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  • 11
    Online Resource
    Online Resource
    Cortical Up State Activity Is Enhanced After Seizures: A Quantitative Analysis
    Ovid Technologies (Wolters Kluwer Health) ; 2010
    In:  Journal of Clinical Neurophysiology Vol. 27, No. 6 ( 2010-12), p. 425-432
    Details
    In: Journal of Clinical Neurophysiology, Ovid Technologies (Wolters Kluwer Health), Vol. 27, No. 6 ( 2010-12), p. 425-432
    Type of Medium: Online Resource
    ISSN: 0736-0258
    URL: Article
    DOI: 10.1097/WNP.0b013e3181fdf8bd
    RVK:
    XA 10000
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2010
    detail.hit.zdb_id: 2065729-8
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  • 12
    Online Resource
    Online Resource
    Text-based predictions of COVID-19 diagnosis from self-reported chemosensory descriptions
    Springer Science and Business Media LLC ; 2023
    In:  Communications Medicine Vol. 3, No. 1 ( 2023-07-27)
    Details
    In: Communications Medicine, Springer Science and Business Media LLC, Vol. 3, No. 1 ( 2023-07-27)
    Abstract: There is a prevailing view that humans’ capacity to use language to characterize sensations like odors or tastes is poor, providing an unreliable source of information. Methods Here, we developed a machine learning method based on Natural Language Processing (NLP) using Large Language Models (LLM) to predict COVID-19 diagnosis solely based on text descriptions of acute changes in chemosensation, i.e., smell, taste and chemesthesis, caused by the disease. The dataset of more than 1500 subjects was obtained from survey responses early in the COVID-19 pandemic, in Spring 2020. Results When predicting COVID-19 diagnosis, our NLP model performs comparably (AUC ROC ~ 0.65) to models based on self-reported changes in function collected via quantitative rating scales. Further, our NLP model could attribute importance of words when performing the prediction; sentiment and descriptive words such as “smell”, “taste”, “sense”, had strong contributions to the predictions. In addition, adjectives describing specific tastes or smells such as “salty”, “sweet”, “spicy”, and “sour” also contributed considerably to predictions. Conclusions Our results show that the description of perceptual symptoms caused by a viral infection can be used to fine-tune an LLM model to correctly predict and interpret the diagnostic status of a subject. In the future, similar models may have utility for patient verbatims from online health portals or electronic health records.
    Type of Medium: Online Resource
    ISSN: 2730-664X
    URL: Article
    DOI: 10.1038/s43856-023-00334-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 3096949-9
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  • 13
    Online Resource
    Online Resource
    The Adaptive Olfactory Measure of Threshold (ArOMa-T): a rapid test of olfactory function
    Oxford University Press (OUP) ; 2022
    In:  Chemical Senses Vol. 47 ( 2022-01-01)
    Details
    In: Chemical Senses, Oxford University Press (OUP), Vol. 47 ( 2022-01-01)
    Abstract: Many widely used psychophysical olfactory tests have limitations that can create barriers to adoption. For example, tests that measure the ability to identify odors may confound sensory performance with memory recall, verbal ability, and prior experience with the odor. Conversely, classic threshold-based tests avoid these issues, but are labor intensive. Additionally, many commercially available tests are slow and may require a trained administrator, making them impractical for use in situations where time is at a premium or self-administration is required. We tested the performance of the Adaptive Olfactory Measure of Threshold (ArOMa-T)—a novel odor detection threshold test that employs an adaptive Bayesian algorithm paired with a disposable odorant delivery card—in a non-clinical sample of individuals (n = 534) at the 2021 Twins Day Festival in Twinsburg, OH. Participants successfully completed the test in under 3 min with a false alarm rate of 7.5% and a test–retest reliability of 0.61. Odor detection thresholds differed by sex (~3.2-fold lower for females) and age (~8.7-fold lower for the youngest versus the oldest age group), consistent with prior studies. In an exploratory analysis, we failed to observe evidence of detection threshold differences between participants who reported a history of COVID-19 and matched controls who did not. We also found evidence for broad-sense heritability of odor detection thresholds. Together, this study suggests the ArOMa-T can determine odor detection thresholds. Additional validation studies are needed to confirm the value of ArOMa-T in clinical or field settings where rapid and portable assessment of olfactory function is needed.
