N
Nicholas T. Carnevale
Researcher at Yale University
Publications - 47
Citations - 6837
Nicholas T. Carnevale is an academic researcher from Yale University. The author has contributed to research in topics: Dendritic spine & Biology. The author has an hindex of 25, co-authored 41 publications receiving 6230 citations. Previous affiliations of Nicholas T. Carnevale include Santa Clara Valley Medical Center & Duke University.
Papers
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Journal ArticleDOI
Functional Structure of the Mitral Cell Dendritic Tuft in the Rat Olfactory Bulb
TL;DR: The main finding was that the dendritic tuft functions as a single electrical compartment for subthreshold signals within the range of amplitudes detectable by voltage-sensitive dye recording.
Journal ArticleDOI
Neuron Names: A Gene- and Property-Based Name Format, With Special Reference to Cortical Neurons
Gordon M. Shepherd,Luis N. Marenco,Michael L. Hines,Michele Migliore,Michele Migliore,Robert A. McDougal,Nicholas T. Carnevale,Adam J. H. Newton,Adam J. H. Newton,Monique Surles-Zeigler,Giorgio A. Ascoli +10 more
TL;DR: The format of parent-child relations for the region and subregion for naming a neuron is extended so that additional properties can become an explicit part of a neuron’s identity and name, or archived in a linked properties database.
Proceedings Article
The Electrotonic Transformation: a Tool for Relating Neuronal Form to Function
TL;DR: This work has developed a quantitative yet intuitive approach to the analysis of electrotonus, using the logarithm of voltage attenuation as the distance metric to transform the architecture of the cell from anatomical to electrotonic space.
Journal ArticleDOI
Early experiences in developing and managing the neuroscience gateway
Subhashini Sivagnanam,Amit Majumdar,Kenneth Yoshimoto,Vadim Astakhov,Anita Bandrowski,Maryann E. Martone,Nicholas T. Carnevale +6 more
TL;DR: The early experiences in bringing up the Neuroscience Gateway are shared and the software architecture it is based on, how it is implemented, and how users can use this for computational neuroscience research using high performance computing at the back end are described.