024412 DOI: 10.1007/s00894-006-0140-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2006] [Accepted: 08/07/2006] [Indexed: 10/24/2022]
Abstract
SOMMER is a publicly available, Java-based toolbox for training and visualizing two- and three-dimensional unsupervised self-organizing maps (SOMs). Various map topologies are implemented for planar rectangular, toroidal, cubic-surface and spherical projections. The software allows for visualization of the training process, which has been shown to be particularly valuable for teaching purposes.
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Affiliation(s)
- Michael Schmuker
- Institute of Organic Chemistry and Chemical Biology, Johann Wolfgang Goethe-University, Siesmayerstr. 70, 60323, Frankfurt, Germany
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Meissner M, Schmuker M, Schneider G. Optimized Particle Swarm Optimization (OPSO) and its application to artificial neural network training. BMC Bioinformatics 2006; 7:125. [PMID: 16529661 PMCID: PMC1464136 DOI: 10.1186/1471-2105-7-125] [Citation(s) in RCA: 181] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2005] [Accepted: 03/10/2006] [Indexed: 05/07/2023] Open
Abstract
Background Particle Swarm Optimization (PSO) is an established method for parameter optimization. It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task. We present a method for parameter meta-optimization based on PSO and its application to neural network training. The concept of the Optimized Particle Swarm Optimization (OPSO) is to optimize the free parameters of the PSO by having swarms within a swarm. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations. Results Our results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments. We applied the OPSO method to neural network training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules. On average, training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with training-, test- and validation data was obtained. Conclusion Optimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task.
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Affiliation(s)
- Michael Meissner
- Johann Wolfgang Goethe-Universität, Institut für Organische Chemie und Chemische Biologie, Siesmayerstraße 70, D-60323 Frankfurt, Germany
| | - Michael Schmuker
- Johann Wolfgang Goethe-Universität, Institut für Organische Chemie und Chemische Biologie, Siesmayerstraße 70, D-60323 Frankfurt, Germany
| | - Gisbert Schneider
- Johann Wolfgang Goethe-Universität, Institut für Organische Chemie und Chemische Biologie, Siesmayerstraße 70, D-60323 Frankfurt, Germany
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Abstract
Besides the choice of an automated software method for selecting 'maximally diverse' compounds from a large pool of molecules, it is the implementation of the algorithm that critically determines the usefulness of the approach. The speed of execution of two implementations of the Maxmin algorithm is compared for the selection of maximally diverse subsets of large compound collections. Different versions of the software are compared using various C compiler options and Java virtual machines. The analysis shows that the Maxmin algorithm can be implemented in both languages yielding sufficient speed of execution. For large compound libraries the Java version outperformes the C version. While the Java version selects the same compounds independent of the virtual machine used, the C version produces slightly different subsets depending on the compiler and on the optimization settings.
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Affiliation(s)
- Michael Schmuker
- Johann Wolfgang Goethe-Universität, Institut für Organische Chemie und Chemische Biologie, Marie-Curie-Str 11, D-60439 Frankfurt, Germany
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Zuegge J, Ralph S, Schmuker M, McFadden GI, Schneider G. Deciphering apicoplast targeting signals--feature extraction from nuclear-encoded precursors of Plasmodium falciparum apicoplast proteins. Gene 2001; 280:19-26. [PMID: 11738814 DOI: 10.1016/s0378-1119(01)00776-4] [Citation(s) in RCA: 154] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The malaria causing protozoan Plasmodium falciparum contains a vestigal, non-photosynthetic plastid, the apicoplast. Numerous proteins encoded by nuclear genes are targeted to the apicoplast courtesy of N-terminal extensions. With the impending sequence completion of an entire genome of the malaria parasite, it is important to have software tools in place for prediction of subcellular locations for all proteins. Apicoplast targeting signals are bipartite; containing a signal peptide and a transit peptide. Nuclear-encoded apicoplast protein precursors were analyzed for characteristic features by statistical methods, principal component analysis, self-organizing maps, and supervised neural networks. The transit peptide contains a net positive charge and is rich in asparagine, lysine, and isoleucine residues. A novel prediction system (PATS, predict apicoplast-targeted sequences) was developed based on various sequence features, yielding a Matthews correlation coefficient of 0.91 (97% correct predictions) in a 40-fold cross-validation study. This system predicted 22% apicoplast proteins of the 205 potential proteins on P. falciparum chromosome 2, and 21% of 243 chromosome 3 proteins. A combination of the PATS results with a signal peptide prediction yields 15% potentially nuclear-encoded apicoplast proteins on chromosomes 2 and 3. The prediction tool will advance P. falciparum genome analysis, and it might help to identify apicoplast proteins as drug targets for the development of novel anti-malaria agents.
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Affiliation(s)
- J Zuegge
- F. Hoffmann-La Roche Ltd., Pharmaceuticals Division, CH-4070 Basel, Switzerland
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