    Type of Medium: Online Resource
    ISSN: 0379-864X , 1464-3553
    URL: Article
    DOI: 10.1093/chemse/bjac036
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1494617-8
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  • 14
    Online Resource
    Online Resource
    Parsing Sage and Rosemary in Time: The Machine Learning Race to Crack Olfactory Perception
    Oxford University Press (OUP) ; 2021
    In:  Chemical Senses Vol. 46 ( 2021-01-01)
    Details
    In: Chemical Senses, Oxford University Press (OUP), Vol. 46 ( 2021-01-01)
    Abstract: Color and pitch perception are largely understandable from characteristics of physical stimuli: the wavelengths of light and sound waves, respectively. By contrast, understanding olfactory percepts from odorous stimuli (volatile molecules) is much more challenging. No intuitive set of molecular features is up to the task. Here in Chemical Senses, the Ray lab reports using a predictive modeling framework—first breaking molecular structure into thousands of features and then using this to train a predictive statistical model on a wide range of perceptual descriptors—to create a tool for predicting the odor character of hundreds of thousands of available but previously uncharacterized molecules (Kowalewski et al. 2021). This will allow future investigators to representatively sample the space of odorous molecules as well as identify previously unknown odorants with a target odor character. Here, I put this work into the context of other modeling efforts and highlight the urgent need for large new datasets and transparent benchmarks for the field to make and evaluate modeling breakthroughs, respectively.
    Type of Medium: Online Resource
    ISSN: 0379-864X , 1464-3553
    URL: Article
    DOI: 10.1093/chemse/bjab020
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1494617-8
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  • 15
    Online Resource
    Online Resource
    NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML
    Public Library of Science (PLoS) ; 2023
    In:  PLOS Computational Biology Vol. 19, No. 3 ( 2023-3-3), p. e1010941-
    Details
    In: PLOS Computational Biology, Public Library of Science (PLoS), Vol. 19, No. 3 ( 2023-3-3), p. e1010941-
    Abstract: As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other researchers. We introduce the NeuroML Database ( NeuroML-DB.org ), which has been developed to address this need and to complement other model sharing resources. NeuroML-DB stores over 1,500 previously published models of ion channels, cells, and networks that have been translated to the modular NeuroML model description language. The database also provides reciprocal links to other neuroscience model databases (ModelDB, Open Source Brain) as well as access to the original model publications (PubMed). These links along with Neuroscience Information Framework (NIF) search functionality provide deep integration with other neuroscience community modeling resources and greatly facilitate the task of finding suitable models for reuse. Serving as an intermediate language, NeuroML and its tooling ecosystem enable efficient translation of models to other popular simulator formats. The modular nature also enables efficient analysis of a large number of models and inspection of their properties. Search capabilities of the database, together with web-based, programmable online interfaces, allow the community of researchers to rapidly assess stored model electrophysiology, morphology, and computational complexity properties. We use these capabilities to perform a database-scale analysis of neuron and ion channel models and describe a novel tetrahedral structure formed by cell model clusters in the space of model properties and features. This analysis provides further information about model similarity to enrich database search.
    Type of Medium: Online Resource
    ISSN: 1553-7358
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.1371/journal.pcbi.1010941
    DOI: 10.1371/journal.pcbi.1010941.g001
    DOI: 10.1371/journal.pcbi.1010941.g002
    DOI: 10.1371/journal.pcbi.1010941.g003
    DOI: 10.1371/journal.pcbi.1010941.g004
    DOI: 10.1371/journal.pcbi.1010941.g005
    DOI: 10.1371/journal.pcbi.1010941.g006
    DOI: 10.1371/journal.pcbi.1010941.g007
    DOI: 10.1371/journal.pcbi.1010941.g008
    DOI: 10.1371/journal.pcbi.1010941.t001
    DOI: 10.1371/journal.pcbi.1010941.t002
    Language: English
    Publisher: Public Library of Science (PLoS)
    Publication Date: 2023
    detail.hit.zdb_id: 2193340-6
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  • 16
    Online Resource
    Online Resource
    Origins of correlated spiking in the mammalian olfactory bulb
    Proceedings of the National Academy of Sciences ; 2013
    In:  Proceedings of the National Academy of Sciences Vol. 110, No. 42 ( 2013-10-15), p. 17083-17088
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 110, No. 42 ( 2013-10-15), p. 17083-17088
    Abstract: Mitral/tufted (M/T) cells of the main olfactory bulb transmit odorant information to higher brain structures. The relative timing of action potentials across M/T cells has been proposed to encode this information and to be critical for the activation of downstream neurons. Using ensemble recordings from the mouse olfactory bulb in vivo, we measured how correlations between cells are shaped by stimulus (odor) identity, common respiratory drive, and other cells’ activity. The shared respiration cycle is the largest source of correlated firing, but even after accounting for all observable factors a residual positive noise correlation was observed. Noise correlation was maximal on a ∼100-ms timescale and was seen only in cells separated by 〈 200 µm. This correlation is explained primarily by common activity in groups of nearby cells. Thus, M/T-cell correlation principally reflects respiratory modulation and sparse, local network connectivity, with odor identity accounting for a minor component.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1303830110
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2013
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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  • 17
    Online Resource
    Online Resource
    Intermediate intrinsic diversity enhances neural population coding
    Proceedings of the National Academy of Sciences ; 2013
    In:  Proceedings of the National Academy of Sciences Vol. 110, No. 20 ( 2013-05-14), p. 8248-8253
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 110, No. 20 ( 2013-05-14), p. 8248-8253
    Abstract: Cell-to-cell variability in molecular, genetic, and physiological features is increasingly recognized as a critical feature of complex biological systems, including the brain. Although such variability has potential advantages in robustness and reliability, how and why biological circuits assemble heterogeneous cells into functional groups is poorly understood. Here, we develop analytic approaches toward answering how neuron-level variation in intrinsic biophysical properties of olfactory bulb mitral cells influences population coding of fluctuating stimuli. We capture the intrinsic diversity of recorded populations of neurons through a statistical approach based on generalized linear models. These models are flexible enough to predict the diverse responses of individual neurons yet provide a common reference frame for comparing one neuron to the next. We then use Bayesian stimulus decoding to ask how effectively different populations of mitral cells, varying in their diversity, encode a common stimulus. We show that a key advantage provided by physiological levels of intrinsic diversity is more efficient and more robust encoding of stimuli by the population as a whole. However, we find that the populations that best encode stimulus features are not simply the most heterogeneous, but those that balance diversity with the benefits of neural similarity.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1221214110
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2013
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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  • 18
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    Online Resource
    On the Role of Theory and Modeling in Neuroscience
    Society for Neuroscience ; 2023
    In:  The Journal of Neuroscience Vol. 43, No. 7 ( 2023-02-15), p. 1074-1088
    Details
    In: The Journal of Neuroscience, Society for Neuroscience, Vol. 43, No. 7 ( 2023-02-15), p. 1074-1088
    Abstract: In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction, will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment.
    Type of Medium: Online Resource
    ISSN: 0270-6474 , 1529-2401
    URL: Article
    DOI: 10.1523/JNEUROSCI.1179-22.2022
    Language: English
    Publisher: Society for Neuroscience
    Publication Date: 2023
    detail.hit.zdb_id: 1475274-8
    SSG: 12
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  • 19
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    Online Resource
    NeuroElectro: a window to the world's neuron electrophysiology data
    Frontiers Media SA ; 2014
    In:  Frontiers in Neuroinformatics Vol. 8 ( 2014-04-29)
    Details
    In: Frontiers in Neuroinformatics, Frontiers Media SA, Vol. 8 ( 2014-04-29)
    Type of Medium: Online Resource
    ISSN: 1662-5196
    URL: Article
    DOI: 10.3389/fninf.2014.00040
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2014
    detail.hit.zdb_id: 2452979-5
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  • 20
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    Online Resource
    Homeostatic regulation of spontaneous and evoked synaptic transmission in two steps
    Springer Science and Business Media LLC ; 2013
    In:  Molecular Brain Vol. 6, No. 1 ( 2013), p. 38-
    Details
    In: Molecular Brain, Springer Science and Business Media LLC, Vol. 6, No. 1 ( 2013), p. 38-
    Type of Medium: Online Resource
    ISSN: 1756-6606
    URL: Article
    DOI: 10.1186/1756-6606-6-38
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2013
    detail.hit.zdb_id: 2436057-0
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